<?xml version="1.0" encoding="utf-8"?><?xml-stylesheet type="text/xsl" href="publist.xsl"?><publist>    <publication pub_id="1">        <status>On</status>        <application>DNA Methylation</application>        <title>Comprehensive DNA methylation profiling in a human cancer genome identifies novel epigenetic targets</title>        <journal>Carcinogenesis</journal>        <issue>2006 Dec;27(12):2409-23. Epub 2006 Sep 4</issue>        <pubdate>2006-12-01</pubdate>        <epubdate>2006-09-04</epubdate>        <url>http://dx.doi.org/10.1093/carcin/bgl161</url>        <url_pdf>http://carcin.oxfordjournals.org/cgi/reprint/27/12/2409</url_pdf>        <url_supplemental>http://carcin.oxfordjournals.org/cgi/content/full/bgl161/DC1</url_supplemental>        <abstract>Using a unique microarray platform for cytosine methylation profiling, the DNA methylation landscape of the human genome was monitored at more than 21,000 sites, including 79% of the annotated transcriptional start sites (TSS). Analysis of an oligodendroglioma derived cell line LN-18 revealed more than 4,000 methylated TSS. The gene-centric analysis indicated a complex pattern of DNA methylation exists along each autosome, with a trend of increasing density approaching the telomeres. Remarkably, 2% of CpG islands (CGI) were densely methylated, and 17% had significant levels of 5mC, whether or not they corresponded to a TSS. Substantial independent verification, obtained from 95 loci, suggested that this approach is capable of large scale detection of cytosine methylation with an accuracy approaching 90%. In addition, we detected large genomic domains that are also susceptible to DNA methylation reinforced inactivation, such as the HOX cluster on chromosome 7 (CH7). Extrapolation from the data suggests that more than 2000 genomic loci may be susceptible to methylation and associated inactivation, and most have yet to be identified. Finally, we report six new targets of epigenetic inactivation (IRX3, WNT10A, WNT6, RAR{alpha}, BMP7, and ZGPAT). These targets displayed cell line and tumor specific differential methylation when compared with normal brain samples, suggesting they may have utility as biomarkers. Uniquely, hypermethylation of the CGI within an IRX3 exon was correlated with over-expression of IRX3 in tumor tissues and cell lines relative to normal brain samples.</abstract>        <author>            <id>1</id>            <author_shortname>Ordway JM</author_shortname>            <author_fullname>J. M. Ordway</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Bedell JA</author_shortname>            <author_fullname>J. A. Bedell</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Citek RW</author_shortname>            <author_fullname>R. W. Citek</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Nunberg A</author_shortname>            <author_fullname>A. Nunberg</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Garrido A</author_shortname>            <author_fullname>A. Garrido</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Kendall R</author_shortname>            <author_fullname>R. Kendall</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Stevens JR</author_shortname>            <author_fullname>J. R. Stevens</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Cao D</author_shortname>            <author_fullname>D. Cao</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Doerge RW</author_shortname>            <author_fullname>R. W. Doerge</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Korshunova Y</author_shortname>            <author_fullname>Y. Korshunova</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Holemon H</author_shortname>            <author_fullname>H. Holemon</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>12</id>            <author_shortname>McPherson JD</author_shortname>            <author_fullname>J. D. McPherson</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>13</id>            <author_shortname>Lakey N</author_shortname>            <author_fullname>N. Lakey</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>14</id>            <author_shortname>Leon J</author_shortname>            <author_fullname>J. Leon</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>15</id>            <author_shortname>Martienssen RA</author_shortname>            <author_fullname>R. A. Martienssen</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>16</id>            <author_shortname>Jeddeloh JA</author_shortname>            <author_fullname>J. A. Jeddeloh</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Orion Genomics, St. Louis, MO, USA</institution>        <institution>Department of Agronomy, Purdue University, W. Lafayette, IN, USA &amp; Department of Statistics, Purdue University, W. Lafayette, IN, USA</institution>        <institution>Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX</institution>        <institution>Cold Spring Harbor Laboratory, Cold Spring Harbor, NY</institution>    </publication>    <publication pub_id="2">        <status>On</status>        <application>DNA Methylation</application>        <title>Comparative isoschizomer profiling of cytosine methylation: The HELP assay</title>        <journal>Genome Res.</journal>        <issue>2006 Aug;16(8):1046-55. Epub 2006 Jun 29. </issue>        <pubdate>2006-08-01</pubdate>        <epubdate>2006-06-29</epubdate>        <url>http://dx.doi.org/10.1101/gr.5273806</url>        <url_pdf>http://www.genome.org/cgi/reprint/16/8/1046</url_pdf>        <url_supplemental>http://www.genome.org/cgi/content/full/gr.5273806/DC1</url_supplemental>        <abstract>The distribution of cytosine methylation in 6.2 Mb of the mouse genome was tested using cohybridization of genomic representations from a methylation-sensitive restriction enzyme and its methylation-insensitive isoschizomer. This assay, termed HELP (HpaII tiny fragment Enrichment by Ligation-mediated PCR), allows both intragenomic profiling and intergenomic comparisons of cytosine methylation. The intragenomic profile shows most of the genome to be contiguous methylated sequence with occasional clusters of hypomethylated loci, usually but not exclusively at promoters and CpG islands. Intergenomic comparison found marked differences in cytosine methylation between spermatogenic and brain cells, identifying 223 new candidate tissue-specific differentially methylated regions (T-DMRs). Bisulfite pyrosequencing confirmed the four candidates tested to be T-DMRs, while quantitative RT-PCR for two genes with T-DMRs located at their promoters showed the HELP data to be correlated with gene activity at these loci. The HELP assay is robust, quantitative, and accurate and is providing new insights into the distribution and dynamic nature of cytosine methylation in the genome.</abstract>        <author>            <id>1</id>            <author_shortname>Khulan B</author_shortname>            <author_fullname>Batbayar Khulan</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Thompson RF</author_shortname>            <author_fullname>Reid F. Thompson</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Ye K</author_shortname>            <author_fullname>Kenny Ye</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Fazzari MJ</author_shortname>            <author_fullname>Melissa J. Fazzari</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Suzuki M</author_shortname>            <author_fullname>Masako Suzuki</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Stasiek E</author_shortname>            <author_fullname>Edyta Stasiek</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Figueroa ME</author_shortname>            <author_fullname>Maria E. Figueroa</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Glass JL</author_shortname>            <author_fullname>Jacob L. Glass</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Chen Q</author_shortname>            <author_fullname>Quan Chen</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Montagna C</author_shortname>            <author_fullname>Cristina Montagna</author_fullname>            <author_affiliation>1,5</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Hatchwell E</author_shortname>            <author_fullname>Eli Hatchwell</author_fullname>            <author_affiliation>6</author_affiliation>        </author>        <author>            <id>12</id>            <author_shortname>Selzer RR</author_shortname>            <author_fullname>Rebecca R. Selzer</author_fullname>            <author_affiliation>7</author_affiliation>        </author>        <author>            <id>13</id>            <author_shortname>Richmond TA</author_shortname>            <author_fullname>Todd A. Richmond</author_fullname>            <author_affiliation>7</author_affiliation>        </author>        <author>            <id>14</id>            <author_shortname>Green RD</author_shortname>            <author_fullname>Roland D. Green</author_fullname>            <author_affiliation>7</author_affiliation>        </author>        <author>            <id>15</id>            <author_shortname>Melnick A</author_shortname>            <author_fullname>Ari Melnick</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>16</id>            <author_shortname>Greally JM</author_shortname>            <author_fullname>John M. Greally</author_fullname>            <author_affiliation>1,3</author_affiliation>        </author>        <institution>Molecular Genetics, Albert Einstein College of Medicine, Bronx, New York</institution>        <institution>Epidemiology and Population Health Albert Einstein College of Medicine, Bronx, New York</institution>        <institution>Medicine (Hematology) Albert Einstein College of Medicine, Bronx, New York</institution>        <institution>Developmental and Molecular Biology Albert Einstein College of Medicine, Bronx, New York</institution>        <institution>Pathology, Albert Einstein College of Medicine, Bronx, New York</institution>        <institution>Cold Spring Harbor Laboratories, Cold Spring Harbor, New York</institution>        <institution>NimbleGen Systems Inc., Madison, Wisconsin</institution>    </publication>    <publication pub_id="3">        <status>On</status>        <application>DNase Hypersensitivity</application>        <title>Genome-scale mapping of DNase I sensitivity in vivo using tiling DNA microarrays</title>        <journal>Nature Methods</journal>        <issue>2006 Jul;3(7):511-8.</issue>        <pubdate>2006-07-01</pubdate>        <epubdate>2006-06-21</epubdate>        <url>http://dx.doi.org/10.1038/nmeth890</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>Localized accessibility of critical DNA sequences to the regulatory machinery is a key requirement for regulation of human genes. Here we describe a high-resolution, genome-scale approach for quantifying chromatin accessibility by measuring DNase I sensitivity as a continuous function of genome position using tiling DNA microarrays (DNase-array). We demonstrate this approach across 1% (approx30 Mb) of the human genome, wherein we localized 2,690 classical DNase I hypersensitive sites with high sensitivity and specificity, and also mapped larger-scale patterns of chromatin architecture. DNase I hypersensitive sites exhibit marked aggregation around transcriptional start sites (TSSs), though the majority mark nonpromoter functional elements. We also developed a computational approach for visualizing higher-order features of chromatin structure. This revealed that human chromatin organization is dominated by large (100&#226;&#8364;&#8220;500 kb) 'superclusters' of DNase I hypersensitive sites, which encompass both gene-rich and gene-poor regions. DNase-array is a powerful and straightforward approach for systematic exposition of the cis-regulatory architecture of complex genomes.</abstract>        <author>            <id>1</id>            <author_shortname>Sabo PJ</author_shortname>            <author_fullname>Peter J Sabo</author_fullname>            <author_affiliation>1,6</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Kuehn MS</author_shortname>            <author_fullname>Michael S Kuehn</author_fullname>            <author_affiliation>1,2,6</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Thurman R</author_shortname>            <author_fullname>Robert Thurman</author_fullname>            <author_affiliation>1,2</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Johnson BE</author_shortname>            <author_fullname>Brett E Johnson</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Johnson EM</author_shortname>            <author_fullname>Ericka M Johnson</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Cao H</author_shortname>            <author_fullname>Hua Cao</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Yu M</author_shortname>            <author_fullname>Man Yu</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Rosenzweig E</author_shortname>            <author_fullname>Elizabeth Rosenzweig</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Goldy J</author_shortname>            <author_fullname>Jeff Goldy</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Haydock A</author_shortname>            <author_fullname>Andrew Haydock</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Weaver M</author_shortname>            <author_fullname>Molly Weaver</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>12</id>            <author_shortname>Shafer A</author_shortname>            <author_fullname>Anthony Shafer</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>13</id>            <author_shortname>Lee K</author_shortname>            <author_fullname>Kristin Lee</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>14</id>            <author_shortname>Neri F</author_shortname>            <author_fullname>Fidencio Neri</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>15</id>            <author_shortname>Humbert R</author_shortname>            <author_fullname>Richard Humbert</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>16</id>            <author_shortname>Singer MA</author_shortname>            <author_fullname>Michael A Singer</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>17</id>            <author_shortname>Richmond TA</author_shortname>            <author_fullname>Todd A Richmond</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>18</id>            <author_shortname>Dorschner MO</author_shortname>            <author_fullname>Michael O Dorschner</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>19</id>            <author_shortname>McArthur M</author_shortname>            <author_fullname>Michael McArthur</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>20</id>            <author_shortname>Hawrylycz M</author_shortname>            <author_fullname>Michael Hawrylycz</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>21</id>            <author_shortname>Green RD</author_shortname>            <author_fullname>Roland D Green</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>22</id>            <author_shortname>Navas PA</author_shortname>            <author_fullname>Patrick A Navas</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>23</id>            <author_shortname>Noble WS</author_shortname>            <author_fullname>William S Noble</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>24</id>            <author_shortname>Stamatoyannopoulos JA</author_shortname>            <author_fullname>John A Stamatoyannopoulos</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Department of Genome Sciences, University of Washington, 1705 NE Pacific St., Box 357730, Seattle, Washington 98195, USA</institution>        <institution>Division of Medical Genetics, Department of Medicine, University of Washington, 1705 NE Pacific St., Box 357730, Seattle, Washington 98195, USA</institution>        <institution>Nimblegen Systems, Inc., 1 Science Court, Madison, Wisconsin 53711, USA</institution>        <institution>Department of Microbiology, John Innes Centre, Norwich Research Park, Colney, Norwich, NR4 7UH, UK</institution>        <institution>Allen Institute for Brain Sciences, 551 N. 34th Street, Seattle, Washington 98103, USA</institution>        <institution>These authors contributed equally to this work.</institution>    </publication>    <publication pub_id="4">        <status>On</status>        <application>DNase Hypersensitivity</application>        <title>News and Views: How to find an opening (or lots of them)</title>        <journal>Nature Methods</journal>        <issue>2006 Jul;3(7):501-2.</issue>        <pubdate>2006-07-01</pubdate>        <epubdate>2006-06-21</epubdate>        <url>http://dx.doi.org/10.1038/nmeth0706-501</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>DNase-chip and DNase-array: similar names for two different new approaches that give a genomic perspective to the conventional DNase I hypersensitivity assay used to measure chromatin accessibility.</abstract>        <author>            <id>1</id>            <author_shortname>Giresi PG</author_shortname>            <author_fullname>Paul G Giresi</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Lieb JD</author_shortname>            <author_fullname>Jason D Lieb</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Department of Biology and Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina</institution>    </publication>    <publication pub_id="5">        <status>On</status>        <application>DNase Hypersensitivity</application>        <title>DNase-chip: a high-resolution method to identify DNase I hypersensitive sites using tiled microarrays</title>        <journal>Nature Methods</journal>        <issue>2006 Jul;3(7):503-9.</issue>        <pubdate>2006-08-01</pubdate>        <epubdate>2006-06-21</epubdate>        <url>http://dx.doi.org/10.1038/nmeth888</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>Mapping DNase I hypersensitive sites is an accurate method of identifying the location of gene regulatory elements, including promoters, enhancers, silencers and locus control regions. Although Southern blots are the traditional method of identifying DNase I hypersensitive sites, the conventional manual method is not readily scalable to studying large chromosomal regions, much less the entire genome. Here we describe DNase-chip, an approach that can rapidly identify DNase I hypersensitive sites for any region of interest, or potentially for the entire genome, by using tiled microarrays. We used DNase-chip to identify DNase I hypersensitive sites accurately from a representative 1% of the human genome in both primary and immortalized cell types. We found that although most DNase I hypersensitive sites were present in both cell types studied, some of them were cell-type specific. This method can be applied globally or in a targeted fashion to any tissue from any species with a sequenced genome.</abstract>        <author>            <id>1</id>            <author_shortname>Crawford GE</author_shortname>            <author_fullname>Crawford GE</author_fullname>            <author_affiliation>1,3</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Davis S</author_shortname>            <author_fullname>Davis S</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Scacheri PC</author_shortname>            <author_fullname>Scacheri PC</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Renaud G</author_shortname>            <author_fullname>Renaud G</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Halawi MJ</author_shortname>            <author_fullname>Halawi MJ</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Erdos MR</author_shortname>            <author_fullname>Erdos MR</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Green R</author_shortname>            <author_fullname>Green R</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Meltzer PS</author_shortname>            <author_fullname>Meltzer PS</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Wolfsberg TG</author_shortname>            <author_fullname>Wolfsberg TG</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Collins FS</author_shortname>            <author_fullname>Collins FS</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>National Human Genome Research Institute, National Institutes of Health, Building 31, Room 4B09, Bethesda, Maryland 20892, USA.</institution>        <institution>NimbleGen Systems, Incorporated, 1 Science Court, Madison, Wisconsin 53711, USA.</institution>        <institution>Present address: Institute for Genome Sciences &amp; Policy, and Department of Pediatrics, 101 Science Drive, CIEMAS Building, Duke University, Box 3382, Durham, North Carolina 27708, USA.</institution>    </publication>    <publication pub_id="6">        <status>On</status>        <application>ChIP-chip</application>        <title>A computational genomics approach to identify cis-regulatory modules from chromatin immunoprecipitation microarray data--A case study using E2F1</title>        <journal>Genome Res.</journal>        <issue>2006 Dec;16(12):1585-95. Epub 2006 Oct 19.</issue>        <pubdate>2006-12-01</pubdate>        <epubdate>2006-10-19</epubdate>        <url>http://dx.doi.org/10.1101/gr.5520206</url>        <url_pdf></url_pdf>        <url_supplemental>http://www.genome.org/cgi/content/full/gr.5520206/DC1</url_supplemental>        <abstract>Advances in high-throughput technologies, such as ChIP-chip, and the completion of human and mouse genomic sequences now allow analysis of the mechanisms of gene regulation on a systems level. In this study, we have developed a computational genomics approach (termed ChIPModules), which begins with experimentally determined binding sites and integrates positional weight matrices constructed from transcription factor binding sites, a comparative genomics approach, and statistical learning methods to identify transcriptional regulatory modules. We began with E2F1 binding site information obtained from ChIP-chip analyses of ENCODE regions, from both HeLa and MCF7 cells. Our approach not only distinguished targets from nontargets with a high specificity, but it also identified five regulatory modules for E2F1. One of the identified modules predicted a colocalization of E2F1 and AP-2alpha on a set of target promoters with an intersite distance of &#60;270 bp. We tested this prediction using ChIP-chip assays with arrays containing approximately 14,000 human promoters. We found that both E2F1 and AP-2alpha bind within the predicted distance to a large number of human promoters, demonstrating the strength of our sequence-based, unbiased, and universal protocol. Finally, we have used our ChIPModules approach to develop a database that includes thousands of computationally identified and/or experimentally verified E2F1 target promoters.</abstract>        <author>            <id>1</id>            <author_shortname>Jin VX</author_shortname>            <author_fullname>Jin VX</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Rabinovich A</author_shortname>            <author_fullname>Rabinovich A</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Squazzo SL</author_shortname>            <author_fullname>Squazzo SL</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Green R</author_shortname>            <author_fullname>Green R</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Farnham PJ</author_shortname>            <author_fullname>Farnham PJ</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Department of Pharmacology and the Genome Center, University of California-Davis, Davis, California 95616, USA</institution>    </publication>    <publication pub_id="7">        <status>On</status>        <application>ChIP-chip</application>        <title>Distinct Functions of Dispersed GATA Factor Complexes at an Endogenous Gene Locus</title>        <journal>Mol. Cell. Biol.</journal>        <issue>2006 Oct;26(19):7056-67</issue>        <pubdate>2006-10-01</pubdate>        <epubdate>2006-10-01</epubdate>        <url>http://dx.doi.org/10.1128/MCB.01033-06</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>The reciprocal expression of GATA-1 and GATA-2 during hematopoiesis is an important determinant of red blood cell development. Whereas Gata2 is preferentially transcribed early in hematopoiesis, elevated GATA-1 levels result in GATA-1 occupancy at sites upstream of the Gata2 locus and transcriptional repression. GATA-2 occupies these sites in the transcriptionally active locus, suggesting that a "GATA switch" abrogates GATA-2-mediated positive autoregulation. Chromatin immunoprecipitation (ChIP) coupled with genomic microarray analysis and quantitative ChIP analysis with GATA-1-null cells expressing an estrogen receptor ligand binding domain fusion to GATA-1 revealed additional GATA switches 77 kb upstream of Gata2 and within intron 4 at +9.5 kb. Despite indistinguishable GATA-1 occupancy at -77 kb and +9.5 kb versus other GATA switch sites, GATA-1 functioned uniquely at the different regions. GATA-1 induced histone deacetylation at and near Gata2 but not at the -77 kb region. The -77 kb region, which was DNase I hypersensitive in both active and inactive states, conferred equivalent enhancer activities in GATA-1- and GATA-2-expressing cells. By contrast, the +9.5 kb region exhibited considerably stronger enhancer activity in GATA-2- than in GATA-1-expressing cells, and other GATA switch sites were active only in GATA-1- or GATA-2-expressing cells. Chromosome conformation capture analysis demonstrated higher-order interactions between the -77 kb region and Gata2 in the active and repressed states. These results indicate that dispersed GATA factor complexes function via long-range chromatin interactions and qualitatively distinct activities to regulate Gata2 transcription.</abstract>        <author>            <id>1</id>            <author_shortname>Grass JA</author_shortname>            <author_fullname>Grass JA</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Jing H</author_shortname>            <author_fullname>Jing H</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Kim SI</author_shortname>            <author_fullname>Kim SI</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Martowicz ML</author_shortname>            <author_fullname>Martowicz ML</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Pal S</author_shortname>            <author_fullname>Pal S</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Blobel GA</author_shortname>            <author_fullname>Blobel GA</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Bresnick EH</author_shortname>            <author_fullname>Bresnick EH</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>University of Wisconsin Medical School, Department of Pharmacology, 1300 University Avenue, Madison, WI</institution>    </publication>    <publication pub_id="8">        <status>On</status>        <application>ChIP-chip</application>        <title>Genome-wide mapping of Polycomb target genes unravels their roles in cell fate transitions</title>        <journal>Genes Dev.</journal>        <issue>2006 May 1;20(9):1123-36. Epub 2006 Apr 17.</issue>        <pubdate>2006-05-01</pubdate>        <epubdate>2006-04-17</epubdate>        <url>http://dx.doi.org/10.1101/gad.381706</url>        <url_pdf>http://www.genesdev.org/cgi/reprint/20/9/1123</url_pdf>        <url_supplemental>http://www.genesdev.org/cgi/content/full/gad.381706/DC1</url_supplemental>        <abstract>The Polycomb group (PcG) proteins form chromatin-modifying complexes that are essential for embryonic development and stem cell renewal and are commonly deregulated in cancer. Here, we identify their target genes using genome-wide location analysis in human embryonic fibroblasts. We find that Polycomb-Repressive Complex 1 (PRC1), PRC2, and tri-methylated histone H3K27 co-occupy &gt;1000 silenced genes with a strong functional bias for embryonic development and cell fate decisions. We functionally identify 40 genes derepressed in human embryonic fibroblasts depleted of the PRC2 components (EZH2, EED, SUZ12) and the PRC1 component, BMI-1. Interestingly, several markers of osteogenesis, adipogenesis, and chrondrogenesis are among these genes, consistent with the mesenchymal origin of fibroblasts. Using a neuronal model of differentiation, we delineate two different mechanisms for regulating PcG target genes. For genes activated during differentiation, PcGs are displaced. However, for genes repressed during differentiation, we paradoxically find that they are already bound by the PcGs in nondifferentiated cells despite being actively transcribed. Our results are consistent with the hypothesis that PcGs are part of a preprogrammed memory system established during embryogenesis marking certain key genes for repressive signals during subsequent developmental and differentiation processes.</abstract>        <author>            <id>1</id>            <author_shortname>Bracken AP</author_shortname>            <author_fullname>Adrian P. Bracken</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Dietrich N</author_shortname>            <author_fullname>Nikolaj Dietrich</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Pasini D</author_shortname>            <author_fullname>Diego Pasini</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Hansen KH</author_shortname>            <author_fullname>Klaus H. Hansen</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Helin K</author_shortname>            <author_fullname>Kristian Helin</author_fullname>            <author_affiliation>1,2</author_affiliation>        </author>        <institution>Biotech Research and Innovation Centre (BRIC), 2100 Copenhagen &#216;, Denmark;</institution>        <institution>Faculty of Health Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark</institution>    </publication>    <publication pub_id="9">        <status>On</status>        <application>ChIP-chip</application>        <title>Development of Arabidopsis whole-genome microarrays and their application to the discovery of binding sites for the TGA2 transcription factor in salicylic acid-treated plants</title>        <journal>Plant J.</journal>        <issue>2006 Jul;47(1):152-62.</issue>        <pubdate>2006-07-01</pubdate>        <epubdate>2006-07-01</epubdate>        <url>http://dx.doi.org/10.1111/j.1365-313X.2006.02770.x</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>We have developed two long-oligonucleotide microarrays for the analysis of genome features in Arabidopsis thaliana, in particular for the high-throughput identification of transcription factor-binding sites. The first platform contains 190 000 probes representing the 2-kb regions upstream of all annotated genes at a density of seven probes per promoter. The second platform is divided into three chips, each of over 390 000 features, and represents the entire Arabidopsis genome at a density of one probe per 90 bases. Protein&#226;&#8364;&#8220;DNA complexes resulting from the formaldehyde fixation of leaves of plants 2 h after exposure to 1 mm salicylic acid (SA) were immunoprecipitated using antibodies against the TGA2 transcription factor. After reversal of the cross-links and amplification, the resulting ChIP sample was hybridized to both platforms. High signal ratios of the ChIP sample versus raw chromatin for clusters of neighboring probes provided evidence for 51 putative binding sites for TGA2, including the only previously confirmed site in the promoter of PR-1 (At2g14610). Enrichment of several regions was confirmed by quantitative real-time PCR. Motif search revealed that the palindromic octamer TGACGTCA was found in 55% of the enriched regions. Interestingly, 15 of the putative binding sites for TGA2 lie outside the presumptive promoter regions. The effect of the 2-h SA treatment on gene expression was measured using Affymetrix ATH1 arrays, and SA-induced genes were found to be significantly over-represented among genes neighboring putative TGA2-binding sites.</abstract>        <author>            <id>1</id>            <author_shortname>Thibaud-Nissen F</author_shortname>            <author_fullname>Fran&#231;oise Thibaud-Nissen</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Wu H</author_shortname>            <author_fullname>Hank Wu</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Richmond T</author_shortname>            <author_fullname>Todd Richmond</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Redman JC</author_shortname>            <author_fullname>Julia C. Redman</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Johnson C</author_shortname>            <author_fullname>Christopher Johnson</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Green R</author_shortname>            <author_fullname>Roland Green</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Arias J</author_shortname>            <author_fullname>Jonathan Arias</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Town CD</author_shortname>            <author_fullname>Christopher D. Town</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>The Institute for Genomic Research, 9712 Medical Center Drive, Rockville, MD 20850, USA</institution>        <institution>NimbleGen Systems Inc., Madison, WI 53711, USA</institution>        <institution>University of Maryland, Baltimore County, Baltimore, MD 21250, USA</institution>    </publication>    <publication pub_id="10">        <status>On</status>        <application>ChIP-chip</application>        <title>Suz12 binds to silenced regions of the genome in a cell-type-specific manner</title>        <journal>Genome Res.</journal>        <issue>2006 Jul;16(7):890-900. Epub 2006 Jun 2.</issue>        <pubdate>2006-07-01</pubdate>        <epubdate>2006-06-02</epubdate>        <url>http://dx.doi.org/10.1101/gr.5306606</url>        <url_pdf></url_pdf>        <url_supplemental>http://www.genome.org/cgi/content/full/gr.5306606/DC1</url_supplemental>        <abstract>Suz12 is a component of the Polycomb group complexes 2, 3, and 4 (PRC 2/3/4). These complexes are critical for proper embryonic development, but very few target genes have been identified in either mouse or human cells. Using a variety of ChIP-chip approaches, we have identified a large set of Suz12 target genes in five different human and mouse cell lines. Interestingly, we found that Suz12 target promoters are cell type specific, with transcription factors and homeobox proteins predominating in embryonal cells and glycoproteins and immunoglobulin-related proteins predominating in adult tumors. We have also characterized the localization of other components of the PRC complex with Suz12 and investigated the overall relationship between Suz12 binding and markers of active versus inactive chromatin, using both promoter arrays and custom tiling arrays. Surprisingly, we find that the PRC complexes can be localized to discrete binding sites or spread through large regions of the mouse and human genomes. Finally, we have shown that some Suz12 target genes are bound by OCT4 in embryonal cells and suggest that OCT4 maintains stem cell self-renewal, in part, by recruiting PRC complexes to certain genes that promote differentiation.</abstract>        <author>            <id>1</id>            <author_shortname>Squazzo SL</author_shortname>            <author_fullname>Sharon L. Squazzo</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>O&#8217;Geen H</author_shortname>            <author_fullname>Henriette O&#8217;Geen</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Komashko VM</author_shortname>            <author_fullname>Vitalina M. Komashko</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Krig SR</author_shortname>            <author_fullname>Sheryl R. Krig</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Jin VX</author_shortname>            <author_fullname>Victor X. Jin</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Jang SW</author_shortname>            <author_fullname>Sung Jang</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Margueron R</author_shortname>            <author_fullname>Raphael Margueron</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Reinberg D</author_shortname>            <author_fullname>Danny Reinberg</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Green R</author_shortname>            <author_fullname>Roland Green</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Farnham PJ</author_shortname>            <author_fullname>Peggy J. Farnham</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Department of Pharmacology and the Genome Center, University of California-Davis, Davis, California 95616, USA</institution>        <institution>NimbleGen Systems Inc., Madison, Wisconsin 53711, USA</institution>        <institution>Howard Hughes Medical Institute, Division of Nucleic Acids Enzymology, Department of Biochemistry, Robert Wood Johnson Medical School, Piscataway, New Jersey 08854, USA</institution>        <institution>Graduate Program in Cellular and Molecular Biology, University of Wisconsin, Madison, Wisconsin 53706, USA</institution>    </publication>    <publication pub_id="11">        <status>On</status>        <application>ChIP-chip</application>        <title>T-bet binding to newly identified target gene promoters is cell-type independent, but results in variable context-dependent functional effects</title>        <journal>J. Biol. Chem.</journal>        <issue>Vol. 281, Issue 17, 11992-12000, April 28, 2006</issue>        <pubdate>2006-04-28</pubdate>        <epubdate>2006-02-10</epubdate>        <url>http://dx.doi.org/10.1074/jbc.M513613200</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>Recently developed target gene identification strategies based upon the chromatin immunoprecipitation assay provide a powerful method to determine the localization of transcription factor binding within mammalian genomes. However, in many cases, it is unclear if the binding capacity of a transcription factor correlates with an obligate role in gene regulation in diverse contexts. It is therefore important to carefully examine the relationship between transcription factor binding and its ability to functionally regulate gene expression. T-bet is a T-box transcription factor expressed in several hematopoietic cell types. By utilizing a chromatin immunoprecipitation assay coupled to genomic microarray technology approach, we identified numerous promoters, including CXCR3, IL2Rbeta, and CCL3, that are bound by T-bet in B cells. Most surprisingly, the ability of T-bet to associate with the target promoters is not dependent upon the cell type background. Several of the promoters appear to be functionally regulated by T-bet. However, we could not detect a functional consequence for T-bet association with many of the identified promoters in overexpression studies or an examination of wild type and T-bet-/- primary B, CD4+, and CD8+ T cells. Thus, there is a high variability in the functional consequences, if any, that result from the association of T-bet with individual target promoters.</abstract>        <author>            <id>1</id>            <author_shortname>Beima KM</author_shortname>            <author_fullname>Beima KM</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Miazgowicz MM</author_shortname>            <author_fullname>Miazgowicz MM</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Lewis MD</author_shortname>            <author_fullname>Lewis MD</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Yan PS</author_shortname>            <author_fullname>Yan PS</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Huang TH</author_shortname>            <author_fullname>Huang TH</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Weinmann AS</author_shortname>            <author_fullname>Weinmann AS</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Immunology Dept., University of Washington, Seattle, WA</institution>    </publication>    <publication pub_id="12">        <status>On</status>        <application>ChIP-chip</application>        <title>Unbiased location analysis of E2F1-binding sites suggests a widespread role for E2F1 in the human genome</title>        <journal>Genome Res.</journal>        <issue>2006 May;16(5):595-605. Epub 2006 Apr 10.</issue>        <pubdate>2006-05-01</pubdate>        <epubdate>2006-04-10</epubdate>        <url>http://dx.doi.org/10.1101/gr.4887606</url>        <url_pdf>http://www.genome.org/cgi/reprint/16/5/595</url_pdf>        <url_supplemental>http://www.genome.org/cgi/content/full/gr.4887606/DC1</url_supplemental>        <abstract>The E2F family of transcription factors regulates basic cellular processes. Here, we take an unbiased approach towards identifying E2F1 target genes by examining localization of E2F1-binding sites using high-density oligonucleotide tiling arrays. To begin, we developed a statistically-based methodology for analysis of ChIP-chip data obtained from arrays that represent 30 Mb of the human genome. Using this methodology, we identified regions bound by E2F1, MYC, and RNA Polymerase II (POLR2A). We found a large number of binding sites for all three factors; extrapolation suggests there may be approximately 20,000-30,000 E2F1- and MYC-binding sites and approximately 12,000-17,000 active promoters in HeLa cells. In contrast to our results for MYC, we find that the majority of E2F1-binding sites (&gt;80%) are located in core promoters and that 50% of the sites overlap transcription starts. Only a small fraction of E2F1 sites possess the canonical binding motif. Surprisingly, we found that approximately 30% of genes in the 30-Mb region possessed an E2F1 binding site in a core promoter and E2F1 was bound near to 83% of POLR2A-bound sites. To determine if these results were representative of the entire human genome, we performed ChIP-chip analyses of approximately 24,000 promoters and confirmed that greater than 20% of the promoters were bound by E2F1. Our results suggest that E2F1 is recruited to promoters via a method distinct from recognition of the known consensus site and point toward a new understanding of E2F1 as a factor that contributes to the regulation of a large fraction of human genes.</abstract>        <author>            <id>1</id>            <author_shortname>Bieda M</author_shortname>            <author_fullname>Mark Bieda</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Xu X</author_shortname>            <author_fullname>Xiaoqin Xu</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Singer M</author_shortname>            <author_fullname>Mike Singer</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Green R</author_shortname>            <author_fullname>Roland Green</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Farnham PJ</author_shortname>            <author_fullname>Peggy J. Farnham1</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <institution>Department of Pharmacology and the Genome Center, University of California-Davis, Davis, California</institution>        <institution>NimbleGen Systems Inc., Madison, Wisconsin</institution>    </publication>    <publication pub_id="13">        <status>On</status>        <application>ChIP-chip</application>        <title>High-resolution ChIP-chip analysis reveals that the Drosophila MSL complex selectively identifies active genes on the male X chromosome</title>        <journal>Genes Dev.</journal>        <issue>2006 Apr 1;20(7):848-57. Epub 2006 Mar 17.</issue>        <pubdate>2006-04-01</pubdate>        <epubdate>2006-03-17</epubdate>        <url>http://dx.doi.org/10.1101/gad.1400206</url>        <url_pdf>http://www.genesdev.org/cgi/reprint/20/7/848</url_pdf>        <url_supplemental>http://www.genesdev.org/cgi/content/full/gad.1400206/DC1</url_supplemental>        <abstract>X-chromosome dosage compensation in Drosophila requires the male-specific lethal (MSL) complex, which up-regulates gene expression from the single male X chromosome. Here, we define X-chromosome-specific MSL binding at high resolution in two male cell lines and in late-stage embryos. We find that the MSL complex is highly enriched over most expressed genes, with binding biased toward the 3' end of transcription units. The binding patterns are largely similar in the distinct cell types, with ~600 genes clearly bound in all three cases. Genes identified as clearly bound in one cell type and not in another indicate that attraction of MSL complex correlates with expression state. Thus, sequence alone is not sufficient to explain MSL targeting. We propose that the MSL complex recognizes most X-linked genes, but only in the context of chromatin factors or modifications indicative of active transcription. Distinguishing expressed genes from the bulk of the genome is likely to be an important function common to many chromatin organizing and modifying activities.</abstract>        <author>            <id>1</id>            <author_shortname>Alekseyenko AA</author_shortname>            <author_fullname>Artyom A. Alekseyenko</author_fullname>            <author_affiliation>1,2,3</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Larschan E</author_shortname>            <author_fullname>Erica Larschan</author_fullname>            <author_affiliation>2,3</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Lai WR</author_shortname>            <author_fullname>Weil R. Lai</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Park PJ</author_shortname>            <author_fullname>Peter J. Park</author_fullname>            <author_affiliation>2,4</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Kuroda MI</author_shortname>            <author_fullname>Mitzi I. Kuroda1</author_fullname>            <author_affiliation>2,3</author_affiliation>        </author>        <institution>Howard Hughes Medical Institute</institution>        <institution>Harvard-Partners Center for Genetics and Genomics, Brigham and Women's Hospital, Boston, Massachusetts</institution>        <institution>Department of Genetics, Harvard Medical School, Boston, Massachusetts</institution>        <institution>Children's Hospital Informatics Program, Boston, Massachusetts</institution>    </publication>    <publication pub_id="14">        <status>On</status>        <application>ChIP-chip</application>        <title>Drosophila Chromosome-wide gene-specific targeting of the dosage compensation complex</title>        <journal>Genes Dev.</journal>        <issue>2006 Apr 1;20(7):858-70. Epub 2006 Mar 17</issue>        <pubdate>2006-04-01</pubdate>        <epubdate>2006-03-17</epubdate>        <url>http://dx.doi.org/10.1101/gad.1399406</url>        <url_pdf>http://www.genesdev.org/cgi/reprint/20/7/858</url_pdf>        <url_supplemental>http://www.genesdev.org/cgi/content/full/gad.1399406/DC1</url_supplemental>        <abstract>The dosage compensation complex (DCC) of Drosophila melanogaster is capable of distinguishing the single male X from the other chromosomes in the nucleus. It selectively interacts in a discontinuous pattern with much of the X chromosome. How the DCC identifies and binds the X, including binding to the many genes that require dosage compensation, is currently unknown. To identify bound genes and attempt to isolate the targeting cues, we visualized male-specific lethal 1 (MSL1) protein binding along the X chromosome by combining chromatin immunoprecipitation with high-resolution microarrays. More than 700 binding regions for the DCC were observed, encompassing more than half the genes found on the X chromosome. In addition, several rare autosomal binding sites were identified. Essential genes are preferred targets, and genes binding high levels of DCC appear to experience the most compensation (i.e., greatest increase in expression). DCC binding clearly favors genes over intergenic regions, and binds most strongly to the 3' end of transcription units. Within the targeted genes, the DCC exhibits a strong preference for exons and coding sequences. Our results demonstrate gene-specific binding of the DCC, and identify several sequence elements that may partly direct its targeting.</abstract>        <author>            <id>1</id>            <author_shortname>Gilfillan GD</author_shortname>            <author_fullname>Gregor D. Gilfillan</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Straub T</author_shortname>            <author_fullname>Tobias Straub</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>de Wit E</author_shortname>            <author_fullname>Elzo de Wit</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Greil F</author_shortname>            <author_fullname>Frauke Greil</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Lamm R</author_shortname>            <author_fullname>Rosemarie Lamm</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>van Steensel B</author_shortname>            <author_fullname>Bas van Steense</author_fullname>            <author_affiliation>1,2</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Becker PB</author_shortname>            <author_fullname>Peter B. Becker</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Adolf-Butenandt-Institut, Molekularbiologie, Ludwig-Maximilians-Universit&#228;t M&#252;nchen, Germany</institution>        <institution>Netherlands Cancer Institute, Amsterdam, The Netherlands</institution>    </publication>    <publication pub_id="15">        <status>On</status>        <application>ChIP-chip</application>        <title>Genome-Wide Analysis of Menin Binding Provides Insights to MEN1 Tumorigenesis</title>        <journal>PLoS Genet.</journal>        <issue>2006 Apr;2(4):e51. Epub 2006 Apr 7.</issue>        <pubdate>2006-04-07</pubdate>        <epubdate>2006-04-07</epubdate>        <url>http://dx.doi.org/10.1371/journal.pgen.0020051</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>Multiple Endocrine Neoplasia, type I (MEN1) is a familial cancer syndrome characterized primarily by tumors of multiple endocrine glands. The gene for MEN1 encodes a ubiquitously expressed tumor suppressor protein called menin. Menin was recently shown to interact with several components of a trithorax family histone methyltransferase complex including ASH2, Rbbp5, WDR5, and the leukemia proto-oncoprotein MLL. To elucidate menin's role as a tumor suppressor and gain insights to the endocrine-specific tumor phenotype in MEN1, we mapped the genomic binding sites of menin, MLL1, and Rbbp5, to ~20,000 promoters in Hela S3, HepG2 and pancreatic islet cells using the strategy of chromatin-immunoprecipitation coupled with microarray analysis (ChIP-chip). We found that menin, MLL1, and Rbbp5 localize to the promoters of thousands of human genes, but do not always bind together. These data suggest that menin functions as a general regulator of transcription. We also found that factor occupancy generally correlates with high gene expression, but the loss of menin does not result in significant changes in most transcript levels. One exception is the developmentally programmed transcription factor, HLXB9, which is overexpressed in islets in the absence of menin. Our findings expand the realm of menin-targeted genes several hundred-fold beyond that previously described, and provide potential insights to the endocrine tumor bias observed in MEN1 patients.</abstract>        <author>            <id>1</id>            <author_shortname>Scacheri PC</author_shortname>            <author_fullname>Peter C Scacheri</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Davis S</author_shortname>            <author_fullname>Sean Davis</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Odom DT</author_shortname>            <author_fullname>Duncan T Odom</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Crawford GE</author_shortname>            <author_fullname>Gregory E Crawford</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Perkins S</author_shortname>            <author_fullname>Stacie Perkins</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Halawi MJ</author_shortname>            <author_fullname>Mohamad J Halawi</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Agarwal SK</author_shortname>            <author_fullname>Sunita K Agarwal</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Marx SJ</author_shortname>            <author_fullname>Stephen J Marx</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Spiegel AM</author_shortname>            <author_fullname>Allen M Spiegel</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Meltzer PS</author_shortname>            <author_fullname>Paul S Meltzer</author_fullname>            <author_affiliation>6</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Collins FS</author_shortname>            <author_fullname>Francis S Collins</author_fullname>            <author_affiliation>7,8</author_affiliation>        </author>        <institution>NIH, NHGRI, Bethesda, MD, United States of America</institution>        <institution>NIH, Bethesda, MD, United States of America</institution>        <institution>Whitehead Institute for Biomedical Research, Cambridge, Massachusetts, United States of America</institution>        <institution>NIH, NIDDK, Bethesda, MD, United States of America</institution>        <institution>NIH, NIDCD, Bethesda, MD, United States of America</institution>        <institution>Cancer Genetics Branch, National Human Genome Research Institute, NIH, Bethesda, MD, National Human Genome Research Institute, Bethesda, MD, USA,</institution>        <institution>National Institutes of Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America</institution>        <institution>To whom correspondence should be addressed. E-mail: fc23a@nih.gov</institution>    </publication>    <publication pub_id="16">        <status>On</status>        <application>ChIP-chip</application>        <title>Genome-scale profiling of histone H3.3 replacement patterns</title>        <journal>Nat. Genet.</journal>        <issue>2005 Oct;37(10):1090-7. Epub 2005 Sep 11</issue>        <pubdate>2005-10-01</pubdate>        <epubdate>2005-09-11</epubdate>        <url>http://dx.doi.org/10.1038/ng1637</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>Histones of multicellular organisms are assembled into chromatin primarily during DNA replication. When chromatin assembly occurs at other times, the histone H3.3 variant replaces canonical H3. Here we introduce a new strategy for profiling epigenetic patterns on the basis of H3.3 replacement, using microarrays covering roughly one-third of the Drosophila melanogaster genome at 100-bp resolution. We identified patterns of H3.3 replacement over active genes and transposons. H3.3 replacement occurred prominently at sites of abundant RNA polymerase II and methylated H3 Lys4 throughout the genome and was enhanced on the dosage-compensated male X chromosome. Active genes were depleted of histones at promoters and were enriched in H3.3 from upstream to downstream of transcription units. We propose that deposition and inheritance of actively modified H3.3 in regulatory regions maintains transcriptionally active chromatin.</abstract>        <author>            <id>1</id>            <author_shortname>Mito Y</author_shortname>            <author_fullname>Yoshiko Mito</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Henikoff JG</author_shortname>            <author_fullname>Jorja G. Henikoff</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Henikoff S</author_shortname>            <author_fullname>Steven Henikoff</author_fullname>            <author_affiliation>1,2</author_affiliation>        </author>        <institution>Fred Hutchinson Cancer Research Center, Seattle Washington</institution>        <institution>Howard Hughes Medical Institute</institution>    </publication>    <publication pub_id="17">        <status>On</status>        <application>ChIP-chip</application>        <title>Immobilization of Escherichia coli RNA Polymerase and Location of Binding Sites by Use of Chromatin Immunoprecipitation and Microarrays</title>        <journal>J. Bacteriol.</journal>        <issue>2005 Sep;187(17):6166-74.</issue>        <pubdate>2005-09-01</pubdate>        <epubdate>2005-09-01</epubdate>        <url>http://dx.doi.org/10.1128/JB.187.17.6166-6174.2005</url>        <url_pdf>http://jb.asm.org/cgi/reprint/187/17/6166</url_pdf>        <url_supplemental>http://jb.asm.org/cgi/content/full/187/17/6166/DC1</url_supplemental>        <abstract>The genome-wide location of RNA polymerase binding sites was determined in Escherichia coli using chromatin immunoprecipitation and microarrays (chIP-chip). Cross-linked chromatin was isolated in triplicate from rifampin-treated cells, and DNA bound to RNA polymerase was precipitated with an antibody specific for the beta' subunit. The DNA was amplified and hybridized to "tiled" oligonucleotide microarrays representing the whole genome at 25-bp resolution. A total of 1,139 binding sites were detected and evaluated by comparison to gene expression data from identical conditions and to 961 promoters previously identified by established methods. Of the detected binding sites, 418 were located within 1,000 bp of a known promoter, leaving 721 previously unknown RNA polymerase binding sites. Within 200 bp, we were able to detect 51% (189/368) of the known sigma70-specific promoters occurring upstream of an expressed open reading frame and 74% (273/368) within 1,000 bp. Conversely, many known promoters were not detected by chIP-chip, leading to an estimated 26% negative-detection rate. Most of the detected binding sites could be associated with expressed transcription units, but 299 binding sites occurred near inactive transcription units. This map of RNA polymerase binding sites represents a foundation for studies of transcription factors in E. coli and an important evaluation of the chIP-chip technique.</abstract>        <author>            <id>1</id>            <author_shortname>Herring CD</author_shortname>            <author_fullname>Christopher D. Herring</author_fullname>            <author_affiliation>1,2,4</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Raffaelle M</author_shortname>            <author_fullname>Marni Raffaelle</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Allen TE</author_shortname>            <author_fullname>Timothy E. Allen</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Kanin EI</author_shortname>            <author_fullname>Elenita I. Kanin</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Landick R</author_shortname>            <author_fullname>Robert Landick</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Ansari AZ</author_shortname>            <author_fullname>Aseem Z. Ansari</author_fullname>            <author_affiliation>2,3</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Palsson BO</author_shortname>            <author_fullname>Bernhard &#216;. Palsson</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Department of Bioengineering, University of California San Diego, San Diego, California</institution>        <institution>Department of Biochemistry, University of Wisconsin Madison, Madison, Wisconsin</institution>        <institution>Genome Center of Wisconsin, Madison, Wisconsin</institution>        <institution>Department of Bacteriology, University of Wisconsin Madison, Madison, Wisconsin</institution>    </publication>    <publication pub_id="18">        <status>On</status>        <application>ChIP-chip</application>        <title>A high-resolution map of active promoters in the human genome</title>        <journal>Nature</journal>        <issue>2005 Aug 11;436(7052):876-80. Epub 2005 Jun 29.</issue>        <pubdate>2005-08-11</pubdate>        <epubdate>2005-06-29</epubdate>        <url>http://dx.doi.org/10.1038/nature03877</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>In eukaryotic cells, transcription of every protein-coding gene begins with the assembly of an RNA polymerase II preinitiation complex (PIC) on the promoter. The promoters, in conjunction with enhancers, silencers and insulators, define the combinatorial codes that specify gene expression patterns. Our ability to analyse the control logic encoded in the human genome is currently limited by a lack of accurate information regarding the promoters for most genes. Here we describe a genome-wide map of active promoters in human fibroblast cells, determined by experimentally locating the sites of PIC binding throughout the human genome. This map defines 10,567 active promoters corresponding to 6,763 known genes and at least 1,196 un-annotated transcriptional units. Features of the map suggest extensive use of multiple promoters by the human genes and widespread clustering of active promoters in the genome. In addition, examination of the genome-wide expression profile reveals four general classes of promoters that define the transcriptome of the cell. These results provide a global view of the functional relationships among transcriptional machinery, chromatin structure and gene expression in human cells.</abstract>        <author>            <id>1</id>            <author_shortname>Kim TH</author_shortname>            <author_fullname>Tae Hoon Kim</author_fullname>            <author_affiliation>1,5</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Barrera LO</author_shortname>            <author_fullname>Leah O. Barrera</author_fullname>            <author_affiliation>1,5</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Zheng M</author_shortname>            <author_fullname>Ming Zheng</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Qu C</author_shortname>            <author_fullname>Chunxu Qu</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Singer MA</author_shortname>            <author_fullname>Michael A. Singer</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Richmond TA</author_shortname>            <author_fullname>Todd A. Richmond</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Wu Y</author_shortname>            <author_fullname>Yingnian Wu</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Green RD</author_shortname>            <author_fullname>Roland D. Green</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Ren B</author_shortname>            <author_fullname>Bing Ren</author_fullname>            <author_affiliation>1,2</author_affiliation>        </author>        <institution>Ludwig Institute for Cancer Research</institution>        <institution>Department of Cellular and Molecular Medicine and Moores Cancer Center, UCSD School of Medicine</institution>        <institution>Department of Statistics, University of California, Los Angeles</institution>        <institution>NimbleGen Systems, Inc.</institution>        <institution>These authors contributed equally to this work</institution>    </publication>    <publication pub_id="19">        <status>On</status>        <application>ChIP-chip</application>        <title>Silencing of human polycomb target genes is associated with methylation of histone H3 Lys 27</title>        <journal>Genes Dev.</journal>        <issue>2004 Jul 1;18(13):1592-605</issue>        <pubdate>2004-07-01</pubdate>        <epubdate>2004-07-01</epubdate>        <url>http://dx.doi.org/10.1101/gad.1200204</url>        <url_pdf>http://www.genesdev.org/cgi/reprint/18/13/1592</url_pdf>        <url_supplemental>http://www.genesdev.org/cgi/content/full/18/13/1592/DC1</url_supplemental>        <abstract>Polycomb group (PcG) complexes 2 and 3 are involved in transcriptional silencing. These complexes contain a histone lysine methyltransferase (HKMT) activity that targets different lysine residues on histones H1 or H3 in vitro. However, it is not known if these histones are methylation targets in vivo because the human PRC2/3 complexes have not been studied in the context of a natural promoter because of the lack of known target genes. Here we report the use of RNA expression arrays and CpG-island DNA arrays to identify and characterize human PRC2/3 target genes. Using oligonucleotide arrays, we first identified a cohort of genes whose expression changes upon siRNA-mediated removal of Suz12, a core component of PRC2/3, from colon cancer cells. To determine which of the putative target genes are directly bound by Suz12 and to precisely map the binding of Suz12 to those promoters, we combined a high-resolution chromatin immunoprecipitation (ChIP) analysis with custom oligonucleotide promoter arrays. We next identified additional putative Suz12 target genes by using ChIP coupled to CpG-island microarrays. We showed that HKMT-Ezh2 and Eed, two other components of the PRC2/3 complexes, colocalize to the target promoters with Suz12. Importantly, recruitment of Suz12, Ezh2 and Eed to target promoters coincides with methylation of histone H3 on Lys 27.</abstract>        <author>            <id>1</id>            <author_shortname>Kirmizis A</author_shortname>            <author_fullname>Antonis Kirmizis</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Bartley SM</author_shortname>            <author_fullname>Stephanie M. Bartley</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Kuzmichev A</author_shortname>            <author_fullname>Andrei Kuzmichev</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Margueron R</author_shortname>            <author_fullname>Raphael Margueron</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Reinberg D</author_shortname>            <author_fullname>Danny Reinberg</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Green R</author_shortname>            <author_fullname>Roland Green</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Farnham PJ</author_shortname>            <author_fullname>Peggy J. Farnham</author_fullname>            <author_affiliation>1,4</author_affiliation>        </author>        <institution>McArdle Laboratory for Cancer Research, University of Wisconsin Medical School, Madison, Wisconsin 53706, USA</institution>        <institution>Howard Hughes Medical Institute, Division of Nucleic Acids Enzymology, Department of Biochemistry, Robert Wood Johnson Medical School, Piscataway, New Jersey 08854, USA</institution>        <institution>NimbleGen Systems Inc., Madison, Wisconsin 53711, USA</institution>        <institution>Corresponding author. E-MAIL farnham@oncology.wisc.edu; FAX (608) 262-2824.</institution>    </publication>    <publication pub_id="20">        <status>On</status>        <application>CGH</application>        <title>Structural variation in the human genome</title>        <journal>Nat. Rev. Genet.</journal>        <issue>2006 Feb;7(2):85-97.</issue>        <pubdate>2006-02-01</pubdate>        <epubdate>2006-02-01</epubdate>        <url>http://dx.doi.org/10.1038/nrg1767</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>The first wave of information from the analysis of the human genome revealed SNPs to be the main source of genetic and phenotypic human variation. However, the advent of genome-scanning technologies has now uncovered an unexpectedly large extent of what we term 'structural variation' in the human genome. This comprises microscopic and, more commonly, submicroscopic variants, which include deletions, duplications and large-scale copy-number variants &#226;&#8364;&#8221; collectively termed copy-number variants or copy-number polymorphisms &#226;&#8364;&#8221; as well as insertions, inversions and translocations. Rapidly accumulating evidence indicates that structural variants can comprise millions of nucleotides of heterogeneity within every genome, and are likely to make an important contribution to human diversity and disease susceptibility.</abstract>        <author>            <id>1</id>            <author_shortname>Feuk L</author_shortname>            <author_fullname>Feuk L</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Carson AR</author_shortname>            <author_fullname>Carson AR</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Scherer SW</author_shortname>            <author_fullname>Scherer SW</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>The Centre for Applied Genomics and Program in Genetics and Genomic Biology, The Hospital for Sick Children and Department of Molecular and Medical Genetics, University of Toronto, MaRS Centre East Tower, 101 College Street, Room 14-701, Ontario M5G 1L7, Canada</institution>    </publication>    <publication pub_id="21">        <status>On</status>        <application>CGH</application>        <title>Ultra-high resolution array painting facilitates breakpoint sequencing</title>        <journal>J. Med. Genet.</journal>        <issue>2007 Jan;44(1):51-8. Epub 2006 Sep 13.</issue>        <pubdate>2007-01-01</pubdate>        <epubdate>2006-09-13</epubdate>        <url>http://dx.doi.org/10.1136/jmg.2006.044909</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>Objective: The authors describe a significant advance of the method of array painting which allows the rapid, ultra-high resolution mapping of translocation breakpoints such that rearrangement junction fragments can be amplified directly and sequenced. Method: Ultra-high resolution array painting involves the hybridisation of probes generated by PCR of small numbers of flow sorted derivative chromosomes to oligonucelotide arrays designed to tile breakpoint regions at extremely high resolution. Results and Discussion: The authors demonstrate how ultra-high resolution array painting of four balanced translocation cases rapidly and efficiently maps breakpoints to a point where junction fragments can be amplified easily and sequenced. With this new development, breakpoints can be mapped using just two array experiments, the first utilising whole genome array painting to tiling resolution large insert clone arrays, the second utilising ultra-high reolution oligonucleotide arrays targeted to the breakpoint regions. In this way breakpoints can be mapped and then sequenced in a matter of a few weeks.</abstract>        <author>            <id>1</id>            <author_shortname>Gribble SM</author_shortname>            <author_fullname>Gribble SM</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Kalaitzopoulos D</author_shortname>            <author_fullname>Kalaitzopoulos D</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Burford DC</author_shortname>            <author_fullname>Burford DC</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Prigmore E</author_shortname>            <author_fullname>Prigmore E</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Selzer RR</author_shortname>            <author_fullname>Selzer RR</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Ng BL</author_shortname>            <author_fullname>Ng BL</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Matthews NS</author_shortname>            <author_fullname>Matthews NS</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Porter KM</author_shortname>            <author_fullname>Porter KM</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Curley R</author_shortname>            <author_fullname>Curley R</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Lindasy SJ</author_shortname>            <author_fullname>Lindasy SJ</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Baptista J</author_shortname>            <author_fullname>Baptista J</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>12</id>            <author_shortname>Richmond TA</author_shortname>            <author_fullname>Richmond TA</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>13</id>            <author_shortname>Carter NP</author_shortname>            <author_fullname>Carter NP</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>The Wellcome Trust Sanger Institute, United Kingdom</institution>    </publication>    <publication pub_id="22">        <status>On</status>        <application>CGH</application>        <title>Discovery of previously unidentified genomic disorders from the duplication architecture of the human genome</title>        <journal>Nat. Genet.</journal>        <issue>2006 Sep;38(9):1038-42. Epub 2006 Aug 13.</issue>        <pubdate>2006-09-01</pubdate>        <epubdate>2006-08-13</epubdate>        <url>http://dx.doi.org/10.1038/ng1862</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>Genomic disorders are characterized by the presence of flanking segmental duplications that predispose these regions to recurrent rearrangement. Based on the duplication architecture of the genome, we investigated 130 regions that we hypothesized as candidates for previously undescribed genomic disorders1. We tested 290 individuals with mental retardation by BAC array comparative genomic hybridization and identified 16 pathogenic rearrangements, including de novo microdeletions of 17q21.31 found in four individuals. Using oligonucleotide arrays, we refined the breakpoints of this microdeletion, defining a 478-kb critical region containing six genes that were deleted in all four individuals. We mapped the breakpoints of this deletion and of four other pathogenic rearrangements in 1q21.1, 15q13, 15q24 and 17q12 to flanking segmental duplications, suggesting that these are also sites of recurrent rearrangement. In common with the 17q21.31 deletion, these breakpoint regions are sites of copy number polymorphism in controls, indicating that these may be inherently unstable genomic regions.</abstract>        <author>            <id>1</id>            <author_shortname>Sharp AJ</author_shortname>            <author_fullname>Andrew J Sharp</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Hansen S</author_shortname>            <author_fullname>Sierra Hansen</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Selzer RR</author_shortname>            <author_fullname>Rebecca R Selzer</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Cheng Z</author_shortname>            <author_fullname>Ze Cheng</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Regan R</author_shortname>            <author_fullname>Regina Regan</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Hurst JA</author_shortname>            <author_fullname>Jane A Hurst</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Stewart H</author_shortname>            <author_fullname>Helen Stewart</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Price SM</author_shortname>            <author_fullname>Sue M Price</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Blair E</author_shortname>            <author_fullname>Edward Blair</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Hennekam RC</author_shortname>            <author_fullname>Raoul C Hennekam</author_fullname>            <author_affiliation>5,6</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Fitzpatrick CA</author_shortname>            <author_fullname>Carrie A Fitzpatrick</author_fullname>            <author_affiliation>7</author_affiliation>        </author>        <author>            <id>12</id>            <author_shortname>Segraves R</author_shortname>            <author_fullname>Rick Segraves</author_fullname>            <author_affiliation>8</author_affiliation>        </author>        <author>            <id>13</id>            <author_shortname>Richmond TA</author_shortname>            <author_fullname>Todd A Richmond</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>14</id>            <author_shortname>Guiver C</author_shortname>            <author_fullname>Cheryl Guiver</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>15</id>            <author_shortname>Albertson DG</author_shortname>            <author_fullname>Donna G Albertson</author_fullname>            <author_affiliation>8,9</author_affiliation>        </author>        <author>            <id>16</id>            <author_shortname>Pinkel</author_shortname>            <author_fullname>Daniel Pinkel</author_fullname>            <author_affiliation>8</author_affiliation>        </author>        <author>            <id>17</id>            <author_shortname>Eis PS</author_shortname>            <author_fullname>Peggy S Eis</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>18</id>            <author_shortname>Schwartz S</author_shortname>            <author_fullname>Stuart Schwartz</author_fullname>            <author_affiliation>7</author_affiliation>        </author>        <author>            <id>19</id>            <author_shortname>Knight SJ</author_shortname>            <author_fullname>Samantha J L Knight</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>20</id>            <author_shortname>Eichler EE</author_shortname>            <author_fullname>Evan E Eichler</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Department of Genome Sciences and The Howard Hughes Medical Institute, University of Washington School of Medicine, 1705 NE Pacific St., Seattle, Washington 98195, USA.</institution>        <institution>NimbleGen Systems, Inc., Madison, Wisconsin 53711, USA.</institution>        <institution>Oxford Genetics Knowledge Park, The Wellcome Trust Centre for Human Genetics, Churchill Hospital, Oxford OX3 7BN, UK.</institution>        <institution>Department of Clinical Genetics, Oxford Radcliffe Hospitals National Health Service (NHS) Trust, Churchill Hospital, Oxford OX3 7LJ, UK.</institution>        <institution>Clinical and Molecular Genetics Unit, Institute of Child Health, University College London, London, UK.</institution>        <institution>Department of Pediatrics, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands.</institution>        <institution>Department of Human Genetics, University of Chicago, Chicago, Illinois 60637, USA.</institution>        <institution>Comprehensive Cancer Center, University of California San Francisco (UCSF), San Francisco, California 94143, USA.</institution>        <institution>Cancer Research Institute, UCSF, San Francisco, California 94143, USA</institution>    </publication>    <publication pub_id="23">        <status>On</status>        <application>CGH</application>        <title>Deletion at 14q22-23 indicates a contiguous gene syndrome comprising anophthalmia, pituitary hypoplasia, and ear anomalies</title>        <journal>Am. J. Med. Genet.</journal>        <issue>2006 Aug 15;140(16):1711-8</issue>        <pubdate>2006-08-15</pubdate>        <epubdate>2006-07-11</epubdate>        <url>http://dx.doi.org/10.1002/ajmg.a.31335</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>Anophthalmia and pituitary gland hypoplasia are both debilitating conditions where the underlying genetic defect is unknown in the majority of cases. We identified a patient with bilateral anophthalmia and absence of the optic nerves, chiasm and tracts, as well as pituitary gland hypoplasia and ear anomalies with a de novo apparently balanced chromosomal translocation, 46,XY,t(3;14)(q28;q23.2). Translocation breakpoint analysis using FISH and high-resolution microarray comparative genomic hybridization (CGH) has identified a 9.66 Mb deleted region on the long arm of chromosome 14 which includes the genes BMP4, OTX2, RTN1, SIX6, SIX1, and SIX4. Three other patients with interstitial deletions involving 14q22-23 have been described, all with bilateral anophthalmia, pituitary abnormalities, ear anomalies, and a facial phenotype similar to our patient. OTX2 is involved in ocular developmental defects, and the severity of the ocular phenotype in our patient and the other 14q22-23 deletion patients, suggests this genomic region harbors other gene/s involved in ocular development. BMP4 haploinsufficiency is predicted to contribute to the ocular phenotype on the basis of its expression pattern and observed murine mutant phenotypes. In addition, deletion of BMP4 and SIX6 is likely to contribute to the abnormal pituitary development, and SIX1 deletion may contribute to the ear and other craniofacial features. This indicates that contiguous gene deletion may contribute to the phenotypic features in the 14q22-23 deletion patients.</abstract>        <author>            <id>1</id>            <author_shortname>Nolen LD</author_shortname>            <author_fullname>Leisha D. Nolen</author_fullname>            <author_affiliation>1,7</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Amor D</author_shortname>            <author_fullname>David Amor</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Haywood A</author_shortname>            <author_fullname>Ashley Haywood</author_fullname>            <author_affiliation>1,3,4</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>St Heaps L</author_shortname>            <author_fullname>Luke St. Heaps</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Willcock C</author_shortname>            <author_fullname>Chris Willcock</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Mihelec M</author_shortname>            <author_fullname>Marija Mihelec</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Tam P</author_shortname>            <author_fullname>Patrick Tam</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Billson F</author_shortname>            <author_fullname>Frank Billson</author_fullname>            <author_affiliation>6</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Grigg J</author_shortname>            <author_fullname>John Grigg</author_fullname>            <author_affiliation>6</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Peters G</author_shortname>            <author_fullname>Greg Peters</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Jamieson RV</author_shortname>            <author_fullname>Robyn V. Jamieson</author_fullname>            <author_affiliation>1,8</author_affiliation>        </author>        <institution>Eye Genetics Research Group, Children's Medical Research Institute, The Children's Hospital at Westmead and Save Sight Institute, Sydney, New South Wales, Australia</institution>        <institution>Department of Genetic Health Services Victoria, Murdoch Children's Research Institute, Royal Children's Hospital, Melbourne, Victoria, Australia</institution>        <institution>Department of Cytogenetics, Western Sydney Genetics Program, The Children's Hospital at Westmead, New South Wales, Australia</institution>        <institution>Department of Biological Sciences, Macquarie University, Sydney, New South Wales, Australia</institution>        <institution>Embryology Unit, Children's Medical Research Institute, Westmead, New South Wales, Australia</institution>        <institution>Discipline of Ophthalmology and Save Sight Institute, Faculty of Medicine, University of Sydney, New South Wales, Australia</institution>        <institution>School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania</institution>        <institution>Discipline of Paediatrics and Child Health, Faculty of Medicine, University of Sydney, New South Wales, Australia</institution>    </publication>    <publication pub_id="24">        <status>On</status>        <application>CGH</application>        <title>Microdissection-based high-resolution genomic array analysis of two patients with cytogenetically identical interstitial deletions of chromosome 1q but distinct clinical phenotypes</title>        <journal>Am. J. Med. Genet.</journal>        <issue>2006 Jul 11;140(15):1637-1643</issue>        <pubdate>2006-07-11</pubdate>        <epubdate>2006-07-11</epubdate>        <url>http://dx.doi.org/10.1002/ajmg.a.31349</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>We describe two boys with cytogenetically identical interstitial deletions in the q42.11-q42.13 region of the long arm of chromosome 1 detected by high-resolution G-banding analysis. These children share some phenotypic features but also exhibit distinct morphologic differences. We further characterized the deletions using a new technical strategy - microdissection-based high-resolution genomic array (MHGA) analysis - to define the breakpoints, genomic sizes, and gene contents of the deletions. This showed that the patients had distinguishable deletions that were adjacent but did not overlap, thus explaining the observed phenotypic differences. These results were surprising because we expected at least some degree of overlap to explain the features that were shared. MHGA can quickly give precise and detailed information about any rearrangement in the genome using as little material as a single cell. This novel strategy provides unique advantages for both clinical diagnosis and genomic research.</abstract>        <author>            <id>1</id>            <author_shortname>Rice GM</author_shortname>            <author_fullname>G.M. Rice</author_fullname>            <author_affiliation>1,6</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Qi Z</author_shortname>            <author_fullname>Z. Qi</author_fullname>            <author_affiliation>2,5</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Selzer R</author_shortname>            <author_fullname>R. Selzer</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Richmond T</author_shortname>            <author_fullname>T. Richmond</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Thompson K</author_shortname>            <author_fullname>K. Thompson</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Pauli RM</author_shortname>            <author_fullname>R.M. Pauli</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Yu J</author_shortname>            <author_fullname>J. Yu</author_fullname>            <author_affiliation>2,4,5</author_affiliation>        </author>        <institution>Departments of Pediatrics and Medical Genetics, University of Wisconsin - Madison, Madison, Wisconsin</institution>        <institution>Wisconsin State Laboratory of Hygiene, University of Wisconsin - Madison, Madison, Wisconsin</institution>        <institution>NimbleGen Systems, Inc., Madison, Wisconsin</institution>        <institution>Department of Pathology and Laboratory Medicine, University of Wisconsin - Madison, Madison, Wisconsin</institution>        <institution>Department of Laboratory Medicine, University of California San Francisco, San Francisco, California</institution>        <institution>Waisman Center, University of Wisconsin - Madison, Madison, Wisconsin</institution>    </publication>    <publication pub_id="25">        <status>On</status>        <application>CGH</application>        <title>Comparative Oncogenomics Identifies NEDD9 as a Melanoma Metastasis Gene</title>        <journal>Cell</journal>        <issue>2006 Jun 30;125(7):1269-81.</issue>        <pubdate>2006-06-30</pubdate>        <epubdate>2006-06-30</epubdate>        <url>http://dx.doi.org/10.1016/j.cell.2006.06.008</url>        <url_pdf></url_pdf>        <url_supplemental>http://www.cell.com/cgi/content/full/125/7/1269/DC1/</url_supplemental>        <abstract>Genomes of human cancer cells are characterized by numerous chromosomal aberrations of uncertain pathogenetic significance. Here, in an inducible mouse model of melanoma, we characterized metastatic variants with an acquired focal chromosomal amplification that corresponds to a much larger amplification in human metastatic melanomas. Further analyses identified Nedd9, an adaptor protein related to p130CAS, as the only gene within the minimal common region that exhibited amplification-associated overexpression. A series of functional, biochemical, and clinical studies established NEDD9 as a bona fide melanoma metastasis gene. NEDD9 enhanced invasion in vitro and metastasis in vivo of both normal and transformed melanocytes, functionally interacted with focal adhesion kinase and modulated focal contact formation, and exhibited frequent robust overexpression in human metastatic melanoma relative to primary melanoma. Thus, comparative oncogenomics has enabled the identification and facilitated the validation of a highly relevant cancer gene governing metastatic potential in human melanoma.</abstract>        <author>            <id>1</id>            <author_shortname>Kim M</author_shortname>            <author_fullname>Minjung Kim</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Gans JD</author_shortname>            <author_fullname>Joseph D. Gans</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Nogueira C</author_shortname>            <author_fullname>Cristina Nogueira</author_fullname>            <author_affiliation>1,2</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Wang A</author_shortname>            <author_fullname>Audrey Wang</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Paik JH</author_shortname>            <author_fullname>Ji-Hye Paik</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Feng B</author_shortname>            <author_fullname>Bin Feng</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Brennan C</author_shortname>            <author_fullname>Cameron Brennan</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Hahn WC</author_shortname>            <author_fullname>William C. Hahn</author_fullname>            <author_affiliation>1,5</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Cordon-Cardo C</author_shortname>            <author_fullname>Carlos Cordon-Cardo</author_fullname>            <author_affiliation>6</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Wagner SN</author_shortname>            <author_fullname>Stephan N. Wagner</author_fullname>            <author_affiliation>7</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Flotte TJ</author_shortname>            <author_fullname>Thomas J. Flotte</author_fullname>            <author_affiliation>8</author_affiliation>        </author>        <author>            <id>12</id>            <author_shortname>Duncan LM</author_shortname>            <author_fullname>Lyn M. Duncan</author_fullname>            <author_affiliation>8</author_affiliation>        </author>        <author>            <id>13</id>            <author_shortname>Granter SR</author_shortname>            <author_fullname>Scott R. Granter</author_fullname>            <author_affiliation>9</author_affiliation>        </author>        <author>            <id>14</id>            <author_shortname>Chin L</author_shortname>            <author_fullname>Lynda Chin</author_fullname>            <author_affiliation>1,3,10</author_affiliation>        </author>        <institution>Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA</institution>        <institution>Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), Medical Faculty, University of Porto, Porto, Portugal</institution>        <institution>Center for Applied Cancer Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA</institution>        <institution>Department of Surgery, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10021, USA</institution>        <institution>Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA</institution>        <institution>Department of Pathology, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10021, USA</institution>        <institution>DIAID, Department of Dermatology, Medical University of Vienna and Center of Molecular Medicine, Austrian Academy of Sciences, Wahringer Gurtel 18-20, A-1090 Vienna, Austria</institution>        <institution>Dermatopathology Unit, Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA</institution>        <institution>Department of Pathology, Brigham and Women&#8217;s Hospital, Boston, MA 02115, USA.</institution>        <institution>Department of Dermatology, Harvard Medical School, Boston, MA 02115, USA</institution>    </publication>    <publication pub_id="26">        <status>On</status>        <application>CGH</application>        <title>Copy number variation: New insights in genome diversity</title>        <journal>Genome Res.</journal>        <issue>2006 Aug;16(8):949-61. Epub 2006 Jun 29.</issue>        <pubdate>2006-08-01</pubdate>        <epubdate>2006-06-29</epubdate>        <url>http://dx.doi.org/10.1101/gr.3677206</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>DNA copy number variation has long been associated with specific chromosomal rearrangements and genomic disorders, but its ubiquity in mammalian genomes was not fully realized until recently. Although our understanding of the extent of this variation is still developing, it seems likely that, at least in humans, copy number variants (CNVs) account for a substantial amount of genetic variation. Since many CNVs include genes that result in differential levels of gene expression, CNVs may account for a significant proportion of normal phenotypic variation. Current efforts are directed toward a more comprehensive cataloging and characterization of CNVs that will provide the basis for determining how genomic diversity impacts biological function, evolution, and common human diseases.</abstract>        <author>            <id>1</id>            <author_shortname>Freeman JL</author_shortname>            <author_fullname>Jennifer L. Freeman</author_fullname>            <author_affiliation>1,2</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Perry GH</author_shortname>            <author_fullname>George H. Perry</author_fullname>            <author_affiliation>1,3</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Feuk L</author_shortname>            <author_fullname>Lars Feuk</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Redon R</author_shortname>            <author_fullname>Richard Redon</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>McCarroll SA</author_shortname>            <author_fullname>Steven A. McCarroll</author_fullname>            <author_affiliation>6</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Altshuler DM</author_shortname>            <author_fullname>David M. Altshuler</author_fullname>            <author_affiliation>6</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Aburatani H</author_shortname>            <author_fullname>Hiroyuki Aburatani</author_fullname>            <author_affiliation>7</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Jones KW</author_shortname>            <author_fullname>Keith W. Jones</author_fullname>            <author_affiliation>8</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Tyler-Smith C</author_shortname>            <author_fullname>Chris Tyler-Smith</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Hurles ME</author_shortname>            <author_fullname>Matthew E. Hurles</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Carter NP</author_shortname>            <author_fullname>Nigel P. Carter</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>12</id>            <author_shortname>Scherer SW</author_shortname>            <author_fullname>Stephen W. Scherer</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>13</id>            <author_shortname>Lee C</author_shortname>            <author_fullname>Charles Lee</author_fullname>            <author_affiliation>1,2,9</author_affiliation>        </author>        <institution>Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA</institution>        <institution>Harvard Medical School, Boston, Massachusetts 02115, USA</institution>        <institution>School of Human Evolution and Social Change, Arizona State University, Tempe, Arizona 85287, USA</institution>        <institution>Department of Genetics and Genomic Biology, The Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada</institution>        <institution>The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, United Kingdom</institution>        <institution>Program in Medical and Population Genetics, Broad Institute of Harvard University and Massachusetts Institute of Technology, Cambridge, Massachusetts 02141, USA</institution>        <institution>Genome Science Division, University of Tokyo, Tokyo, 153-8904 Japan</institution>        <institution>Molecular Genetics Division, Affymetrix, Inc., Santa Clara, California 95051, USA</institution>        <institution>Corresponding author.</institution>    </publication>    <publication pub_id="27">        <status>On</status>        <application>CGH</application>        <title>Linkage Disequilibrium and Heritability of CNPs within Duplicated Regions of the Human Genome</title>        <journal>Am. J. Hum. Genet.</journal>        <issue>2006 Aug;79(2):275-90. Epub 2006 Jun 15</issue>        <pubdate>2006-08-01</pubdate>        <epubdate>2006-06-15</epubdate>        <url>http://dx.doi.org/10.1086/505653</url>        <url_pdf></url_pdf>        <url_supplemental>http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pubmed&#38;pubmedid=16826518#figures-tables-sec</url_supplemental>        <abstract>Studies of copy number variation and linkage disequilibrium have typically excluded complex regions of the genome that are rich in duplications which are prone to rearrangement. In an attempt to assess the heritability and linkage disequilibrium of copy number polymorphisms in duplication-rich regions of the genome, we profiled copy number variation in 130 putative rearrangement hotspot regions among 269 individuals of European, Yoruba, Chinese, and Japanese ancestry analyzed by the International HapMap Consortium. Eighty four hotspot regions, corresponding to 257 BAC probes, showed evidence of copy number differences. Despite a predisposing genetic architecture, polymorphism was never observed in the remaining 46 rearrangement hotspots, and we suggest these represent excellent candidates sites for pathogenic rearrangements. We used a combination of BAC-based and high-density customized oligonucleotide arrays to resolve the molecular basis of structural rearrangements. For common variants (&gt;10% frequency), we observed a distinct bias against copy number losses, suggesting that deletions are subject to purifying selection. Heritability estimates did not differ significantly from 1.0 among the majority (30/34) of loci analyzed, consistent with normal Mendelian inheritance. Some of the CNPs in duplication-rich regions showed strong linkage disequilibrium with nearby SNPs and were observed to segregate on ancestral SNP haplotypes. However, linkage disequilibrium with the best available SNP markers is weaker than has been reported for deletion polymorphisms in less-complex regions of the genome. These observations may be accounted for by a low density of SNP data in duplicated regions, challenges in mapping and typing the CNPs, and the possibility that CNPs in these regions have rearranged on multiple haplotype backgrounds. Our results underscore the need for complete maps of genetic variation in duplication-rich regions of the genome.</abstract>        <author>            <id>1</id>            <author_shortname>Locke DP</author_shortname>            <author_fullname>Devin P. Locke</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Sharp AJ</author_shortname>            <author_fullname>Andrew J. Sharp</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>McCarroll SA</author_shortname>            <author_fullname>Steven A. McCarroll</author_fullname>            <author_affiliation>2, 3</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>McGrath SD</author_shortname>            <author_fullname>Sean D. McGrath</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Newman TL</author_shortname>            <author_fullname>Tera L. Newman</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Cheng Z</author_shortname>            <author_fullname>Ze Cheng</author_fullname>            <author_affiliation>1,4</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Schwartz S</author_shortname>            <author_fullname>Stuart Schwartz</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Albertson DG</author_shortname>            <author_fullname>Donna G. Albertson</author_fullname>            <author_affiliation>6</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Pinkel D</author_shortname>            <author_fullname>Daniel Pinkel</author_fullname>            <author_affiliation>6</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Altshuler DM</author_shortname>            <author_fullname>David M. Altshuler</author_fullname>            <author_affiliation>2, 3, 7, 8</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Eichler EE</author_shortname>            <author_fullname>Evan E. Eichler</author_fullname>            <author_affiliation>1,4</author_affiliation>        </author>        <institution>Department of Genome Sciences, University of Washington School of Medicine.</institution>        <institution>Department of Molecular Biology and Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA</institution>        <institution>Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA</institution>        <institution>Howard Hughes Medical Institute 1705 NE Pacific St. Seattle, WA</institution>        <institution>Department of Genetics, University of Chicago, Chicago, IL</institution>        <institution>Comprehensive Cancer Center, UCSF, San Francisco, CA</institution>        <institution>Harvard Medical School, Boston, MA</institution>        <institution>Department of Medicine, Massachusetts General Hospital, Boston, MA</institution>    </publication>    <publication pub_id="28">        <status>On</status>        <application>CGH</application>        <title>Complex genomic alterations and gene expression in acute lymphoblastic leukemia with intrachromosomal amplification of chromosome 21</title>        <journal>PNAS</journal>        <issue>2006 May 23;103(21):8167-72. Epub 2006 May 15.</issue>        <pubdate>2006-03-21</pubdate>        <epubdate>2006-03-14</epubdate>        <url>http://dx.doi.org/10.1073/pnas.0602360103</url>        <url_pdf>http://www.pnas.org/cgi/reprint/103/12/4534</url_pdf>        <url_supplemental>http://www.pnas.org/cgi/content/full/0511340103/DC1</url_supplemental>        <abstract>Deletions and amplifications of the human genomic sequence (copy number polymorphisms) are the cause of numerous diseases and a potential cause of phenotypic variation in the normal population. Comparative genomic hybridization (CGH) has been developed as a useful tool for detecting alterations in DNA copy number that involve blocks of DNA several kilobases or larger in size. We have developed high-resolution CGH (HR-CGH) to detect accurately and with relatively little bias the presence and extent of chromosomal aberrations in human DNA. Maskless array synthesis was used to construct arrays containing 385,000 oligonucleotides with isothermal probes of 45-85 bp in length; arrays tiling the beta-globin locus and chromosome 22q were prepared. Arrays with a 9-bp tiling path were used to map a 622-bp heterozygous deletion in the beta-globin locus. Arrays with an 85-bp tiling path were used to analyze DNA from patients with copy number changes in the pericentromeric region of chromosome 22q. Heterozygous deletions and duplications as well as partial triploidies and partial tetraploidies of portions of chromosome 22q were mapped with high resolution (typically up to 200 bp) in each patient, and the precise breakpoints of two deletions were confirmed by DNA sequencing. Additional peaks potentially corresponding to known and novel additional CNPs were also observed. Our results demonstrate that HR-CGH allows the detection of copy number changes in the human genome at an unprecedented level of resolution.</abstract>        <author>            <id>1</id>            <author_shortname>Strefford JC</author_shortname>            <author_fullname>Jon C. Strefford</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>van Delft FW</author_shortname>            <author_fullname>Frederik W. van Delft</author_fullname>            <author_affiliation>2,3</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Robinson HM</author_shortname>            <author_fullname>Hazel M. Robinson</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Worley H</author_shortname>            <author_fullname>Helen Worley</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Yiannikouris O</author_shortname>            <author_fullname>Olga Yiannikouris</author_fullname>            <author_affiliation>2,3</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Selzer R</author_shortname>            <author_fullname>Rebecca Selzer</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Richmond T</author_shortname>            <author_fullname>Todd Richmond</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Hann I</author_shortname>            <author_fullname>Ian Hann</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Bellotti T</author_shortname>            <author_fullname>Tony Bellotti</author_fullname>            <author_affiliation>6</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Raghavan M</author_shortname>            <author_fullname>Manoj Raghavan</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Young BD</author_shortname>            <author_fullname>Bryan D. Young</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>12</id>            <author_shortname>Saha V</author_shortname>            <author_fullname>Vaskar Saha</author_fullname>            <author_affiliation>2,3</author_affiliation>        </author>        <author>            <id>13</id>            <author_shortname>Harrison CJ</author_shortname>            <author_fullname>Christine J. Harrison</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Leukaemia Research Cytogenetics Group, Cancer Sciences Division, University of Southampton, Southampton United Kingdom;</institution>        <institution>Cancer Research UK Children's Cancer Group Institute of Cancer, Queen Mary University of London, London, United Kingdom</institution>        <institution>Medical Oncology Unit, Institute of Cancer, Queen Mary University of London, London, United Kingdom</institution>        <institution>NimbleGen Systems, Inc., Madison, WI</institution>        <institution>Department of Haematology, Great Ormond Street Hospital for Children NHS Trust, London United Kingdom</institution>        <institution>Computer Learning Research Centre, Royal Holloway, University of London, Egham, Surrey, United Kingdom</institution>    </publication>    <publication pub_id="29">        <status>On</status>        <application>CGH</application>        <title>High-resolution analysis of chromosomal breakpoints and genomic instability identifes PTPRD as a candidate tumor suppressor gene in neuroblastoma</title>        <journal>Cancer Res.</journal>        <issue>2006 Apr 1;66(7):3673-80.</issue>        <pubdate>2006-04-01</pubdate>        <epubdate>2006-04-01</epubdate>        <url>http://dx.doi.org/10.1158/0008-5472.CAN-05-4154</url>        <url_pdf></url_pdf>        <url_supplemental>http://cancerres.aacrjournals.org/cgi/content/full/66/7/3673/DC1</url_supplemental>        <abstract>Although neuroblastoma is characterized by numerous recurrent, large-scale chromosomal imbalances, the genes targeted by such imbalances have remained elusive. We have applied whole-genome oligonucleotide array comparative genomic hybridization (median probe spacing 6 kb) to 56 neuroblastoma tumors and cell lines to identify genes involved with disease pathogenesis. This set of tumors was selected for having either 11q loss or MYCN amplification, abnormalities that define the two most common genetic subtypes of metastatic neuroblastoma. Our analyses have permitted us to map large-scale chromosomal imbalances and high-level amplifications at exon-level resolution and to identify novel microdeletions and duplications. Chromosomal breakpoints (n = 467) generating imbalances &gt;2 Mb were mapped to intervals ranging between 6 and 50 kb in size, providing substantial information on each abnormality. For example, breakpoints leading to large-scale hemizygous loss of chromosome 11q were highly clustered and preferentially associated with segmental duplications. High-level amplifications of MYCN were extremely complex, often resulting in a series of discontinuous regions of amplification. Imbalances (n = 540) &lt;2 Mb long were also detected. Although the majority (78%) of these imbalances mapped to segmentally duplicated regions and primarily reflect constitutional copy number polymorphisms, many subtle imbalances were detected that are likely somatically acquired alterations and include genes involved with tumorigenesis, apoptosis, or neural cell differentiation. The most frequent microdeletion involved the PTPRD locus, indicating a possible tumor suppressor function for this gene. (Cancer Res 2006; 66(7): 3673-80)</abstract>        <author>            <id>1</id>            <author_shortname>Stallings RL</author_shortname>            <author_fullname>Stallings RL</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Nair P</author_shortname>            <author_fullname>Nair P</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Maris JM</author_shortname>            <author_fullname>Maris JM</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Catchpoole D</author_shortname>            <author_fullname>Catchpoole D</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>McDermott M</author_shortname>            <author_fullname>McDermott M</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>O'Meara A</author_shortname>            <author_fullname>O'Meara A</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Breatnach F</author_shortname>            <author_fullname>Breatnach F</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Children's Cancer Research Institute and Department of Pediatrics, University of Texas Health Science Center San Antonio, San Antonio, TX</institution>    </publication>    <publication pub_id="30">        <status>On</status>        <application>CGH</application>        <title>High-resolution mapping of DNA copy alterations in human chromosome 22 using high-density tiling oligonucleotide arrays</title>        <journal>PNAS</journal>        <issue>2006 Mar 21;103(12):4534-9. Epub 2006 Mar 14.</issue>        <pubdate>2006-03-21</pubdate>        <epubdate>2006-03-14</epubdate>        <url>http://dx.doi.org/10.1073/pnas.0511340103</url>        <url_pdf>http://www.pnas.org/cgi/reprint/103/12/4534</url_pdf>        <url_supplemental>http://www.pnas.org/cgi/content/full/0511340103/DC1</url_supplemental>        <abstract>Deletions and amplifications of the human genomic sequence (copy number polymorphisms) are the cause of numerous diseases and a potential cause of phenotypic variation in the normal population. Comparative genomic hybridization (CGH) has been developed as a useful tool for detecting alterations in DNA copy number that involve blocks of DNA several kilobases or larger in size. We have developed high-resolution CGH (HR-CGH) to detect accurately and with relatively little bias the presence and extent of chromosomal aberrations in human DNA. Maskless array synthesis was used to construct arrays containing 385,000 oligonucleotides with isothermal probes of 45&#226;&#8364;&#8220;85 bp in length; arrays tiling the beta-globin locus and chromosome 22q were prepared. Arrays with a 9-bp tiling path were used to map a 622-bp heterozygous deletion in the beta-globin locus. Arrays with an 85-bp tiling path were used to analyze DNA from patients with copy number changes in the pericentromeric region of chromosome 22q. Heterozygous deletions and duplications as well as partial triploidies and partial tetraploidies of portions of chromosome 22q were mapped with high resolution (typically up to 200 bp) in each patient, and the precise breakpoints of two deletions were confirmed by DNA sequencing. Additional peaks potentially corresponding to known and novel additional CNPs were also observed. Our results demonstrate that HR-CGH allows the detection of copy number changes in the human genome at an unprecedented level of resolution.</abstract>        <author>            <id>1</id>            <author_shortname>Urban AE</author_shortname>            <author_fullname>Alexander Eckehart Urban</author_fullname>            <author_affiliation>1,2,8</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Korbel JO</author_shortname>            <author_fullname>Jan O. Korbel</author_fullname>            <author_affiliation>3,8</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Selzer R</author_shortname>            <author_fullname>Rebecca Selzer</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Richmond T</author_shortname>            <author_fullname>Todd Richmond</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Hacker A</author_shortname>            <author_fullname>April Hacker</author_fullname>            <author_affiliation>7</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Popescu GV</author_shortname>            <author_fullname>George V. Popescu</author_fullname>            <author_affiliation>1,2</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Cubells JF</author_shortname>            <author_fullname>Joseph F. Cubells</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Green R</author_shortname>            <author_fullname>Roland Green</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Emanuel BS</author_shortname>            <author_fullname>Beverly S. Emanuel</author_fullname>            <author_affiliation>6</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Gerstein MB</author_shortname>            <author_fullname>Mark B. Gerstein</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Weissman SM</author_shortname>            <author_fullname>Sherman M. Weissman</author_fullname>            <author_affiliation>2,6</author_affiliation>        </author>        <author>            <id>12</id>            <author_shortname>Snyder M</author_shortname>            <author_fullname>Michael Snyder</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT 06520</institution>        <institution>Department of Genetics</institution>        <institution>and Molecular Biophysics and Biochemistry Department, Yale University School of Medicine, New Haven, CT 06520</institution>        <institution>NimbleGen Systems, Inc., 1 Science Court, Madison, WI 53711</institution>        <institution>Departments of Human Genetics, and Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA 30322</institution>        <institution>Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia, PA 19104</institution>        <institution>The Children&#8217;s Hospital of Philadelphia, Philadelphia, PA 19104</institution>        <institution>A.E.U. and J.O.K. contributed equally to this work.</institution>    </publication>    <publication pub_id="31">        <status>On</status>        <application>CGH</application>        <title>A High-Resolution Survey of Deletion Polymorphism in the Human Genome</title>        <journal>Nat. Genet.</journal>        <issue>2006 Jan;38(1):75-81. Epub 2005 Dec 4</issue>        <pubdate>2006-01-01</pubdate>        <epubdate>2005-12-04</epubdate>        <url>http://dx.doi.org/10.1038/ng1697</url>        <url_pdf>http://www.nature.com/ng/journal/v38/n1/pdf/ng1697.pdf</url_pdf>        <url_supplemental></url_supplemental>        <abstract>Recent work has shown that copy number polymorphism is an important class of genetic variation in human genomes. Here we report a new method that uses SNP genotype data from parent-offspring trios to identify polymorphic deletions. We applied this method to data from the International HapMap Project to produce the first high-resolution population surveys of deletion polymorphism. Approximately 100 of these deletions have been experimentally validated using comparative genome hybridization on tiling-resolution oligonucleotide microarrays. Our analysis identifies a total of 586 distinct regions that harbor deletion polymorphisms in one or more of the families. Notably, we estimate that typical individuals are hemizygous for roughly 30-50 deletions larger than 5 kb, totaling around 550-750 kb of euchromatic sequence across their genomes. The detected deletions span a total of 267 known and predicted genes. Overall, however, the deleted regions are relatively gene-poor, consistent with the action of purifying selection against deletions. Deletion polymorphisms may well have an important role in the genetics of complex traits; however, they are not directly observed in most current gene mapping studies. Our new method will permit the identification of deletion polymorphisms in high-density SNP surveys of trio or other family data.</abstract>        <author>            <id>1</id>            <author_shortname>Conrad DF</author_shortname>            <author_fullname>Donald F Conrad</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Andrews TD</author_shortname>            <author_fullname>T Daniel Andrews</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Carter NP</author_shortname>            <author_fullname>Nigel P Carter</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Hurles ME</author_shortname>            <author_fullname>Matthew E Hurles</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Pritchard JK</author_shortname>            <author_fullname>Jonathan K Pritchard</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Department of Human Genetics, The University of Chicago</institution>        <institution>Genome Dynamics and Evolution Group, The Wellcome Trust, Sanger Institute</institution>    </publication>    <publication pub_id="32">        <status>On</status>        <application>CGH</application>        <title>Analysis of chromosome breakpoints in neuroblastoma at sub-kilobase resolution using fine-tiling oligonucleotide array CGH</title>        <journal>Genes Chromosomes Cancer</journal>        <issue>2005 Nov;44(3):305-19</issue>        <pubdate>2005-11-01</pubdate>        <epubdate>2005-11-01</epubdate>        <url>http://dx.doi.org/10.1002/gcc.20243</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>Understanding the genes and genetic pathways targeted by recurrent chromosomal imbalances in malignancy, along with the molecular mechanisms that generate the imbalances, are important problems in cancer biology. In this report, we demonstrate that oligonucleotide array CGH (oaCGH) analysis can routinely map chromosomal imbalance breakpoints at exon-level resolution, including imbalances that are single copy number genomic alterations. Different tiling-path array designs were used in this study: a whole-genome array with a 6-kb median probe spacing and fine-tiling arrays for selected genomic regions with either 50- or 140-bp median probe spacing. In both array formats, oligonucleotide probes were of isothermal design and were tiled through genic and inter-genic regions. Whole-genome oaCGH analysis of two neuroblastoma cell lines and three primary tumors led to the identification of 58 chromosomal breakpoints that generated 45 large-scale partial chromosomal imbalances (&gt; 2 Mb). An unexpectedly high proportion (34%) of these breakpoint intervals mapped to regions containing segmental duplications. In addition, 88 smaller-sized regions (&lt; 2 Mb) of imbalance were detected, the majority of which mapped to segmentally duplicated regions and may reflect constitutional copy number polymorphisms. The chromosomal breakpoints for 12 recurrent abnormalities exhibited in neuroblastoma tumors and cell lines, including MYCN amplicon boundaries, loss of 3p, loss of 11q, and gain of 17q, could be mapped to intervals ranging from 50 bp to 10 kb in size using high-density fine-tiling oligonucleotide microarrays. Fine-tiling oaCGH analysis provides an unprecedented level of resolution, allowing detailed mapping of recurrent unbalanced chromosomal abnormalities. Supplementary material for this article can be found on the Genes, Chromosomes, and Cancer website at http://www.interscience.wiley.com/jpages/1045-2257/suppmat/index.html</abstract>        <author>            <id>1</id>            <author_shortname>Selzer RR</author_shortname>            <author_fullname>Rebecca R. Selzer</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Richmond TA</author_shortname>            <author_fullname>Todd A. Richmond</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Pofahl NJ</author_shortname>            <author_fullname>Nathan J. Pofahl</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Green RD</author_shortname>            <author_fullname>Roland D. Green</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Eis PS</author_shortname>            <author_fullname>Peggy S. Eis</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Nair P</author_shortname>            <author_fullname>Prakash Nair</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Brothman AR</author_shortname>            <author_fullname>Arthur R. Brothman</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Stallings RL</author_shortname>            <author_fullname>Raymond L. Stallings</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <institution>NimbleGen Systems Inc., Madison, WI 53711</institution>        <institution>Department of Pediatrics and Human Genetics, University of Utah School of Medicine, Salt Lake City, UT</institution>        <institution>Children's Cancer Research Institute and Department of Pediatrics, University of Texas Health Science Center San Antonio, San Antonio, TX</institution>    </publication>    <publication pub_id="33">        <status>On</status>        <application>CGS</application>        <title>Genetic changes that correlate with reduced susceptibility to daptomycin in Staphylococcus aureus</title>        <journal>Antimicrob. Agents Chemother.</journal>        <issue>2006 Jun;50(6):2137-45</issue>        <pubdate>2006-06-01</pubdate>        <epubdate>2006-06-01</epubdate>        <url>http://dx.doi.org/10.1128/AAC.00039-06</url>        <url_pdf>http://www.pubmedcentral.nih.gov/picrender.fcgi?artid=1479123&amp;blobtype=pdf</url_pdf>        <url_supplemental></url_supplemental>        <abstract>Daptomycin is a lipopeptide antibiotic with potent activity against gram-positive bacteria. Complete-genome comparisons of laboratory-derived Staphylococcus aureus with decreased susceptibility to daptomycin and their susceptible parent were used to identify genes that contribute to reduced susceptibility to daptomycin. Selective pressure of growth in sublethal concentrations of daptomycin resulted in the accumulation of mutations over time correlating with incremental decreases in susceptibility. Single point mutations resulting in amino acid substitutions occurred in three distinct proteins: MprF, a lysylphosphatidylglycerol synthetase; YycG, a histidine kinase; and RpoB and RpoC, the &#195;&#8240;&#194;&#191; and &#195;&#8240;&#194;&#191;&#195;&#8230;&#195;&#165; subunits of RNA polymerase. Sequence analysis of mprF, yycF, yycG, rpoB, and rpoC in clinical isolates that showed treatment-emergent increases in daptomycin MICs revealed point mutations in mprF and a nucleotide insertion in yycG, suggesting a role for these genes in decreased susceptibility to daptomycin in the hospital setting.</abstract>        <author>            <id>1</id>            <author_shortname>Friedman L</author_shortname>            <author_fullname>Friedman L</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Alder JD</author_shortname>            <author_fullname>Alder JD</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Silverman JA</author_shortname>            <author_fullname>Silverman JA</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Cubist Pharmaceuticals, Inc., Lexington, MA</institution>    </publication>    <publication pub_id="34">        <status>On</status>        <application>CGS</application>        <title>Molecular genetic anatomy of inter- and intraserotype variation in the human bacterial pathogen group A Streptococcus</title>        <journal>PNAS</journal>        <issue>2006 May 2;103(18):7059-64. Epub 2006 Apr 24.</issue>        <pubdate>2006-05-02</pubdate>        <epubdate>2006-04-24</epubdate>        <url>http://dx.doi.org/10.1073/pnas.0510279103</url>        <url_pdf>http://www.pnas.org/cgi/reprint/103/18/7059</url_pdf>        <url_supplemental>http://www.pnas.org/cgi/content/full/0510279103/DC1</url_supplemental>        <abstract>In recent years we have studied the relationship between strain genotypes and patient phenotypes in group A Streptococcus (GAS), a model human bacterial pathogen that causes extensive morbidity and mortality worldwide. We have concentrated our efforts on serotype M3 organisms because these strains are common causes of pharyngeal and invasive infections, produce unusually severe invasive infections, and can exhibit epidemic behavior. Our studies have been hindered by the lack of genome-scale phylogenies of multiple GAS strains and whole-genome sequences of multiple serotype M3 strains recovered from individuals with defined clinical phenotypes. To remove some of these impediments, we sequenced to closure the genome of four additional GAS strains and conducted comparative genomic resequencing of 12 contemporary serotype M3 strains representing distinct genotypes and phenotypes. Serotype M3 strains are a single phylogenetic lineage. Strains from asymptomatic throat carriers were significantly less virulent for mice than sterile-site isolates and evolved to a less virulent phenotype by multiple genetic pathways. Strain persistence or extinction between epidemics was strongly associated with presence or absence, respectively, of the prophage encoding streptococcal pyrogenic exotoxin A. A serotype M3 clone significantly underrepresented among necrotizing fasciitis cases has a unique frameshift mutation that truncates MtsR, a transcriptional regulator controlling expression of genes encoding iron-acquisition proteins. Expression microarray analysis of this clone confirmed significant alteration in expression of genes encoding iron metabolism proteins. Our analysis provided unprecedented detail about the molecular anatomy of bacterial strain genotype-patient phenotype relationships.</abstract>        <author>            <id>1</id>            <author_shortname>Beres SB</author_shortname>            <author_fullname>Beres SB</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Richter EW</author_shortname>            <author_fullname>Richter EW</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Nagiec MJ</author_shortname>            <author_fullname>Nagiec MJ</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Sumby P</author_shortname>            <author_fullname>Sumby P</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Porcella SF</author_shortname>            <author_fullname>Porcella SF</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Deleo FR</author_shortname>            <author_fullname>Deleo FR</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Musser JM</author_shortname>            <author_fullname>Musser JM</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Center for Molecular and Translational Human Infectious Diseases Research, The Methodist Hospital Research Institute, Houston, TX</institution>    </publication>    <publication pub_id="35">        <status>On</status>        <application>CGS</application>        <title>Probing genomic diversity and evolution of Escherichia coli O157  single-nucleotide polymorphisms</title>        <journal>Genome Res.</journal>        <issue>2006 Jun;16(6):757-67. Epub 2006 Apr 10.</issue>        <pubdate>2006-06-01</pubdate>        <epubdate>2006-04-10</epubdate>        <url>http://dx.doi.org/10.1101/gr.4759706</url>        <url_pdf>http://www.genome.org/cgi/reprint/16/6/757</url_pdf>        <url_supplemental>http://www.genome.org/cgi/content/full/gr.4759706/DC1</url_supplemental>        <abstract>Infections by Shiga toxin-producing Escherichia coli O157:H7 (STEC O157) are the predominant cause of bloody diarrhea and hemolytic uremic syndrome in the United States. In silico comparison of the two complete STEC O157 genomes (Sakai and EDL933) revealed a strikingly high level of sequence identity in orthologous protein-coding genes, limiting the use of nucleotide sequences to study the evolution and epidemiology of this bacterial pathogen. To systematically examine single nucleotide polymorphisms (SNPs) at a genome scale, we designed comparative genome sequencing microarrays and analyzed 1199 chromosomal genes (a total of 1,167,948 bp) and 92,721 bp of the large virulence plasmid (pO157) of eleven outbreak-associated STEC O157 strains. We discovered 906 SNPs in 523 chromosomal genes and observed a high level of DNA polymorphisms among the pO157 plasmids. Based on a uniform rate of synonymous substitution for Escherichia coli and Salmonella enterica (4.7 x 10&#226;&#8364;&#8220;9 per site per year), we estimate that the most recent common ancestor of the contemporary beta-glucuronidase-negative, non-sorbitolfermenting STEC O157 strains existed ca. 40 thousand years ago. The phylogeny of the STEC O157 strains based on the informative synonymous SNPs was compared to the maximum parsimony trees inferred from pulsed-field gel electrophoresis and multilocus variable numbers of tandem repeats analysis. The topological discrepancies indicate that, in contrast to the synonymous mutations, parts of STEC O157 genomes have evolved through different mechanisms with highly variable divergence rates. The SNP loci reported here will provide useful genetic markers for developing high-throughput methods for fine-resolution genotyping of STEC O157. Functional characterization of nucleotide polymorphisms should shed new insights on the evolution, epidemiology, and pathogenesis of STEC O157 and related pathogens.</abstract>        <author>            <id>1</id>            <author_shortname>Zhang W</author_shortname>            <author_fullname>Wei Zhang</author_fullname>            <author_affiliation>1,5</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Qi W</author_shortname>            <author_fullname>Weihong Qi</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Albert TJ</author_shortname>            <author_fullname>Thomas J. Albert</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Motiwala AS</author_shortname>            <author_fullname>Alifiya S. Motiwala</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Alland D</author_shortname>            <author_fullname>David Alland</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Hyytia-Trees EK</author_shortname>            <author_fullname>Eija K. Hyytia-Trees</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Ribot EM</author_shortname>            <author_fullname>Efrain M. Ribot</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Fields PI</author_shortname>            <author_fullname>Patricia I. Fields</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Whittam TS</author_shortname>            <author_fullname>Thomas S. Whittam</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Swaminathan B</author_shortname>            <author_fullname>Bala Swaminathan</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Foodborne and Diarrheal Diseases Branch, National Center for Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia</institution>        <institution>Microbial Evolution Laboratory, National Food Safety and Toxicology Center, Michigan State University, East Lansing, Michigan</institution>        <institution>NimbleGen Systems Inc., Madison, Wisconsin</institution>        <institution>Division of Infectious Diseases, New Jersey Medical School, University of Medicine and Dentistry of New Jersey, Newark, New Jersey</institution>        <institution>Present address: National Center for Food Safety and Technology, Illinois Institute of Technology, Summit, IL</institution>    </publication>    <publication pub_id="36">        <status>On</status>        <application>CGS</application>        <title>Genome-Wide Analysis of Group A Streptococci Reveals a Mutation That Modulates Global Phenotype and Disease Specificity</title>        <journal>PLoS Pathog.</journal>        <issue>2006 Jan;2(1):e5. Epub 2006 Jan 27</issue>        <pubdate>2006-01-27</pubdate>        <epubdate>2006-01-27</epubdate>        <url>http://dx.doi.org/10.1371/journal.ppat.0020005</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>Many human pathogens produce phenotypic variants as a means to circumvent the host immune system and enhance survival and, as a potential consequence, exhibit increased virulence. For example, it has been known for almost 90 y that clinical isolates of the human bacterial pathogen group A streptococci (GAS) have extensive phenotypic heterogeneity linked to variation in virulence. However, the complete underlying molecular mechanism(s) have not been defined. Expression microarray analysis of nine clinical isolates identified two fundamentally different transcriptomes, designated pharyngeal transcriptome profile (PTP) and invasive transcriptome profile (ITP). PTP and ITP GAS differed in approximately 10% of the transcriptome, including at least 23 proven or putative virulence factor genes. ITP organisms were recovered from skin lesions of mice infected subcutaneously with PTP GAS and were significantly more able to survive phagocytosis and killing by human polymorphonuclear leukocytes. Complete genome resequencing of a mouse-derived ITP GAS revealed that the organism differed from its precursor by only a 7-bp frameshift mutation in the gene (covS) encoding the sensor kinase component of a two-component signal transduction system implicated in virulence. Genetic complementation, and sequence analysis of covR/S in 42 GAS isolates confirmed the central role of covR/S in transcriptome, exoproteome, and virulence modulation. Genome-wide analysis provides a heretofore unattained understanding of phenotypic variation and disease specificity in microbial pathogens, resulting in new avenues for vaccine and therapeutics research.</abstract>        <author>            <id>1</id>            <author_shortname>Sumby P</author_shortname>            <author_fullname>Paul Sumby</author_fullname>            <author_affiliation>1,2</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Whitney AR</author_shortname>            <author_fullname>Adeline R Whitney</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Graviss EA</author_shortname>            <author_fullname>Edward A Graviss</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>DeLeo FR</author_shortname>            <author_fullname>Frank R DeLeo</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Musser JM</author_shortname>            <author_fullname>James M Musser</author_fullname>            <author_affiliation>1,2,4</author_affiliation>        </author>        <institution>Center for Molecular and Translational Human Infectious Diseases Research, The Methodist Hospital Research Institute, Houston, Texas, United States of America</institution>        <institution>Center for Human Bacterial Pathogenesis Research, Department of Pathology, Baylor College of Medicine, Houston, Texas, United States of America</institution>        <institution>Laboratory of Human Bacterial Pathogenesis, Rocky Mountain Laboratories, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, Montana, United States of America</institution>        <institution>To whom correspondence should be addressed. E-mail: jmmusser@tmh.tm</institution>    </publication>    <publication pub_id="37">        <status>On</status>        <application>CGS</application>        <title>Identification of a nitroimidazo-oxazine-specific protein involved in PA-824 resistance in Mycobacterium tuberculosis</title>        <journal>PNAS</journal>        <issue>2006 Jan 10;103(2):431-6. Epub 2005 Dec 30</issue>        <pubdate>2006-01-10</pubdate>        <epubdate>2005-12-30</epubdate>        <url>http://dx.doi.org/10.1073/pnas.0508392103</url>        <url_pdf>http://www.pnas.org/cgi/reprint/103/2/431</url_pdf>        <url_supplemental>http://www.pnas.org/cgi/content/full/0508392103/DC1</url_supplemental>        <abstract>PA-824 is a promising new compound for the treatment of tuberculosis that is currently undergoing human trials. Like its progenitors metronidazole and CGI-17341, PA-824 is a prodrug of the nitroimidazole class, requiring bioreductive activation of an aromatic nitro group to exert an antitubercular effect. We have confirmed that resistance to PA-824 (a nitroimidazo-oxazine) and CGI-17341 (a nitroimidazo-oxazole) is most commonly mediated by loss of a specific glucose-6-phosphate dehydrogenase (FGD1) or its deazaflavin cofactor F420, which together provide electrons for the reductive activation of this class of molecules. Although FGD1 and F420 are necessary for sensitivity to these compounds, they are not sufficient and require additional accessory proteins that directly interact with the nitroimidazole. To understand more proximal events in the reductive activation of PA-824, we examined mutants that were wild-type for both FGD1 and F420 and found that, although these mutants had acquired high-level resistance to PA-824 (and another nitroimidazo-oxazine), they retained sensitivity to CGI-17341 (and a related nitroimidazo-oxazole). Microarray-based comparative genome sequencing of these mutants identified lesions in Rv3547, a conserved hypothetical protein with no known function. Complementation with intact Rv3547 fully restored sensitivity to nitroimidazo-oxazines and restored the ability of Mtb to metabolize PA-824. These results suggest that the sensitivity of Mtb to PA-824 and related compounds is mediated by a protein that is highly specific for subtle structural variations in these bicyclic nitroimidazoles.</abstract>        <author>            <id>1</id>            <author_shortname>Manjunatha UH</author_shortname>            <author_fullname>Manjunatha UH</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Boshoff H</author_shortname>            <author_fullname>Boshoff H</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Dowd CS</author_shortname>            <author_fullname>Dowd CS</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Zhang L</author_shortname>            <author_fullname>Zhang L</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Albert TJ</author_shortname>            <author_fullname>Albert TJ</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Norton JE</author_shortname>            <author_fullname>Norton JE</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Daniels L</author_shortname>            <author_fullname>Daniels L</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Dick T</author_shortname>            <author_fullname>Dick T</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Pang SS</author_shortname>            <author_fullname>Pang SS</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Barry CE 3rd</author_shortname>            <author_fullname>Barry CE 3rd</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Tuberculosis Research Section, NIAID, Rockville, MD</institution>        <institution>NimbleGen Systems, Inc., Madison, WI</institution>    </publication>    <publication pub_id="38">        <status>On</status>        <application>CGS</application>        <title>Mutation Discovery in Bacterial Genomes: Metronidazole Resistance in Helicobacter pylori</title>        <journal>Nature Methods</journal>        <issue>2005 Dec;2(12):951-3. Epub 2005 Nov 18.</issue>        <pubdate>2005-12-01</pubdate>        <epubdate>2005-11-18</epubdate>        <url>http://dx.doi.org/10.1038/nmeth805</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>We developed a microarray hybridization&#226;&#8364;&#8220;based method, 'comparative genome sequencing' (CGS), to find mutations in bacterial genomes and used it to study metronidazole resistance in H. pylori. CGS identified mutations in several genes, most likely affecting metronidazole activation, and produced no false positives in analysis of three megabases. We conclude that CGS identifies mutations in bacterial genomes efficiently, should enrich understanding of systems biology and genome evolution, and help track pathogens during outbreaks.</abstract>        <author>            <id>1</id>            <author_shortname>Albert TJ</author_shortname>            <author_fullname>Albert TJ</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Dailidiene D</author_shortname>            <author_fullname>Dailidiene D</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Dailide G</author_shortname>            <author_fullname>Dailide G</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Norton JE</author_shortname>            <author_fullname>Norton JE</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Kalia A</author_shortname>            <author_fullname>Kalia A</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Richmond TA</author_shortname>            <author_fullname>Richmond TA</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Molla M</author_shortname>            <author_fullname>Molla M</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Singh J</author_shortname>            <author_fullname>Singh J</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Green RD</author_shortname>            <author_fullname>Green RD</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Berg DE</author_shortname>            <author_fullname>Berg DE</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <institution>NimbleGen Systems, Inc. Madison, WI</institution>        <institution>Departements of Molecular Microbiology, Genetics and Medicine, Washington University Medical School, St. Louis, MO</institution>        <institution>Department of Biology and Center for Genetics and Moluecular Medicine, University of Lousiville, Louisville, KY</institution>    </publication>    <publication pub_id="39">        <status>On</status>        <application>Expression</application>        <title>Insights into social insects from the genome of the honeybee Apis mellifera</title>        <journal>Nature</journal>        <issue>2006 October 26, 443: 931-949.</issue>        <pubdate>2006-10-26</pubdate>        <epubdate>2006-10-26</epubdate>        <url>http://dx.doi.org/10.1038/nature05260</url>        <url_pdf>http://www.nature.com/nature/journal/v443/n7114/pdf/nature05260.pdf</url_pdf>        <url_supplemental>http://www.nature.com/nature/journal/v443/n7114/suppinfo/nature05260.html</url_supplemental>        <abstract>Here we report the genome sequence of the honeybee Apis mellifera, a key model for social behaviour and essential to global ecology through pollination. Compared with other sequenced insect genomes, the A. mellifera genome has high A+T and CpG contents, lacks major transposon families, evolves more slowly, and is more similar to vertebrates for circadian rhythm, RNA interference and DNA methylation genes, among others. Furthermore, A. mellifera has fewer genes for innate immunity, detoxification enzymes, cuticle-forming proteins and gustatory receptors, more genes for odorant receptors, and novel genes for nectar and pollen utilization, consistent with its ecology and social organization. Compared to Drosophila, genes in early developmental pathways differ in Apis, whereas similarities exist for functions that differ markedly, such as sex determination, brain function and behaviour. Population genetics suggests a novel African origin for the species A. mellifera and insights into whether Africanized bees spread throughout the New World via hybridization or displacement.</abstract>        <author>            <id>1</id>            <author_shortname>The Honeybee Genome Sequencing Consortium</author_shortname>            <author_fullname>The Honeybee Genome Sequencing Consortium</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>The Honeybee Genome Sequencing Consortium</institution>    </publication>    <publication pub_id="40">        <status>On</status>        <application>Expression</application>        <title>Identification of estrogen-responsive genes in the parenchyma and fat pad of the bovine mammary gland by microarray analysis</title>        <journal>Physiol. Genomics</journal>        <issue>2006 Oct 3;27(1):42-53. Epub 2006 Jun 20</issue>        <pubdate>2006-10-03</pubdate>        <epubdate>2006-06-20</epubdate>        <url>http://dx.doi.org/10.1152/physiolgenomics.00032.2006</url>        <url_pdf>http://physiolgenomics.physiology.org/cgi/reprint/27/1/42</url_pdf>        <url_supplemental>http://physiolgenomics.physiology.org/cgi/content/full/00032.2006/DC1</url_supplemental>        <abstract>Identification of estrogen-responsive genes is an essential step toward understanding mechanisms of estrogen action during mammary gland development. To identify these genes, 16 prepubertal heifers were used in a 2 x 2 factorial experiment, with ovarian status (intact or ovariectomized) as the first factor and estrogen treatment as the second (control or estradiol). Heifers were ovariectomized at ~4.5 mo of age, and estrogen treatments were initiated 1 mo later. After 3 days of treatment, gene expression was analyzed in the parenchyma and fat pad of the bovine mammary gland using a high-density oligonucleotide microarray. Oligonucelotide probes represented 40,808 tentative consensus sequences from TIGR Bos taurus Gene Index and 4,575 singleton expressed sequence tags derived from libraries of pooled mammary gland and gut tissues. Microarray data were analyzed by use of the SAS mixed procedure, with an experiment-wide permutation-based significance level of P &lt; 0.1. Considerable differences in basal gene expression were noted between mammary parenchyma and fat pad. A total of 124 estrogen-responsive genes were identified, with most responding only in the parenchyma or the fat pad. The majority of genes identified were not previously reported to be estrogen responsive. These undoubtedly include genes that are regulated indirectly but also include known estrogen-targeted genes and novel genes with potential estrogen-responsive elements in their promoter regions. The distinctive expression patterns regulated by estrogen in parenchyma and fat pad shed light on the need for both tissues to obtain normal mammary development.</abstract>        <author>            <id>1</id>            <author_shortname>Li RW</author_shortname>            <author_fullname>Li RW</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Meyer MJ</author_shortname>            <author_fullname>Meyer MJ</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Van Tassell CP</author_shortname>            <author_fullname>Van Tassell CP</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Sonstegard TS</author_shortname>            <author_fullname>Sonstegard TS</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Connor EE</author_shortname>            <author_fullname>Connor EE</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Van Amburgh ME</author_shortname>            <author_fullname>Van Amburgh ME</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Boisclair YRD</author_shortname>            <author_fullname>Boisclair YRD</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Capuco AV</author_shortname>            <author_fullname>Capuco AV</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Bovine Functional Genomics Laboratory, United States Department of Agriculture-Agricultural Research Service, Beltsville, Maryland 20705, USA</institution>    </publication>    <publication pub_id="41">        <status>On</status>        <application>Expression</application>        <title>Butyrate induces profound changes in gene expression related to multiple signal pathways in bovine kidney epithelial cells</title>        <journal>BMC Genomics</journal>        <issue>2006, 7:234.</issue>        <pubdate>2006-09-14</pubdate>        <epubdate>2006-09-14</epubdate>        <url>http://dx.doi.org/10.1186/1471-2164-7-234</url>        <url_pdf>http://www.biomedcentral.com/content/pdf/1471-2164-7-234.pdf</url_pdf>        <url_supplemental></url_supplemental>        <abstract>Background Global gene expression profiles of bovine kidney epithelial cells regulated by sodium butyrate were investigated with high-density oligonucleotide microarrays. The bovine microarray with 86,191 distinct 60mer oligonucleotides, each with 4 replicates, was designed and produced with Maskless Array Synthesizer technology. These oligonucleotides represent approximately 45,383 unique cattle sequences. Results 450 genes significantly regulated by butyrate with a median False Discovery Rate (FDR) = 0 % were identified. The majority of these genes were repressed by butyrate and associated with cell cycle control. The expression levels of 30 selected genes identified by the microarray were confirmed using real-time PCR. The results from real-time PCR positively correlated (R = 0.867) with the results from the microarray. Conclusion This study presented the genes related to multiple signal pathways such as cell cycle control and apoptosis. The profound changes in gene expression elucidate the molecular basis for the pleiotropic effects of butyrate on biological processes. These findings enable better recognition of the full range of beneficial roles butyrate may play during cattle energy metabolism, cell growth and proliferation, and possibly in fighting gastrointestinal pathogens.</abstract>        <author>            <id>1</id>            <author_shortname>Li RW</author_shortname>            <author_fullname>Robert W Li</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Li C</author_shortname>            <author_fullname>CongJun Li</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <institution>Bovine Functional Genomics Laboratory, Animal and Natural Resources Institute, United States Department of Agriculture-Agricultural Research Service, Beltsville, MD 20705, USA</institution>        <institution>Growth Biology Laboratory, Animal and Natural Resources Institute, United States Department of Agriculture-Agricultural Research Service, Beltsville, MD 20705, USA</institution>    </publication>    <publication pub_id="42">        <status>On</status>        <application>Expression</application>        <title>The genome of black cottonwood, Populus trichocarpa (Torr. &amp; Gray)</title>        <journal>Science</journal>        <issue>2006 Sep 15;313(5793):1596-604.</issue>        <pubdate>2006-09-15</pubdate>        <epubdate>2006-09-15</epubdate>        <url>http://dx.doi.org/10.1126/science.1128691</url>        <url_pdf></url_pdf>        <url_supplemental>http://www.sciencemag.org/cgi/content/full/sci;313/5793/1596/DC1</url_supplemental>        <abstract>We report the draft genome of the black cottonwood tree, Populus trichocarpa. Integration of shotgun sequence assembly with genetic mapping enabled chromosome-scale reconstruction of the genome. More than 45,000 putative protein-coding genes were identified. Analysis of the assembled genome revealed a whole-genome duplication event; about 8000 pairs of duplicated genes from that event survived in the Populus genome. A second, older duplication event is indistinguishably coincident with the divergence of the Populus and Arabidopsis lineages. Nucleotide substitution, tandem gene duplication, and gross chromosomal rearrangement appear to proceed substantially more slowly in Populus than in Arabidopsis. Populus has more protein-coding genes than Arabidopsis, ranging on average from 1.4 to 1.6 putative Populus homologs for each Arabidopsis gene. However, the relative frequency of protein domains in the two genomes is similar. Overrepresented exceptions in Populus include genes associated with lignocellulosic wall biosynthesis, meristem development, disease resistance, and metabolite transport.</abstract>        <author>            <id>1</id>            <author_shortname>Tuskan GA</author_shortname>            <author_fullname>Tuskan GA</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Difazio S</author_shortname>            <author_fullname>Difazio S</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Jansson S</author_shortname>            <author_fullname>Jansson S</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Bohlmann J</author_shortname>            <author_fullname>Bohlmann J</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Grigoriev I</author_shortname>            <author_fullname>Grigoriev I</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Hellsten U</author_shortname>            <author_fullname>Hellsten U</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Putnam N</author_shortname>            <author_fullname>Putnam N</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Ralph S</author_shortname>            <author_fullname>Ralph S</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Rombauts S</author_shortname>            <author_fullname>Rombauts S</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Salamov A</author_shortname>            <author_fullname>Salamov A</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Schein J</author_shortname>            <author_fullname>Schein J</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>12</id>            <author_shortname>Sterck L</author_shortname>            <author_fullname>Sterck L</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>13</id>            <author_shortname>Aerts A</author_shortname>            <author_fullname>Aerts A</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>14</id>            <author_shortname>Bhalerao RR</author_shortname>            <author_fullname>Bhalerao RR</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>15</id>            <author_shortname>Bhalerao RP</author_shortname>            <author_fullname>Bhalerao RP</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>16</id>            <author_shortname>Blaudez D</author_shortname>            <author_fullname>Blaudez D</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>17</id>            <author_shortname>Boerjan W</author_shortname>            <author_fullname>Boerjan W</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>18</id>            <author_shortname>Brun A</author_shortname>            <author_fullname>Brun A</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>19</id>            <author_shortname>Brunner A</author_shortname>            <author_fullname>Brunner A</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>20</id>            <author_shortname>Busov V</author_shortname>            <author_fullname>Busov V</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>21</id>            <author_shortname>Campbell M</author_shortname>            <author_fullname>Campbell M</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>22</id>            <author_shortname>Carlson J</author_shortname>            <author_fullname>Carlson J</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>23</id>            <author_shortname>Chalot M</author_shortname>            <author_fullname>Chalot M</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>24</id>            <author_shortname>Chapman J</author_shortname>            <author_fullname>Chapman J</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>25</id>            <author_shortname>Chen GL</author_shortname>            <author_fullname>Chen GL</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>26</id>            <author_shortname>Cooper D</author_shortname>            <author_fullname>Cooper D</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>27</id>            <author_shortname>Coutinho PM</author_shortname>            <author_fullname>Coutinho PM</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>28</id>            <author_shortname>Couturier J</author_shortname>            <author_fullname>Couturier J</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>29</id>            <author_shortname>Covert S</author_shortname>            <author_fullname>Covert S</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>30</id>            <author_shortname>Cronk Q</author_shortname>            <author_fullname>Cronk Q</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>31</id>            <author_shortname>Cunningham R</author_shortname>            <author_fullname>Cunningham R</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>32</id>            <author_shortname>Davis J</author_shortname>            <author_fullname>Davis J</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>33</id>            <author_shortname>Degroeve S</author_shortname>            <author_fullname>Degroeve S</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>34</id>            <author_shortname>Dejardin A</author_shortname>            <author_fullname>Dejardin A</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>35</id>            <author_shortname>Depamphilis C</author_shortname>            <author_fullname>Depamphilis C</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>36</id>            <author_shortname>Detter J</author_shortname>            <author_fullname>Detter J</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>37</id>            <author_shortname>Dirks B</author_shortname>            <author_fullname>Dirks B</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>38</id>            <author_shortname>Dubchak I</author_shortname>            <author_fullname>Dubchak I</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>39</id>            <author_shortname>Duplessis S</author_shortname>            <author_fullname>Duplessis S</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>40</id>            <author_shortname>Ehlting J</author_shortname>            <author_fullname>Ehlting J</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>41</id>            <author_shortname>Ellis B</author_shortname>            <author_fullname>Ellis B</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>42</id>            <author_shortname>Gendler K</author_shortname>            <author_fullname>Gendler K</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>43</id>            <author_shortname>Goodstein D</author_shortname>            <author_fullname>Goodstein D</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>44</id>            <author_shortname>Gribskov M</author_shortname>            <author_fullname>Gribskov M</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>45</id>            <author_shortname>Grimwood J</author_shortname>            <author_fullname>Grimwood J</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>46</id>            <author_shortname>Groover A</author_shortname>            <author_fullname>Groover A</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>47</id>            <author_shortname>Gunter L</author_shortname>            <author_fullname>Gunter L</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>48</id>            <author_shortname>Hamberger B</author_shortname>            <author_fullname>Hamberger B</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>49</id>            <author_shortname>Heinze B</author_shortname>            <author_fullname>Heinze B</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>50</id>            <author_shortname>Helariutta Y</author_shortname>            <author_fullname>Helariutta Y</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>51</id>            <author_shortname>Henrissat B</author_shortname>            <author_fullname>Henrissat B</author_fullname>            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<application>Expression</application>        <title>A Novel Approach for Characterizing Expression Levels of Genes Duplicated by Polyploidy</title>        <journal>Genetics</journal>        <issue>2006 Jul;173(3):1823-7. Epub 2006 May 15.</issue>        <pubdate>2006-07-01</pubdate>        <epubdate>2006-05-15</epubdate>        <url>http://dx.doi.org/10.1534/genetics.106.058271</url>        <url_pdf>http://www.genetics.org/cgi/rapidpdf/genetics.106.058271v1</url_pdf>        <url_supplemental>http://www.genetics.org/cgi/content/full/genetics.106.058271/DC1</url_supplemental>        <abstract>Studying gene expression in polyploids is complicated by genome-wide gene duplication and the problem of distinguishing transcript pools derived from each of the two homoeologous genomes such as the A- and D-genomes of allotetraploid Gossypium. Short oligonucleotide probes designed to specifically target several hundred homoeologous gene pairs of Gossypium were printed on custom NimbleGen microarrays. These results demonstrate that relative expression levels of homoeologous genes may be measured by microarrays and that deviation from equal expression levels of homoeologous loci may be common in the allotetraploid nucleus of Gossypium.</abstract>        <author>            <id>1</id>            <author_shortname>Udall JA</author_shortname>            <author_fullname>Joshua A. Udall</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Swanson JM</author_shortname>            <author_fullname>Jordan M. Swanson</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Nettleton D</author_shortname>            <author_fullname>Dan Nettleton</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Percifield RJ</author_shortname>            <author_fullname>Ryan J. Percifield</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Wendel JF</author_shortname>            <author_fullname>Jonathan F. Wendel</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, Iowa</institution>        <institution>Department of Statistics, Iowa State University, Ames, Iowa</institution>    </publication>    <publication pub_id="44">        <status>On</status>        <application>Expression</application>        <title>A Transcriptome-Based Characterization of Habituation in Plant Tissue Culture</title>        <journal>Plant Physiol.</journal>        <issue>2006 Apr;140(4):1255-78. Epub 2006 Feb 17.</issue>        <pubdate>2006-02-17</pubdate>        <epubdate>2006-02-17</epubdate>        <url>http://dx.doi.org/10.1104/pp.105.076059</url>        <url_pdf></url_pdf>        <url_supplemental>http://www.plantphysiol.org/cgi/content/full/pp.105.076059/DC1</url_supplemental>        <abstract>For the last 50 years, scientists have recognized that varying ratios of the plant hormones cytokinin and auxin induce plant cells to form particular tissues: undifferentiated calli, shoot structures, root structures, or a whole plant. Proliferation of undifferentiated callus tissue, greening, and the formation of shoot structures are all cytokinin-dependent processes. Habituation refers to a naturally occurring phenomenon whereby callus cultures, upon continued passage, lose their requirement for cytokinin. Earlier studies of calli with a higher-than-normal cytokinin content indicate that overproduction of cytokinin by the culture tissues is a possible explanation for this acquired cytokinin independence. A transcriptome-based analysis of a well established habituated Arabidopsis (Arabidopsis thaliana) cell culture line was undertaken, to explore genome-wide expression changes underlying the phenomenon of habituation. Increased levels of expression of the cytokinin receptor CRE1, as well as altered levels of expression of several other genes involved in cytokinin signaling, indicated that naturally acquired deregulation of cytokinin-signaling components could play a previously unrecognized role in habituation. Up-regulation of several cytokinin oxidases, down-regulation of several known cytokinin-inducible genes, and a lack of regulation of the cytokinin synthases indicated that increases in hormone concentration may not be required for habituation. In addition, up-regulation of the homeodomain transcription factor FWA, transposon-related elements, and several DNA- and chromatin-modifying enzymes indicated that epigenetic changes contribute to the acquisition of cytokinin habituation.</abstract>        <author>            <id>1</id>            <author_shortname>Pischke MS</author_shortname>            <author_fullname>Michael R. Sussman</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>University of Wisconsin Biotechnology Center and Department of Biochemistry, Madison, Wisconsin</institution>    </publication>    <publication pub_id="45">        <status>On</status>        <application>Expression</application>        <title>Expression profiling by whole-genome interspecies microarray hybridization reveals differential gene expression in procyclic promastigotes, lesion-derived amastigotes, and axenic amastigotes in Leishmania mexicana</title>        <journal>Mol. Biochem. Parasitol.</journal>        <issue>2006 Apr;146(2):198-218. Epub 2006 Jan 6</issue>        <pubdate>2006-04-01</pubdate>        <epubdate>2006-01-06</epubdate>        <url>http://dx.doi.org/10.1016/j.molbiopara.2005.12.009</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>We examined the Leishmania mexicana transcriptome to identify differentially regulated mRNAs using high-density whole-genome oligonucleotide microarrays designed from the genome data of a closely related species, Leishmania major. Statistical analysis on array hybridization data representing 8156 predicted coding regions revealed 288 genes (3.5% of all genes) whose steady-state mRNA levels meet criteria for differential regulation between promastigotes and lesion-derived amastigotes. Interestingly, sample comparison of promastigotes to axenic amastigotes resulted in only 17 genes (0.2%) that meet the same statistical criteria for differential regulation. The reduced number of regulated genes is a consequence of an increase in the magnitude of the transcript levels in cells under axenic conditions. The expression data for a subset of genes was validated by quantitative PCR. Our studies show that interspecies hybridization on microarrays can be used to analyze closely related protozoan parasites, that axenic culture conditions may alter amastigote transcript abundance, and that there is only a relatively modest change in abundance of a few mRNAs between morphologically distinct promastigote and amastigote cultured cells. Leishmania may represent an alternative paradigm for eukaryotic differentiation with minimal contributions from changes in mRNA abundance.</abstract>        <author>            <id>1</id>            <author_shortname>Holzer TR</author_shortname>            <author_fullname>Holzer TR</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>McMaster WR</author_shortname>            <author_fullname>McMaster WR</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Forney JD</author_shortname>            <author_fullname>Forney JD</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Purdue University, Department of Biochemistry, West Lafayette, IN.</institution>    </publication>    <publication pub_id="46">        <status>On</status>        <application>Expression</application>        <title>Global identification of noncoding RNAs in Saccharomyces cerevisiae by modulating an essential RNA processing pathway</title>        <journal>PNAS</journal>        <issue>2006 Mar 14;103(11):4192-7. Epub 2006 Mar 6</issue>        <pubdate>2006-03-14</pubdate>        <epubdate>2006-03-06</epubdate>        <url>http://dx.doi.org/10.1073/pnas.0507669103</url>        <url_pdf>http://www.pubmedcentral.nih.gov/picrender.fcgi?artid=1389707&amp;blobtype=pdf</url_pdf>        <url_supplemental></url_supplemental>        <abstract>Noncoding RNAs (ncRNAs) perform essential cellular tasks and play key regulatory roles in all organisms. Although several new ncRNAs in yeast were recently discovered by individual studies, to our knowledge no comprehensive empirical search has been conducted. We demonstrate a powerful and versatile method for global identification of previously undescribed ncRNAs by modulating an essential RNA processing pathway through the depletion of a key ribonucleoprotein enzyme component, and monitoring differential transcriptional activities with genome tiling arrays during the time course of the ribonucleoprotein depletion. The entire Saccharomyces cerevisiae genome was scanned during cell growth decay regulated by promoter-mediated depletion of Rpp1, an essential and functionally conserved protein component of the RNase P enzyme. In addition to most verified genes and ncRNAs, expression was detected in 98 antisense and intergenic regions, 74 that were further confirmed to contain previously undescribed RNAs. A class of ncRNAs, located antisense to coding regions of verified protein-coding genes, is discussed in this article. One member, HRA1, is likely involved in 18S rRNA maturation.</abstract>        <author>            <id>1</id>            <author_shortname>Samanta MP</author_shortname>            <author_fullname>Samanta MP</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Tongprasit W</author_shortname>            <author_fullname>Tongprasit W</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Sethi H</author_shortname>            <author_fullname>Sethi H</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Chin CS</author_shortname>            <author_fullname>Chin CS</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Stolc V</author_shortname>            <author_fullname>Stolc V</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Genome Research Facility, National Aeronautics and Space Administration Ames Research Center, Moffett Field, CA</institution>    </publication>    <publication pub_id="47">        <status>On</status>        <application>Expression</application>        <title>Genome-wide transcription analyses in rice using tiling microarrays</title>        <journal>Nat. Genet.</journal>        <issue>2006 Jan;38(1):124-9. Epub 2005 Dec 20</issue>        <pubdate>2005-12-20</pubdate>        <epubdate>2005-12-20</epubdate>        <url>http://dx.doi.org/10.1038/ng1704</url>        <url_pdf>http://www.nature.com/ng/journal/v38/n1/pdf/ng1704.pdf</url_pdf>        <url_supplemental>http://www.nature.com/ng/journal/v38/n1/suppinfo/ng1704_S1.html</url_supplemental>        <abstract>Sequencing and computational annotation revealed several features, including high gene numbers, unusual composition of the predicted genes and a large number of genes lacking homology to known genes, that distinguish the rice (Oryza sativa) genome from that of other fully sequenced model species. We report here a full-genome transcription analysis of the indica rice subspecies using high-density oligonucleotide tiling microarrays. Our results provided expression data support for the existence of 35,970 (81.9%) annotated gene models and identified 5,464 unique transcribed intergenic regions that share similar compositional properties with the annotated exons and have significant homology to other plant proteins. Elucidating and mapping of all transcribed regions revealed an association between global transcription and cytological chromosome features, and an overall similarity of transcriptional activity between duplicated segments of the genome. Collectively, our results provide the first whole-genome transcription map useful for further understanding the rice genome.</abstract>        <author>            <id>1</id>            <author_shortname>Li L</author_shortname>            <author_fullname>Lei Li</author_fullname>            <author_affiliation>1,2,9</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Wang X</author_shortname>            <author_fullname>Xiangfeng Wang</author_fullname>            <author_affiliation>1,3,4,9</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Stolc V</author_shortname>            <author_fullname>Viktor Stolc</author_fullname>            <author_affiliation>2,5,9</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Li X</author_shortname>            <author_fullname>Xueyong Li</author_fullname>            <author_affiliation>2,6</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Zhang D</author_shortname>            <author_fullname>Dongfen Zhang</author_fullname>            <author_affiliation>7</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Su N</author_shortname>            <author_fullname>Ning Su</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Tongprasit W</author_shortname>            <author_fullname>Waraporn Tongprasit</author_fullname>            <author_affiliation>8</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Li S</author_shortname>            <author_fullname>Songgang Li</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Cheng Z</author_shortname>            <author_fullname>Zhukuan Cheng</author_fullname>            <author_affiliation>7</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Wang J</author_shortname>            <author_fullname>Jun Wang</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Deng XW</author_shortname>            <author_fullname>Xing Wang Deng</author_fullname>            <author_affiliation>2,3</author_affiliation>        </author>        <institution>National Institute of Biological Sciences, Zhongguancun Life Science Park, Beijing, China.</institution>        <institution>Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, Connecticut, USA.</institution>        <institution>Peking-Yale Joint Research Center of Plant Molecular Genetics and Agrobiotechnology, College of Life Sciences, Peking University, Beijing, China.</institution>        <institution>Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.</institution>        <institution>Genome Research Facility, NASA Ames Research Center, MS 239-11, Moffett Field, California, USA.</institution>        <institution>National Center for Crop Design, China Bioway Biotech Group Co., LTD, Beijing, China.</institution>        <institution>Institute of Genetics &amp; Developmental Biology, Academy of Sciences, Beijing, China.</institution>        <institution>Eloret Corporation, Sunnyvale, California, USA.</institution>        <institution>These authors contributed equally to this work.</institution>    </publication>    <publication pub_id="48">        <status>On</status>        <application>Expression</application>        <title>A pilot study of transcription unit analysis in rice using oligonucleotide tiling-path microarray</title>        <journal>Plant Mol. Biol.</journal>        <issue>2005 Sep;59(1):137-49</issue>        <pubdate>2005-09-01</pubdate>        <epubdate>2005-09-01</epubdate>        <url>http://dx.doi.org/10.1007/s11103-005-6164-5</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>As the international efforts to sequence the rice genome are completed, an immediate challenge and opportunity is to comprehensively and accurately define all transcription units in the rice genome. Here we describe a strategy of using high-density oligonucleotide tiling-path microarrays to map transcription of the japonica rice genome. In a pilot experiment to test this approach, one array representing the reverse strand of the last 11.2 Mb sequence of chromosome 10 was analyzed in detail based on a mathematical model developed in this study. Analysis of the array data detected 77% of the reference gene models in a mixture of four RNA populations. Moreover, significant transcriptional activities were found in many of the previously annotated intergenic regions. These preliminary results demonstrate the utility of genome tiling microarrays in evaluating annotated rice gene models and in identifying novel transcription units that will facilitate rice genome annotation.</abstract>        <author>            <id>1</id>            <author_shortname>Stolc V</author_shortname>            <author_fullname>Stolc V</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Li L</author_shortname>            <author_fullname>Li L</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Wang X</author_shortname>            <author_fullname>Wang X</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Li X</author_shortname>            <author_fullname>Li X</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Su N</author_shortname>            <author_fullname>Su N</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Tongprasit W</author_shortname>            <author_fullname>Tongprasit W</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Han B</author_shortname>            <author_fullname>Han B</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Xue Y</author_shortname>            <author_fullname>Xue Y</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Li J</author_shortname>            <author_fullname>Li J</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Snyder M</author_shortname>            <author_fullname>Snyder M</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Gerstein M</author_shortname>            <author_fullname>Gerstein M</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>12</id>            <author_shortname>Wang J</author_shortname>            <author_fullname>Wang J</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>13</id>            <author_shortname>Deng XW</author_shortname>            <author_fullname>Deng XW</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Department of Molecular Cellular and Developmental Biology, Yale University, New Haven, CT 06520, USA</institution>    </publication>    <publication pub_id="49">        <status>On</status>        <application>Expression</application>        <title>Gene expression in peripheral blood mononuclear cells from patients with chronic fatigue syndrome</title>        <journal>J. Clin. Pathol.</journal>        <issue>2005 Aug;58(8):826-32.</issue>        <pubdate>2005-08-01</pubdate>        <epubdate>2005-08-01</epubdate>        <url>http://dx.doi.org/10.1136/jcp.2005.025718</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>BACKGROUND: Chronic fatigue syndrome (CFS) is a multisystem disease, the pathogenesis of which remains undetermined. AIMS: To test the hypothesis that there are reproducible abnormalities of gene expression in patients with CFS compared with normal healthy persons. METHODS: To gain further insight into the pathogenesis of this disease, gene expression was analysed in peripheral blood mononuclear cells from 25 patients with CFS diagnosed according to the Centers for Disease Control criteria and 25 normal blood donors matched for age, sex, and geographical location, using a single colour microarray representing 9522 human genes. After normalisation, average difference values for each gene were compared between test and control groups using a cutoff fold difference of expression &gt; or = 1.5 and a p value of 0.001. Genes showing differential expression were further analysed using Taqman real time polymerase chain reaction (PCR) in fresh samples. RESULTS: Analysis of microarray data revealed differential expression of 35 genes. Real time PCR confirmed differential expression in the same direction as array results for 16 of these genes, 15 of which were upregulated (ABCD4, PRKCL1, MRPL23, CD2BP2, GSN, NTE, POLR2G, PEX16, EIF2B4, EIF4G1, ANAPC11, PDCD2, KHSRP, BRMS1, and GABARAPL1) and one of which was downregulated (IL-10RA). This profile suggests T cell activation and perturbation of neuronal and mitochondrial function. Upregulation of neuropathy target esterase and eukaryotic translation initiation factor 4G1 may suggest links with organophosphate exposure and virus infection, respectively. CONCLUSION: These results suggest that patients with CFS have reproducible alterations in gene regulation.</abstract>        <author>            <id>1</id>            <author_shortname>Kaushik N</author_shortname>            <author_fullname>N Kaushik</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Fear D</author_shortname>            <author_fullname>D Fear</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Richards SC</author_shortname>            <author_fullname>S C M Richards</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>McDermott CR</author_shortname>            <author_fullname>C R McDermott</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Nuwaysir EF</author_shortname>            <author_fullname>E F Nuwaysir</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Kellam P</author_shortname>            <author_fullname>P Kellam</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Harrison TJ</author_shortname>            <author_fullname>T J Harrison</author_fullname>            <author_affiliation>6</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Wilkinson RJ</author_shortname>            <author_fullname>R J Wilkinson</author_fullname>            <author_affiliation>7</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Tyrrell DA</author_shortname>            <author_fullname>D A J Tyrrell</author_fullname>            <author_affiliation>8</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Holgate ST</author_shortname>            <author_fullname>S T Holgate</author_fullname>            <author_affiliation>9</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Kerr JR</author_shortname>            <author_fullname>J R Kerr</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Department of Paediatric Infectious Diseases, St Mary&#8217;s Campus, Imperial College, 2nd Floor, Medical School Building, Norfolk Place, London W2 1PG, UK</institution>        <institution>The Randall Centre, New Hunt&#8217;s House, King&#8217;s College London, Guy&#8217;s Campus, London SE1 1UL, UK</institution>        <institution>Dorset CFS Service, Wareham, Dorset, UK</institution>        <institution>NimbleGen Systems, Inc, 1 Science Court, Madison, WI 53711, USA</institution>        <institution>Department of Infection, Windeyer Institute of Medical Sciences, Royal Free and University College School of Medicine, London W1T 4JF, UK</institution>        <institution>Department of Medicine, Windeyer Institute of Medical Sciences</institution>        <institution>Wellcome Trust Centre for Research in Clinical Tropical Medicine, Faculty of Medicine, Imperial College London, London W2 1PG, UK</institution>        <institution>CFS Research Foundation, 2 The Briars, Rickmansworth, Hertfordshire WD3 6AU, UK</institution>        <institution>MRC Department of Immunopharmacology, University of Southampton, Southampton SO16 6YD, UK</institution>    </publication>    <publication pub_id="50">        <status>On</status>        <application>Expression</application>        <title>Tiling microarray analysis of rice chromosome 10 to identify the transcriptome and relate its expression to chromosomal architecture</title>        <journal>Genome Biol.</journal>        <issue>2005;6(6):R52. Epub 2005 May 27</issue>        <pubdate>2005-05-27</pubdate>        <epubdate>2005-05-27</epubdate>        <url>http://dx.doi.org/10.1186/gb-2005-6-6-r52</url>        <url_pdf>http://genomebiology.com/content/pdf/gb-2005-6-6-r52.pdf</url_pdf>        <url_supplemental></url_supplemental>        <abstract>BACKGROUND: Sequencing and annotation of the genome of rice (Oryza sativa) have generated gene models in numbers that top all other fully sequenced species, with many lacking recognizable sequence homology to known genes. Experimental evaluation of these gene models and identification of new models will facilitate rice genome annotation and the application of this knowledge to other more complex cereal genomes. RESULTS: We report here an analysis of the chromosome 10 transcriptome of the two major rice subspecies, japonica and indica, using oligonucleotide tiling microarrays. This analysis detected expression of approximately three-quarters of the gene models without previous experimental evidence in both subspecies. Cloning and sequence analysis of the previously unsupported models suggests that the predicted gene structure of nearly half of those models needs improvement. Coupled with comparative gene model mapping, the tiling microarray analysis identified 549 new models for the japonica chromosome, representing an 18% increase in the annotated protein-coding capacity. Furthermore, an asymmetric distribution of genome elements along the chromosome was found that coincides with the cytological definition of the heterochromatin and euchromatin domains. The heterochromatin domain appears to associate with distinct chromosome level transcriptional activities under normal and stress conditions. CONCLUSION: These results demonstrated the utility of genome tiling microarray in evaluating annotated rice gene models and in identifying novel transcriptional units. The tiling microarray sanalysis further revealed a chromosome-wide transcription pattern that suggests a role for transposable element-enriched heterochromatin in shaping global transcription in response to environmental changes in rice.</abstract>        <author>            <id>1</id>            <author_shortname>Li L</author_shortname>            <author_fullname>Lei Li</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Wang X</author_shortname>            <author_fullname>Xiangfeng Wang</author_fullname>            <author_affiliation>2,3,4</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Xia M</author_shortname>            <author_fullname>Mian Xia</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Stolc V</author_shortname>            <author_fullname>Viktor Stolc</author_fullname>            <author_affiliation>1,6</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Su N</author_shortname>            <author_fullname>Ning Su</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Peng Z</author_shortname>            <author_fullname>Zhiyu Peng</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Li S</author_shortname>            <author_fullname>Songgang Li</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Wang J</author_shortname>            <author_fullname>Jun Wang</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Wang X</author_shortname>            <author_fullname>Xiping Wang</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Deng XW</author_shortname>            <author_fullname>Xing Wang Deng</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT 06520, USA</institution>        <institution>National Institute of Biological Sciences, Zhongguancun Life Science Park, Beijing 102206, China</institution>        <institution>Peking-Yale Joint Research Center of Plant Molecular Genetics and Agrobiotechnology, College of Life Sciences, Peking University, Beijing 100871, China</institution>        <institution>Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 101300, China</institution>        <institution>National Center of Crop Design, China Bioway Biotech Group Co., LTD, Beijing 100085, China</institution>        <institution>Genome Research Facility, NASA Ames Research Center, MS 239-11, Moffett Field, CA 94035, USA</institution>    </publication>    <publication pub_id="51">        <status>On</status>        <application>Expression</application>        <title>Identification of transcribed sequences in Arabidopsis thaliana by using high-resolution genome tiling arrays</title>        <journal>PNAS</journal>        <issue>2005 Mar 22;102(12):4453-8. Epub 2005 Mar 8</issue>        <pubdate>2005-03-22</pubdate>        <epubdate>2005-03-08</epubdate>        <url>http://dx.doi.org/10.1073/pnas.0408203102</url>        <url_pdf>http://www.pubmedcentral.nih.gov/picrender.fcgi?artid=555476&amp;blobtype=pdf</url_pdf>        <url_supplemental></url_supplemental>        <abstract>Using a maskless photolithography method, we produced DNA oligonucleotide microarrays with probe sequences tiled throughout the genome of the plant Arabidopsis thaliana. RNA expression was determined for the complete nuclear, mitochondrial, and chloroplast genomes by tiling 5 million 36-mer probes. These probes were hybridized to labeled mRNA isolated from liquid grown T87 cells, an undifferentiated Arabidopsis cell culture line. Transcripts were detected from at least 60% of the nearly 26,330 annotated genes, which included 151 predicted genes that were not identified previously by a similar genome-wide hybridization study on four different cell lines. In comparison with previously published results with 25-mer tiling arrays produced by chromium masking-based photolithography technique, 36-mer oligonucleotide probes were found to be more useful in identifying intron-exon boundaries. Using two-dimensional HPLC tandem mass spectrometry, a small-scale proteomic analysis was performed with the same cells. A large amount of strongly hybridizing RNA was found in regions "antisense" to known genes. Similarity of antisense activities between the 25-mer and 36-mer data sets suggests that it is a reproducible and inherent property of the experiments. Transcription activities were also detected for many of the intergenic regions and the small RNAs, including tRNA, small nuclear RNA, small nucleolar RNA, and microRNA. Expression of tRNAs correlates with genome-wide amino acid usage.</abstract>        <author>            <id>1</id>            <author_shortname>Stolc V</author_shortname>            <author_fullname>Viktor Stolc</author_fullname>            <author_affiliation>1,2,6,8</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Samanta MP</author_shortname>            <author_fullname>Manoj Pratim Samanta</author_fullname>            <author_affiliation>3,6,8</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Tongprasit W</author_shortname>            <author_fullname>Waraporn Tongprasit</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Sethi H</author_shortname>            <author_fullname>Himanshu Sethi</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Liang S</author_shortname>            <author_fullname>Shoudan Liang</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Nelson DC</author_shortname>            <author_fullname>David C. Nelson</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Hegeman A</author_shortname>            <author_fullname>Adrian Hegeman</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Nelson C</author_shortname>            <author_fullname>Clark Nelson</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Rancour D</author_shortname>            <author_fullname>David Rancour</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Bednarek S</author_shortname>            <author_fullname>Sebastian Bednarek</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Ulrich EL</author_shortname>            <author_fullname>Eldon L. Ulrich</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>12</id>            <author_shortname>Zhao Q</author_shortname>            <author_fullname>Qin Zhao</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>13</id>            <author_shortname>Wrobel RL</author_shortname>            <author_fullname>Russell L. Wrobel</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>14</id>            <author_shortname>Newman CS</author_shortname>            <author_fullname>Craig S. Newman</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>15</id>            <author_shortname>Fox BG</author_shortname>            <author_fullname>Brian G. Fox</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>16</id>            <author_shortname>Phillips GN Jr</author_shortname>            <author_fullname>George N. Phillips, Jr.</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>17</id>            <author_shortname>Markley JL</author_shortname>            <author_fullname>John L. Markley</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>18</id>            <author_shortname>Sussman MR</author_shortname>            <author_fullname>Michael R. Sussman</author_fullname>            <author_affiliation>5,7</author_affiliation>        </author>        <institution>Genome Research Facility, National Aeronautics and Space Administration Ames Research Center, Moffett Field, CA 94035</institution>        <institution>Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT 06520</institution>        <institution>Systemix Institute, Cupertino, CA 94035</institution>        <institution>Eloret Corporation at National Aeronautics and Space Administration Ames Research Center, Moffett Field, CA 94035</institution>        <institution>Center for Eukaryotic Structural Genomics, University of Wisconsin, Madison, WI 53706</institution>        <institution>V.S. and M.P.S. contributed equally to the work.</institution>        <institution>M.R.S. is a co-founder of NimbleGen Systems, Inc., the company that is commercializing the maskless array synthesizer technology described in this article.</institution>        <institution>To whom correspondence may be addressed. E-mail: vstolc@mail.arc.nasa.gov or manoj.samanta@systemix.org.</institution>    </publication>    <publication pub_id="52">        <status>On</status>        <application>Expression</application>        <title>Genome-wide transcriptional analysis of flagellar regeneration in Chlamydomonas reinhardtii identifies orthologs of ciliary disease genes</title>        <journal>PNAS</journal>        <issue>2005 Mar 8;102(10):3703-7. Epub 2005 Feb 28</issue>        <pubdate>2005-03-08</pubdate>        <epubdate>2005-02-28</epubdate>        <url>http://dx.doi.org/10.1073/pnas.0408358102</url>        <url_pdf>http://www.pubmedcentral.nih.gov/picrender.fcgi?artid=553310&amp;blobtype=pdf</url_pdf>        <url_supplemental>http://www.pubmedcentral.nih.gov/picrender.fcgi?artid=553310&amp;blobname=pnas_102_10_3703__.html</url_supplemental>        <abstract>The important role that cilia and flagella play in human disease creates an urgent need to identify genes involved in ciliary assembly and function. The strong and specific induction of flagellar-coding genes during flagellar regeneration in Chlamydomonas reinhardtii suggests that transcriptional profiling of such cells would reveal new flagella-related genes. We have conducted a genome-wide analysis of RNA transcript levels during flagellar regeneration in Chlamydomonas by using maskless photolithography method-produced DNA oligonucleotide microarrays with unique probe sequences for all exons of the 19,803 predicted genes. This analysis represents previously uncharacterized whole-genome transcriptional activity profiling study in this important model organism. Analysis of strongly induced genes reveals a large set of known flagellar components and also identifies a number of important disease-related proteins as being involved with cilia and flagella, including the zebrafish polycystic kidney genes Qilin, Reptin, and Pontin, as well as the testis-expressed tubby-like protein TULP2.</abstract>        <author>            <id>1</id>            <author_shortname>Stolc V</author_shortname>            <author_fullname>Viktor Stolc</author_fullname>            <author_affiliation>1,2,6</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Samanta MP</author_shortname>            <author_fullname>Manoj Pratim Samanta</author_fullname>            <author_affiliation>1,5</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Tongprasit W</author_shortname>            <author_fullname>Waraporn Tongprasit</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Marshall WF</author_shortname>            <author_fullname>Wallace F. Marshall</author_fullname>            <author_affiliation>4,6</author_affiliation>        </author>        <institution>Genome Research Facility, National Aeronautic and Space Administration, Ames Research Center, Moffett Field, CA 94035</institution>        <institution>Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT 06520</institution>        <institution>Eloret Corporation at National Aeronautic and Space Administration, Ames Research Center, Moffett Field, CA 94035</institution>        <institution>Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94143</institution>        <institution>Systemix Institute, Cupertino, CA 95014</institution>        <institution>To whom correspondence may be addressed. E-mail: vstolc@mail.arc.nasa.gov or wmarshall@biochem.ucsf.edu</institution>    </publication>    <publication pub_id="53">        <status>On</status>        <application>Expression</application>        <title>Regulation of Iron Transport in Streptococcus pneumoniae by RitR, and Orphan Response Regulato</title>        <journal>J. Bacteriol.</journal>        <issue>2004 Dec;186(23):8123-36</issue>        <pubdate>2004-12-01</pubdate>        <epubdate>2004-12-01</epubdate>        <url>http://dx.doi.org/10.1128/JB.186.23.8123-8136.2004</url>        <url_pdf>http://www.pubmedcentral.nih.gov/picrender.fcgi?artid=529065&amp;blobtype=pdf</url_pdf>        <url_supplemental></url_supplemental>        <abstract>RitR (formerly RR489) is an orphan two-component signal transduction response regulator in Streptococcus pneumoniae that has been shown to be required for lung pathogenicity. In the present study, by using the rough strain R800, inactivation of the orphan response regulator gene ritR by allele replacement reduced pathogenicity in a cyclophosphamide-treated mouse lung model but not in a thigh model, suggesting a role for RitR in regulation of tissue-specific virulence factors. Analysis of changes in genome-wide transcript mRNA levels associated with the inactivation of ritR compared to wild-type cells was performed by the use of high-density DNA microarrays. Genes with a change in transcript abundance associated with inactivation of ritR included piuB, encoding an Fe permease subunit, and piuA, encoding an Fe carrier-binding protein. In addition, a dpr ortholog, encoding an H2O2 resistance protein that has been shown to reduce synthesis of reactive oxygen intermediates, was activated in the wild-type (ritR+) strain. Microarray experiments suggested that RitR represses Fe uptake in vitro by negatively regulating the Piu hemin-iron transport system. Footprinting experiments confirmed site-specific DNA-binding activity for RitR and identified three binding sites that partly overlap the +1 site for transcription initiation upstream of piuB. Transcripts belonging to other gene categories found to be differentially expressed in our array studies include those associated with (i) H2O2 resistance, (ii) repair of DNA damage, (iii) sugar transport and capsule biosynthesis, and (iv) two-component signal transduction elements. These observations suggest that RitR is an important response regulator whose primary role is to maintain iron homeostasis in S. pneumoniae. The name ritR (repressor of iron transport) for the orphan response regulator gene, rr489, is proposed.</abstract>        <author>            <id>1</id>            <author_shortname>Ulijasz AT</author_shortname>            <author_fullname>Andrew T. Ulijasz</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Andes DR</author_shortname>            <author_fullname>David R. Andes</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Glasner JD</author_shortname>            <author_fullname>Jeremy D. Glasner</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Weisblum B</author_shortname>            <author_fullname>Bernard Weisblum</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Department of Pharmacology, University of Wisconsin Medical School, Madison, WI</institution>        <institution>Department of Medicine, University of Wisconsin Medical School, Madison, WI</institution>        <institution>School of Veterinary Medicine, University of Wisconsin College of Agriculture and Life Sciences, Madison, WI</institution>    </publication>    <publication pub_id="54">        <status>On</status>        <application>Expression</application>        <title>Global Identification of Human Transcribed Sequences with Genome Tiling Arrays</title>        <journal>Science</journal>        <issue>2004 Dec 24;306(5705):2242-6. Epub 2004 Nov 11</issue>        <pubdate>2004-12-24</pubdate>        <epubdate>2004-11-11</epubdate>        <url>http://dx.doi.org/10.1126/science.1103388</url>        <url_pdf></url_pdf>        <url_supplemental>http://www.sciencemag.org/cgi/content/full/sci;1103388/DC1</url_supplemental>        <abstract>Elucidating the transcribed regions of the genome constitutes a fundamental aspect of human biology, yet this remains an outstanding problem. To comprehensively identify coding sequences, we constructed a series of high-density oligonucleotide tiling arrays representing sense and antisense strands of the entire nonrepetitive sequence of the human genome. Transcribed sequences were located across the genome via hybridization to complementary DNA samples, reverse-transcribed from polyadenylated RNA obtained from human liver tissue. In addition to identifying many known and predicted genes, we found 10,595 transcribed sequences not detected by other methods. A large fraction of these are located in intergenic regions distal from previously annotated genes and exhibit significant homology to other mammalian proteins.</abstract>        <author>            <id>1</id>            <author_shortname>Bertone P</author_shortname>            <author_fullname>Paul Bertone</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Stolc V</author_shortname>            <author_fullname>Viktor Stolc</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Royce TE</author_shortname>            <author_fullname>Thomas E. Royce</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Rozowsky JS</author_shortname>            <author_fullname>Joel S. Rozowsky</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Urban AE</author_shortname>            <author_fullname>Alexander E. Urban</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Zhu X</author_shortname>            <author_fullname>Xiaowei Zhu</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Rinn JL</author_shortname>            <author_fullname>John L. Rinn</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Tongprasit W</author_shortname>            <author_fullname>Waraporn Tongprasit</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Samanta M</author_shortname>            <author_fullname>Manoj Samanta</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Weissman S</author_shortname>            <author_fullname>Sherman Weissman</author_fullname>            <author_affiliation>6</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Gerstein M</author_shortname>            <author_fullname>Mark Gerstein</author_fullname>            <author_affiliation>3,8</author_affiliation>        </author>        <author>            <id>12</id>            <author_shortname>Snyder M</author_shortname>            <author_fullname>Michael Snyder</author_fullname>            <author_affiliation>7,8</author_affiliation>        </author>        <institution>Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT 06520-8103, USA</institution>        <institution>Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT 06520-8103, USA; Center for Nanotechnology, NASA Ames Research Center, Moffett Field, CA 94035, USA</institution>        <institution>Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520-8114, USA</institution>        <institution>Eloret Corporation, Sunnyvale, CA 94087, USA</institution>        <institution>Center for Nanotechnology, NASA Ames Research Center, Moffett Field, CA 94035, USA</institution>        <institution>Department of Genetics, Yale University School of Medicine, New Haven, CT 06520-8005, USA</institution>        <institution>Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT 06520-8103, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520-8114, USA</institution>        <institution>To whom correspondence should be addressed.</institution>    </publication>    <publication pub_id="55">        <status>On</status>        <application>Expression</application>        <title>A Gene Expression Map for the Euchromatic Genome of Drosophila melanogaster</title>        <journal>Science</journal>        <issue>2004 Oct 22;306(5696):655-60</issue>        <pubdate>2004-10-22</pubdate>        <epubdate>2004-10-22</epubdate>        <url>http://dx.doi.org/10.1126/science.1101312</url>        <url_pdf></url_pdf>        <url_supplemental>http://www.sciencemag.org/cgi/content/full/sci;306/5696/655/DC1</url_supplemental>        <abstract>We used a maskless photolithography method to produce DNA oligonucleotide microarrays with unique probe sequences tiled throughout the genome of Drosophila melanogaster and across predicted splice junctions. RNA expression of protein coding and nonprotein coding sequences was determined for each major stage of the life cycle, including adult males and females. We detected transcriptional activity for 93% of annotated genes and RNA expression for 41% of the probes in intronic and intergenic sequences. Comparison to genome-wide RNA interference data and to gene annotations revealed distinguishable levels of expression for different classes of genes and higher levels of expression for genes with essential cellular functions. Differential splicing was observed in about 40% of predicted genes, and 5440 previously unknown splice forms were detected. Genes within conserved regions of synteny with D. pseudoobscura had highly correlated expression; these regions ranged in length from 10 to 900 kilobase pairs. The expressed intergenic and intronic sequences are more likely to be evolutionarily conserved than nonexpressed ones, and about 15% of them appear to be developmentally regulated. Our results provide a draft expression map for the entire nonrepetitive genome, which reveals a much more extensive and diverse set of expressed sequences than was previously predicted.</abstract>        <author>            <id>1</id>            <author_shortname>Stolc V</author_shortname>            <author_fullname>Viktor Stolc</author_fullname>            <author_affiliation>1,5,10</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Gauhar Z</author_shortname>            <author_fullname>Zareen Gauhar</author_fullname>            <author_affiliation>1,2,10</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Mason C</author_shortname>            <author_fullname>Christopher Mason</author_fullname>            <author_affiliation>2,10</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Halasz G</author_shortname>            <author_fullname>Gabor Halasz</author_fullname>            <author_affiliation>7</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>van Batenburg MF</author_shortname>            <author_fullname>Marinus F. van Batenburg</author_fullname>            <author_affiliation>7,9</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Rifkin SA</author_shortname>            <author_fullname>Scott A. Rifkin</author_fullname>            <author_affiliation>2,3</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Hua S</author_shortname>            <author_fullname>Sujun Hua</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Herreman T</author_shortname>            <author_fullname>Tine Herreman</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Tongprasit W</author_shortname>            <author_fullname>Waraporn Tongprasit</author_fullname>            <author_affiliation>6</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Barbano PE</author_shortname>            <author_fullname>Paolo Emilio Barbano</author_fullname>            <author_affiliation>2,4</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Bussemaker HJ</author_shortname>            <author_fullname>Harmen J. Bussemaker</author_fullname>            <author_affiliation>7,8</author_affiliation>        </author>        <author>            <id>12</id>            <author_shortname>White KP</author_shortname>            <author_fullname>Kevin P. White</author_fullname>            <author_affiliation>2,3,11</author_affiliation>        </author>        <institution>Department of Genetics,</institution>        <institution>Department of Molecular, Cellular, and Developmental Biology.</institution>        <institution>Department of Ecology and Evolutionary Biology</institution>        <institution>Department of Mathematics, Yale University, New Haven, CT 06520, USA</institution>        <institution>Genome Research Facility, NASA Ames Research Center, Mail Stop 239-11, Moffett Field, CA 94035, USA</institution>        <institution>Eloret Corporation, Sunnyvale, CA 94087, USA</institution>        <institution>Department of Biological Sciences</institution>        <institution>Center for Computational Biology and Bioinformatics, Columbia University, New York, NY 10027, USA</institution>        <institution>Bioinformatics Laboratory, Academic Medical Center, University of Amsterdam, 1100 DE Amsterdam, Netherlands</institution>        <institution>These authors contributed equally to this work.</institution>        <institution>To whom correspondence should be addressed. E-mail: kevin.white@yale.edu</institution>    </publication>    <publication pub_id="56">        <status>On</status>        <application>Expression</application>        <title>Gene expression analysis using oligonucleotide arrays produced by maskless photolithography</title>        <journal>Genome Res.</journal>        <issue>2002 Nov;12(11):1749-55</issue>        <pubdate>2002-11-01</pubdate>        <epubdate>2002-11-01</epubdate>        <url>http://dx.doi.org/10.1101/gr.362402</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>Microarrays containing 195,000 in situ synthesized oligonucleotide features have been created using a benchtop, maskless photolithographic instrument. This instrument, the Maskless Array Synthesizer (MAS), uses a digital light processor (DLP) developed by Texas Instruments. The DLP creates the patterns of UV light used in the light-directed synthesis of oligonucleotides. This digital mask eliminates the need for expensive and time-consuming chromium masks. In this report, we describe experiments in which we tested this maskless technology for DNA synthesis on glass surfaces. Parameters examined included deprotection rates, repetitive yields, and oligonucleotide length. Custom gene expression arrays were manufactured and hybridized to Drosophila melanogaster and mouse samples. Quantitative PCR was used to validate the gene expression data from the mouse arrays.</abstract>        <author>            <id>1</id>            <author_shortname>Nuwaysir EF</author_shortname>            <author_fullname>Emile F. Nuwaysir</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Huang W</author_shortname>            <author_fullname>Wei Huang</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Albert TJ</author_shortname>            <author_fullname>Thomas J. Albert</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Singh J</author_shortname>            <author_fullname>Jaz Singh</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Nuwaysir K</author_shortname>            <author_fullname>Kate Nuwaysir</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Pitas A</author_shortname>            <author_fullname>Alan Pitas</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Richmond T</author_shortname>            <author_fullname>Todd Richmond</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Gorski T</author_shortname>            <author_fullname>Tom Gorski</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Berg JP</author_shortname>            <author_fullname>James P. Berg</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Ballin J</author_shortname>            <author_fullname>Jeff Ballin</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>McCormick M</author_shortname>            <author_fullname>Mark McCormick</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>12</id>            <author_shortname>Norton J</author_shortname>            <author_fullname>Jason Norton</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>13</id>            <author_shortname>Pollock T</author_shortname>            <author_fullname>Tim Pollock</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>14</id>            <author_shortname>Sumwalt T</author_shortname>            <author_fullname>Terry Sumwalt</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>15</id>            <author_shortname>Butcher L</author_shortname>            <author_fullname>Lawrence Butcher</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>16</id>            <author_shortname>Porter D</author_shortname>            <author_fullname>DeAnn Porter</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>17</id>            <author_shortname>Molla M</author_shortname>            <author_fullname>Michael Molla</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>18</id>            <author_shortname>Hall C</author_shortname>            <author_fullname>Christine Hall</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>19</id>            <author_shortname>Blattner F</author_shortname>            <author_fullname>Fred Blattner</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>20</id>            <author_shortname>Sussman MR</author_shortname>            <author_fullname>Michael R. Sussman</author_fullname>            <author_affiliation>6</author_affiliation>        </author>        <author>            <id>21</id>            <author_shortname>Wallace RL</author_shortname>            <author_fullname>Rodney L. Wallace</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>22</id>            <author_shortname>Cerrina F</author_shortname>            <author_fullname>Franco Cerrina</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>23</id>            <author_shortname>Green RD</author_shortname>            <author_fullname>Roland D. Green</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>NimbleGen Systems, Inc., Madison, WI 53711</institution>        <institution>Center for NanoTechnology (Department of Electrical Engineering and Computer Engineering</institution>        <institution>Computer Sciences Department,</institution>        <institution>Department of Bacteriology</institution>        <institution>Department of Genetics</institution>        <institution>Biotechnology Center, University of Wisconsin, Madison, WI 53706</institution>    </publication>    <publication pub_id="57">        <status>On</status>        <application>Binding Sequence Characterization</application>        <title>Defining the sequence-recognition profile of DNA-binding molecules</title>        <journal>PNAS</journal>        <issue>2006 Jan 24;103(4):867-72. Epub 2006 Jan 17</issue>        <pubdate>2006-01-24</pubdate>        <epubdate>2006-01-17</epubdate>        <url>http://dx.doi.org/10.1073/pnas.0509843102</url>        <url_pdf>http://www.pubmedcentral.nih.gov/picrender.fcgi?artid=1347994&amp;blobtype=pdf</url_pdf>        <url_supplemental>http://www.pubmedcentral.nih.gov/picrender.fcgi?artid=1347994&amp;blobname=pnas_0509843102_index.html</url_supplemental>        <abstract>Determining the sequence-recognition properties of DNA-binding proteins and small molecules remains a major challenge. To address this need, we have developed a high-throughput approach that provides a comprehensive profile of the binding properties of DNA-binding molecules. The approach is based on displaying every permutation of a duplex DNA sequence (up to 10 positional variants) on a microfabricated array. The entire sequence space is interrogated simultaneously, and the affinity of a DNA-binding molecule for every sequence is obtained in a rapid, unbiased, and unsupervised manner. Using this platform, we have determined the full molecular recognition profile of an engineered small molecule and a eukaryotic transcription factor. The approach also yielded unique insights into the altered sequence-recognition landscapes as a result of cooperative assembly of DNA-binding molecules in a ternary complex. Solution studies strongly corroborated the sequence preferences identified by the array analysis.</abstract>        <author>            <id>1</id>            <author_shortname>Warren CL</author_shortname>            <author_fullname>Christopher L. Warren</author_fullname>            <author_affiliation>1,2</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Kratochvil NC</author_shortname>            <author_fullname>Natasha C. S. Kratochvil</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Hauschild KE</author_shortname>            <author_fullname>Karl E. Hauschil</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Foister S</author_shortname>            <author_fullname>Shane Foister</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Brezinski ML</author_shortname>            <author_fullname>Mary L. Brezinski</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Dervan PB</author_shortname>            <author_fullname>Peter B. Dervan</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Phillips GN Jr</author_shortname>            <author_fullname>George N. Phillips, Jr.</author_fullname>            <author_affiliation>1,2</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Ansari AZ</author_shortname>            <author_fullname>Aseem Z. Ansari</author_fullname>            <author_affiliation>1,2</author_affiliation>        </author>        <institution>Department of Biochemistry</institution>        <institution>and Genome Center, University of Wisconsin, Madison, WI</institution>        <institution>Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA</institution>    </publication>    <publication pub_id="58">        <status>On</status>        <application>Transposon Mapping</application>        <title>Phenotypic Screening of Escherichia coli K-12 Tn5 Insertion Libraries, Using Whole-Genome Oligonucleotide Microarrays</title>        <journal>Appl. Environ. Microbiol.</journal>        <issue>2005 Jan;71(1):451-9</issue>        <pubdate>2005-01-01</pubdate>        <epubdate>2005-01-01</epubdate>        <url>http://dx.doi.org/10.1128/AEM.71.1.451-459.2005</url>        <url_pdf>http://www.pubmedcentral.nih.gov/picrender.fcgi?artid=544249&amp;blobtype=pdf</url_pdf>        <url_supplemental></url_supplemental>        <abstract>Complete genome sequences in combination with global screening methods allow parallel analysis of multiple mutant loci to determine the requirement for specific genes in different environments. In this paper we describe a high-definition microarray approach for investigating the growth effects of Tn5 insertions in Escherichia coli K-12. Libraries of insertion mutants generated by a unique Tn5 mutagenesis system were grown competitively in defined media. Biotin-labeled runoff RNA transcripts were generated in vitro from transposon insertions in each population of mutants. These transcripts were then hybridized to custom-designed oligonucleotide microarrays to detect the presence of each mutant in the population. By using this approach, the signal associated with 25 auxotrophic insertions in a 50-mutant pool was not detectable following nine generations of growth in glucose M9 minimal medium. It was found that individual insertion sites could be mapped to within 50 bp of their genomic locations, and 340 dispensable regions in the E. coli chromosome were identified. Tn5 insertions were detected in 15 genes for which no previous insertions have been reported. Other applications of this method are discussed.</abstract>        <author>            <id>1</id>            <author_shortname>Winterberg KM</author_shortname>            <author_fullname>Kelly M. Winterberg</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Luecke J</author_shortname>            <author_fullname>John Luecke</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Bruegl AS</author_shortname>            <author_fullname>Amanda S. Brueg</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Reznikoff WS</author_shortname>            <author_fullname>William S. Reznikoff</author_fullname>            <author_affiliation>1,4</author_affiliation>        </author>        <institution>Department of Biochemistry</institution>        <institution>Genome Expression Center, University of Wisconsin-Madison, Madison, Wisconsin</institution>        <institution>University of Washington-Seattle, Seattle, Washington</institution>        <institution>Corresponding author. Mailing address: Department of Biochemistry, University of Wisconsin-Madison, 433 Babcock Dr., Room 319, Madison, WI 53706-1544. Phone: (608) 262-3608. Fax: (608) 265-2603. E-mail: reznikoff@biochem.wisc.edu</institution>    </publication>    <publication pub_id="59">        <status>On</status>        <application>MAS Technology</application>        <title>Recent Highlights on Photolytic Oligonucleotide Array in situ Synthesis</title>        <journal>Nucleosides Nucleotides Nucleic Acids</journal>        <issue>2005;24(5-7):891-6</issue>        <pubdate>2005-01-01</pubdate>        <epubdate>2005-01-01</epubdate>        <url>http://dx.doi.org/10.1081/NCN-200059241</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>Light directed synthesis of high-density oligonucleotide microarrays is currently performed using either ortho-nitro-benzyl-type [MeNPOC] (Pease, A.C.; Solas, D.; Sullivan, E.J.; Cronin, T.M.; Holmes, C.P.; Fodor, S.P.A. Proc. Natl. Acad Sci U.SA. 1994, 91, 6333.) or ortho-nitrophenylethyl-type [NPPOC] (Hasan, A.; Stengele, K.P.; Giegrich, H.; Cornwell, P.; Isham, K.R.; Sachleben, R.A.; Pfleiderer, W.; Foote, R.S. Tetrahedron 1997, 53, 424Z) protecting groups as the 5'-O-carbonate ester of the phosphoramidite building block. The synthesis cycle uses a combinatorial approach attaching one specific base per cycle, thus as many as 100 cycles need to be run to make an array of 25-mers. Time needed for deprotection/activation of the growing oligo chain determines overall manufacturing time and consequently also cost. In this report we demonstrate the development of photoprotected posphoramidite monomers for light directed array synthesis with increasing sensitivity to the UV light used. If combined with maskless array synthesis, this technology allows for synthesis of arrays with &gt;780,000 different 25-mer oligonucleotides in about one hour and allows for high flexibility in array design and reiterative redesign. The arrays synthesized show high quality and reproducibility in our standard hybridization based assay.</abstract>        <author>            <id>1</id>            <author_shortname>Stengele KP</author_shortname>            <author_fullname>Stengele KP</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Buhler J</author_shortname>            <author_fullname>B&#252;hler J</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Buhler S</author_shortname>            <author_fullname>B&#252;hler S</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Kvassiouk E</author_shortname>            <author_fullname>Kvassiouk E</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Green R</author_shortname>            <author_fullname>Green R</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Prykota T</author_shortname>            <author_fullname>Prykota T</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Pfleiderer W</author_shortname>            <author_fullname>and Pfleiderer W</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>NimbleGen Systems GbmH, Waldkreiburg, Germany</institution>    </publication>    <publication pub_id="60">        <status>On</status>        <application>MAS Technology</application>        <title>More efficient photolithographic synthesis of DNA-chips by photosensitization</title>        <journal>Nucleosides Nucleotides Nucleic Acids</journal>        <issue>2003 May-Aug;22(5-8):1395-8</issue>        <pubdate>2003-05-01</pubdate>        <epubdate>2003-05-01</epubdate>        <url>http://dx.doi.org/10.1002/chin.200407256</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>This article does not have an abstract.</abstract>        <author>            <id>1</id>            <author_shortname>Woll D</author_shortname>            <author_fullname>D. W&#246;ll</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Walbert S</author_shortname>            <author_fullname>S. Walbert</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Stengele KP</author_shortname>            <author_fullname>Klaus-Peter Stengele</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Green R</author_shortname>            <author_fullname>R. Green</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Albert T</author_shortname>            <author_fullname>T. Albert</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Pfleiderer W</author_shortname>            <author_fullname>W. Pfleiderer</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Steiner UE</author_shortname>            <author_fullname>U. E. Steiner</author_fullname>            <author_affiliation>1,4</author_affiliation>        </author>        <institution>University of Konstanz, Konstanz, Germany</institution>        <institution>Chemogenix GmbH, Pleiskirchen, Germany</institution>        <institution>Nimblegen Systems Inc., Madison, Wisconsin, USA</institution>        <institution>Universit&#228;t Konstanz, Fachbereich Chemie, Universit&#228;tsstrasse 10, 78457, Konstanz, Germany</institution>    </publication>    <publication pub_id="61">        <status>On</status>        <application>MAS Technology</application>        <title>Light-directed 5'--&gt;3' synthesis of complex oligonucleotide microarrays</title>        <journal>Nucleic Acids Res.</journal>        <issue>2003 Apr 1;31(7):e35</issue>        <pubdate>2003-04-01</pubdate>        <epubdate>2003-04-01</epubdate>        <url>http://dx.doi.org/10.1093/nar/gng035</url>        <url_pdf>http://nar.oxfordjournals.org/cgi/reprint/31/7/e35</url_pdf>        <url_supplemental></url_supplemental>        <abstract>Light-directed synthesis of high-density microarrays is currently performed in the 3'-&gt;5' direction due to constraints in existing synthesis chemistry. This results in the probes being unavailable for many common types of enzymatic modification. Arrays that are synthesized in the 5'-&gt;3' direction could be utilized to perform parallel genotyping and resequencing directly on the array surface, dramatically increasing the throughput and reducing the cost relative to existing techniques. In this report we demonstrate the use of photoprotected phosphoramidite monomers for light-directed array synthesis in the 5'-&gt;3' direction, using maskless array synthesis technology. These arrays have a dynamic range of &gt;2.5 orders of magnitude, sensitivity below 1 pM and a coefficient of variance of &lt;10% across the array surface. Arrays containing &gt;150 000 probe sequences were hybridized to labeled mouse cRNA producing highly concordant data (average R2 = 0.998). We have also shown that the 3' ends of array probes are available for sequence-specific primer extension and ligation reactions.</abstract>        <author>            <id>1</id>            <author_shortname>Albert TJ</author_shortname>            <author_fullname>Thomas J. Albert</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Norton J</author_shortname>            <author_fullname>Jason Norton</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Ott M</author_shortname>            <author_fullname>Markus Ott</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Richmond T</author_shortname>            <author_fullname>Todd Richmond</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Nuwaysir K</author_shortname>            <author_fullname>Kate Nuwaysir</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Nuwaysir EF</author_shortname>            <author_fullname>Emile F. Nuwaysir</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Stengele KP</author_shortname>            <author_fullname>Klaus-Peter Stengele</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Green RD</author_shortname>            <author_fullname>Roland D. Green</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>NimbleGen Systems Inc., One Science Court, Madison, WI 53711, USA</institution>        <institution>Chemogenix GmbH, Beuthenerstrasse 2, D-84478 Waldkraiburg, Germany</institution>    </publication>    <publication pub_id="62">        <status>On</status>        <application>MAS Technology</application>        <title>New Types of Very Efficient Photolabile Protecting Groups Based upon the [2-(2-Nitrophenyl)propoxy]carbonyl (NPPOC) Moiety</title>        <journal>Helvetica Chimica Acta</journal>        <issue>87, 620-635 (2004)</issue>        <pubdate>2004-03-25</pubdate>        <epubdate>2004-03-25</epubdate>        <url>http://dx.doi.org/10.1002/hlca.200490060</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>Based upon the photolabile [2-(2-nitrophenyl)propoxy]carbonyl group (NPPOC), a large number of modified 2-(2-nitrophenyl)propanol derivatives substituted at the phenyl ring (see 23-34 and 57-76) as well as at the side-chain (see 85-92 and 95-98) were synthesized to improve the photoreactivity of this new type of photolabile entity. The phenyl moiety was also exchanged by the naphthalenyl group (see 102, 103, 105, 108, 110, 113, and 114), the thienyl substituent (see 115, 117, 118, and 120), and the benzothienyl substituent (see 121). The 2-(2-nitroaryl- and heteroaryl)propanols were converted with diphosgene into the corresponding carbonochloridates, which reacted subsequently with thymidine to the thymidine 5-(protected carbonates) 123-178 as the main reaction products. In several cases, the corresponding 3-carbonates and 3,5-dicarbonates 179-212 were also isolated and characterized. Photolysis studies under standardized conditions (see Table) indicated that the rate of photocleavage varies in a broad range depending on the substituents. So far, the thymidine 5-[2-(5-halo-2-nitrophenyl)propyl carbonates] 127-129, 5-[2-(nitro[1,1-biphenyl]3-yl)propyl carbonates] 136-139, 5-{2-[2-nitro-5-(thianthren-1-yl)phenyl]propyl carbonate} (140), 5-[2-(5-naphthalenyl-2-nitrophenyl)propyl carbonates] 141 and 142, and 5-[2-(2-nitro-5-thienylphenyl)propyl carbonates] 143 and 144 showed the best properties regarding fast and uniform deprotection. Since the nucleobases of 213-215 do not influence the photocleavage features, in general, the new type of photolabile building blocks allows in form of their 3-phosphoramidites the photolithographic formation of high-quality biochips.</abstract>        <author>            <id>1</id>            <author_shortname>B&#252;hler S</author_shortname>            <author_fullname>Sigrid B&#252;hler</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Lagoja I</author_shortname>            <author_fullname>Irene Lagoja</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Giegrich H</author_shortname>            <author_fullname>Heiner Giegrich</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Stengele KP</author_shortname>            <author_fullname>Klaus-Peter Stengele</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Pfleiderer W</author_shortname>            <author_fullname>Wolfgang Pfleiderer</author_fullname>            <author_affiliation>1,3</author_affiliation>        </author>        <institution>Fachbereich Chemie, Universit&#228;t Konstanz, Postfach 5560, D-78457 Konstanz</institution>        <institution>Chemogenix, Beuthener Str. 2, D-84478 Waldkraiburg</institution>        <institution>Correspondence to Wolfgang Pfleiderer</institution>    </publication>    <publication pub_id="63">        <status>On</status>        <application>MAS Technology</application>        <title>Maskless fabrication of light-directed oligonucleotide microarrays using a digital micromirror array</title>        <journal>Nat. Biotechnol.</journal>        <issue>1999 Oct;17(10):974-8</issue>        <pubdate>1999-10-01</pubdate>        <epubdate>1999-10-01</epubdate>        <url>http://dx.doi.org/10.1038/13664</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>Oligonucleotide microarrays, also called "DNA chips," are currently made by a light-directed chemistry that requires a large number of photolithographic masks for each chip. Here we describe a maskless array synthesizer (MAS) that replaces the chrome masks with virtual masks generated on a computer, which are relayed to a digital micromirror array. A 1:1 reflective imaging system forms an ultraviolet image of the virtual mask on the active surface of the glass substrate, which is mounted in a flow cell reaction chamber connected to a DNA synthesizer. Programmed chemical coupling cycles follow light exposure, and these steps are repeated with different virtual masks to grow desired oligonucleotides in a selected pattern. This instrument has been used to synthesize oligonucleotide microarrays containing more than 76,000 features measuring 16 microm 2. The oligonucleotides were synthesized at high repetitive yield and, after hybridization, could readily discriminate single-base pair mismatches. The MAS is adaptable to the fabrication of DNA chips containing probes for thousands of genes, as well as any other solid-phase combinatorial chemistry to be performed in high-density microarrays.</abstract>        <author>            <id>1</id>            <author_shortname>Singh-Gasson S</author_shortname>            <author_fullname>Singh-Gasson S</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Green RD</author_shortname>            <author_fullname>Green RD</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Yue Y</author_shortname>            <author_fullname>Yue Y</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Nelson C</author_shortname>            <author_fullname>Nelson C</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Blattner F</author_shortname>            <author_fullname>Blattner F</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Sussman MR</author_shortname>            <author_fullname>Sussman MR</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Cerrina F</author_shortname>            <author_fullname>Cerrina F</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Center for NanoTechnology, Department of Electrical and Computer Engineering, University of Wisconsin, Madison, WI</institution>    </publication>    <publication pub_id="64">        <status>On</status>        <application>Bioinformatics</application>        <title>Evaluating machine learning approaches for aiding probe selection for gene-expression arrays</title>        <journal>Bioinformatics</journal>        <issue>2002;18 Suppl 1:S164-71</issue>        <pubdate>2002-03-27</pubdate>        <epubdate>2002-03-27</epubdate>        <url>http://bioinformatics.oxfordjournals.org/cgi/content/abstract/18/suppl_1/S164</url>        <url_pdf>http://bioinformatics.oxfordjournals.org/cgi/reprint/18/suppl_1/S164</url_pdf>        <url_supplemental></url_supplemental>        <abstract>Motivation: Microarrays are a fast and cost-effective method of performing thousands of DNA hybridization experiments simultaneously. DNA probes are typically used to measure the expression level of specific genes. Because probes greatly vary in the quality of their hybridizations, choosing good probes is a difficult task. If one could accurately choose probes that are likely to hybridize well, then fewer probes would be needed to represent each gene in a gene-expression microarray, and, hence, more genes could be placed on an array of a given physical size. Our goal is to empirically evaluate how successfully three standard machine-learning algorithms—naïve Bayes, decision trees, and artificial neural networks—can be applied to the task of predicting good probes. Fortunately it is relatively easy to get training examples for such a learning task: place various probes on a gene chip, add a sample where the corresponding genes are highly expressed, and then record how well each probe measures the presence of its corresponding gene. With such training examples, it is possible that an accurate predictor of probe quality can be learned. Results: Two of the learning algorithms we investigate—naïve Bayes and neural networks—learn to predict probe quality surprisingly well. For example, in the top ten predicted probes for a given gene not used for training, on average about five rank in the top 2.5% of that gene's hundreds of possible probes. Decision-tree induction and the simple approach of using predicted melting temperature to rank probes perform significantly worse than these two algorithms. The features we use to represent probes are very easily computed and the time taken to score each candidate probe after training is minor. Training the naïve Bayes algorithm takes very little time, and while it takes over 10 times as long to train a neural network, that time is still not very substantial (on the order of a few hours on a desktop workstation). We also report the information contained in the features we use to describe the probes. We find the fraction of cytosine in the probe to be the most informative feature. We also find, not surprisingly, that the nucleotides in the middle of the probes sequence are more informative than those at the ends of the sequence.</abstract>        <author>            <id>1</id>            <author_shortname>Tobler JB</author_shortname>            <author_fullname>J. B. Tobler</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Molla MN</author_shortname>            <author_fullname>M.N. Molla</author_fullname>            <author_affiliation>1,3</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Nuwaysir EF</author_shortname>            <author_fullname>E.F. Nuwaysir</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Green RD</author_shortname>            <author_fullname>R.D. Green</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Shavlik JW</author_shortname>            <author_fullname>J.W. Shavlik</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <institution>Department of Computer Science, University of Wisconsin, 1210 West Dayton Street, Madison, WI 53706</institution>        <institution>Departments of Computer Science, and Biostatistics and Medical Informatics, University of Wisconsin, 1210 West Dayton Street, Madison, WI 53706</institution>        <institution>NimbleGen Systems, Inc., One Science Ct., Madison, WI 53711</institution>    </publication>    <publication pub_id="65">        <status>On</status>        <application>ENCODE</application>        <title>Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project</title>        <journal>Science</journal>        <issue>2007 Jun 14;447(7146):799-816.</issue>        <pubdate>2007-06-14</pubdate>        <epubdate>2007-06-14</epubdate>        <url>http://dx.doi.org/10.1038/nature05874</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>We report the generation and analysis of functional data from multiple, diverse experiments performed on a targeted 1% of the human genome as part of the pilot phase of the ENCODE Project. These data have been further integrated and augmented by a number of evolutionary and computational analyses. Together, our results advance the collective knowledge about human genome function in several major areas. First, our studies provide convincing evidence that the genome is pervasively transcribed, such that the majority of its bases can be found in primary transcripts, including non-protein-coding transcripts, and those that extensively overlap one another. Second, systematic examination of transcriptional regulation has yielded new understanding about transcription start sites, including their relationship to specific regulatory sequences and features of chromatin accessibility and histone modification. Third, a more sophisticated view of chromatin structure has emerged, including its inter-relationship with DNA replication and transcriptional regulation. Finally, integration of these new sources of information, in particular with respect to mammalian evolution based on inter- and intra-species sequence comparisons, has yielded new mechanistic and evolutionary insights concerning the functional landscape of the human genome. Together, these studies are defining a path for pursuit of a more comprehensive characterization of human genome function.</abstract>    </publication>    <publication pub_id="66">        <status>On</status>        <application>ROMA</application>        <title>Mouse genomic representational oligonucleotide microarray analysis: Detection of copy number variations in normal and tumor specimens</title>        <journal>PNAS</journal>        <issue>2006 Jul 25;103(30):11234-9. Epub 2006 Jul 14.</issue>        <pubdate>2006-07-25</pubdate>        <epubdate>2006-07-14</epubdate>        <url>http://dx.doi.org/10.1073/pnas.0602984103</url>        <url_pdf>http://www.pnas.org/cgi/reprint/103/30/11234</url_pdf>        <url_supplemental></url_supplemental>        <abstract>Genomic amplifications and deletions, the consequence of somatic variation, are a hallmark of human cancer. Such variation has also been observed between "normal" individuals, as well as in individuals with congenital disorders. Thus, copy number measurement is likely to be an important tool for the analysis of genetic variation, genetic disease, and cancer. We developed representational oligonucleotide microarray analysis, a high-resolution comparative genomic hybridization methodology, with this aim in mind, and reported its use in the study of humans. Here we report the development of a representational oligonucleotide microarray analysis microarray for the genomic analysis of the mouse, an important model system for many genetic diseases and cancer. This microarray was designed based on the sequence assembly MM3, and contains {approx}84,000 probes randomly distributed throughout the mouse genome. We demonstrate the use of this array to identify copy number changes in mouse cancers, as well to determine copy number variation between inbred strains of mice. Because restriction endonuclease digestion of genomic DNA is an integral component of our method, differences due to polymorphisms at the restriction enzyme cleavage sites are also observed between strains, and these can be useful to follow the inheritance of loci between crosses of different strains.</abstract>        <author>            <id>1</id>            <author_shortname>Lakshmi B</author_shortname>            <author_fullname>B. Lakshmi</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Hall IM</author_shortname>            <author_fullname>Ira M. Hall</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Egan C</author_shortname>            <author_fullname>Christopher Egan</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Alexander J</author_shortname>            <author_fullname>Joan Alexander</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Leotta A</author_shortname>            <author_fullname>Anthony Leotta</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Healy J</author_shortname>            <author_fullname>John Healy</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Zender L</author_shortname>            <author_fullname>Lars Zender</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Spector MS</author_shortname>            <author_fullname>Mona S. Spector</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Xue W</author_shortname>            <author_fullname>Wen Xue</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Lowe SW</author_shortname>            <author_fullname>Scott W. Lowe</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Wigler M</author_shortname>            <author_fullname>Michael Wigler</author_fullname>            <author_affiliation>1,4</author_affiliation>        </author>        <author>            <id>12</id>            <author_shortname>Lucito R</author_shortname>            <author_fullname>Robert Lucito</author_fullname>            <author_affiliation>1,4</author_affiliation>        </author>        <institution>Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724</institution>        <institution>606 Fairfax Avenue, Apartment A, Norfolk, VA 23507</institution>        <institution>Helicos BioSciences, One Kendall Square, Building 200, Cambridge, MA 02139</institution>        <institution>To whom correspondence may be addressed. E-mail: wigler@cshl.edu or lucito@cshl.edu</institution>    </publication>    <publication pub_id="67">        <status>On</status>        <application>ROMA</application>        <title>Large-Scale Copy Number Polymorphism in the Human Genome</title>        <journal>Science</journal>        <issue>2004 Jul 23;305(5683):525-8</issue>        <pubdate>2004-07-23</pubdate>        <epubdate>2004-07-23</epubdate>        <url>http://dx.doi.org/10.1126/science.1098918</url>        <url_pdf></url_pdf>        <url_supplemental>http://www.sciencemag.org/cgi/content/full/sci;305/5683/525/DC1</url_supplemental>        <abstract>The extent to which large duplications and deletions contribute to human genetic variation and diversity is unknown. Here, we show that large-scale copy number polymorphisms (CNPs) (about 100 kilobases and greater) contribute substantially to genomic variation between normal humans. Representational oligonucleotide microarray analysis of 20 individuals revealed a total of 221 copy number differences representing 76 unique CNPs. On average, individuals differed by 11 CNPs, and the average length of a CNP interval was 465 kilobases. We observed copy number variation of 70 different genes within CNP intervals, including genes involved in neurological function, regulation of cell growth, regulation of metabolism, and several genes known to be associated with disease.</abstract>        <author>            <id>1</id>            <author_shortname>Sebat J</author_shortname>            <author_fullname>Jonathan Sebat</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Lakshmi B</author_shortname>            <author_fullname>B. Lakshmi</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Troge J</author_shortname>            <author_fullname>Jennifer Troge</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Alexander J</author_shortname>            <author_fullname>Joan Alexander</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Young J</author_shortname>            <author_fullname>Janet Young</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Lundin P</author_shortname>            <author_fullname>P&#228;r Lundin</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Maner S</author_shortname>            <author_fullname>Susanne M&#229;n&#233;r</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Massa H</author_shortname>            <author_fullname>Hillary Massa</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Walker M</author_shortname>            <author_fullname>Megan Walker</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Chi M</author_shortname>            <author_fullname>Maoyen Chi</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Navin N</author_shortname>            <author_fullname>Nicholas Navin</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>12</id>            <author_shortname>Lucito R</author_shortname>            <author_fullname>Robert Lucito</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>13</id>            <author_shortname>Healy J</author_shortname>            <author_fullname>John Healy</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>14</id>            <author_shortname>Hicks J</author_shortname>            <author_fullname>James Hicks</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>15</id>            <author_shortname>Ye K</author_shortname>            <author_fullname>Kenny Ye</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>16</id>            <author_shortname>Reiner A</author_shortname>            <author_fullname>Andrew Reiner</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>17</id>            <author_shortname>Gilliam TC</author_shortname>            <author_fullname>T. Conrad Gilliam</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>18</id>            <author_shortname>Trask B</author_shortname>            <author_fullname>Barbara Trask</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>19</id>            <author_shortname>Patterson N</author_shortname>            <author_fullname>Nick Patterson</author_fullname>            <author_affiliation>6</author_affiliation>        </author>        <author>            <id>20</id>            <author_shortname>Zetterberg A</author_shortname>            <author_fullname>Anders Zetterberg</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>21</id>            <author_shortname>Wigler M</author_shortname>            <author_fullname>Michael Wigler</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA</institution>        <institution>Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA</institution>        <institution>Karolinska Institute, Stockholm SE-17176, Sweden</institution>        <institution>Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA</institution>        <institution>Columbia Genome Center, Columbia University, New York, NY 10032, USA</institution>        <institution>Broad Institute, Cambridge, MA 02139, USA</institution>    </publication>    <publication pub_id="68">        <status>On</status>        <application>ROMA</application>        <title>Representational Oligonucleotide Microarray Analysis: A High-Resolution Method to Detect Genome Copy Number Variation</title>        <journal>Genome Res.</journal>        <issue>2003 Oct;13(10):2291-305. Epub 2003 Sep 15</issue>        <pubdate>2003-10-01</pubdate>        <epubdate>2003-09-15</epubdate>        <url>http://dx.doi.org/10.1101/gr.1349003</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>We have developed a methodology we call ROMA (representational oligonucleotide microarray analysis), for the detection of the genomic aberrations in cancer and normal humans. By arraying oligonucleotide probes designed from the human genome sequence, and hybridizing with &quot;representations&quot; from cancer and normal cells, we detect regions of the genome with altered &quot;copy number.&quot; We achieve an average resolution of 30 kb throughout the genome, and resolutions as high as a probe every 15 kb are practical. We illustrate the characteristics of probes on the array and accuracy of measurements obtained using ROMA. Using this methodology, we identify variation between cancer and normal genomes, as well as between normal human genomes. In cancer genomes, we readily detect amplifications and large and small homozygous and hemizygous deletions. Between normal human genomes, we frequently detect large (100 kb to 1 Mb) deletions or duplications. Many of these changes encompass known genes. ROMA will assist in the discovery of genes and markers important in cancer, and the discovery of loci that may be important in inherited predispositions to disease.</abstract>        <author>            <id>1</id>            <author_shortname>Lucito R</author_shortname>            <author_fullname>Robert Lucito</author_fullname>            <author_affiliation>1,5</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Healy J</author_shortname>            <author_fullname>John Healy</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Alexander J</author_shortname>            <author_fullname>Joan Alexander</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Reiner A</author_shortname>            <author_fullname>Andrew Reiner</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Esposito D</author_shortname>            <author_fullname>Diane Esposito</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Chi M</author_shortname>            <author_fullname>Maoyen Chi</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Rodgers L</author_shortname>            <author_fullname>Linda Rodgers</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Brady A</author_shortname>            <author_fullname>Amy Brady</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Sebat J</author_shortname>            <author_fullname>Jonathan Sebat</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Troge J</author_shortname>            <author_fullname>Jennifer Troge</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>West JA</author_shortname>            <author_fullname>Joseph A. West</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>12</id>            <author_shortname>Rostan S</author_shortname>            <author_fullname>Seth Rostan</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>13</id>            <author_shortname>Nguyen KC</author_shortname>            <author_fullname>Ken C.Q. Nguyen</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>14</id>            <author_shortname>Powers S</author_shortname>            <author_fullname>Scott Powers</author_fullname>            <author_affiliation>1,2</author_affiliation>        </author>        <author>            <id>15</id>            <author_shortname>Ye KQ</author_shortname>            <author_fullname>Kenneth Q. Ye</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>16</id>            <author_shortname>Olshen A</author_shortname>            <author_fullname>Adam Olshen</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>17</id>            <author_shortname>Venkatraman E</author_shortname>            <author_fullname>Ennapadam Venkatraman</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>18</id>            <author_shortname>Norton L</author_shortname>            <author_fullname>Larry Norton</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>19</id>            <author_shortname>Wigler M</author_shortname>            <author_fullname>Michael Wigler</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA</institution>        <institution>Tularik Inc., Genomics Division, Greenlawn, New York 11740, USA</institution>        <institution>Department of Applied Math and Statistics, SUNY at Stony Brook, Stony Brook, New York 11794, USA</institution>        <institution>Memorial Sloan-Kettering Cancer Center, New York, New York 10021, USA</institution>    </publication>    <publication pub_id="69">        <status>On</status>        <application>ChIP-chip</application>        <title>Comparison of sample preparation methods for ChIP-chip assays</title>        <journal>BioTechniques</journal>        <issue>41:577-580 (November 2006)</issue>        <pubdate>2006-11-01</pubdate>        <epubdate>2006-11-01</epubdate>        <url>http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pubmed&amp;pubmedid=17140114</url>        <url_pdf></url_pdf>        <url_supplemental>http://www.biotechniques.com/supplemental/OGeenSupl415.pdf</url_supplemental>        <abstract>A single chromatin immunoprecipitation (ChIP) sample does not provide enough DNA for hybridization to a genomic tiling array. A commonly used technique for amplifying the DNA obtained from ChIP assays is ligation-mediated PCR (LM-PCR). However, using this amplification method, we could not identify Oct4 binding sites on genomic tiling arrays representing 1% of the human genome (ENCODE arrays). In contrast, hybridization of a pool of 10 ChIP samples to the arrays produced reproducible binding patterns and low background signals. However, the pooling method would greatly increase the number of ChIP reactions needed to analyze the entire human genome. Therefore, we have adapted the GenomePlex&#194;&#174; whole genome amplification (WGA) method for use in ChIP-chip assays; detailed ChIP and amplification protocols used for these analyses are provided as supplementary material. When applied to ENCODE arrays, the products prepared using this new method resulted in an Oct4 binding pattern similar to that from the pooled Oct4 ChIP samples. Importantly, the signal-to-noise ratio using the GenomePlex WGA method is superior to the LM-PCR amplification method.</abstract>        <author>            <id>1</id>            <author_shortname>O'Geen H</author_shortname>            <author_fullname>Henriette O'Geen</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Nicolet CM</author_shortname>            <author_fullname>Charles M. Nicolet</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Blahnik K</author_shortname>            <author_fullname>Kim Blahnik</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Green R</author_shortname>            <author_fullname>Roland Green</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Farnham PJ</author_shortname>            <author_fullname>Peggy J. Farnham</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>University of California-Davis, Davis, CA</institution>        <institution>NimbleGen Systems Inc., Madison, WI, USA</institution>    </publication>    <publication pub_id="70">        <status>On</status>        <application>ChIP-chip</application>        <title>Proximal genomic localization of STAT1 binding and regulated transcriptional activity</title>        <journal>BMC Genomics</journal>        <issue>2006; 7: 254. Published online 2006 October 11</issue>        <pubdate>2006-10-11</pubdate>        <epubdate>2006-10-11</epubdate>        <url>http://dx.doi.org/10.1186/1471-2164-7-254</url>        <url_pdf>http://www.pubmedcentral.nih.gov/picrender.fcgi?artid=1618399&amp;blobtype=pdf</url_pdf>        <url_supplemental></url_supplemental>        <abstract>Background Signal transducer and activator of transcription (STAT) proteins are key regulators of gene expression in response to the interferon (IFN) family of anti-viral and anti-microbial cytokines. We have examined the genomic relationship between STAT1 binding and regulated transcription using multiple tiling microarray and chromatin immunoprecipitation microarray (ChIP-chip) experiments from public repositories. Results In response to IFN-&#195;&#8240;&#194;&#161;, STAT1 bound proximally to regions of the genome that exhibit regulated transcriptional activity. This finding was consistent between different tiling microarray platforms, and between different measures of transcriptional activity, including differential binding of RNA polymerase II, and differential mRNA transcription. Re-analysis of tiling microarray data from a recent study of IFN-&#195;&#8240;&#194;&#161;-induced STAT1 ChIP-chip and mRNA expression revealed that STAT1 binding is tightly associated with localized mRNA transcription in response to IFN-&#195;&#8240;&#194;&#161;. Close relationships were also apparent between STAT1 binding, STAT2 binding, and mRNA transcription in response to IFN-&#195;&#8240;&#195;&#184;. Furthermore, we found that sites of STAT1 binding within the Encyclopedia of DNA Elements (ENCODE) region are precisely correlated with sites of either enhanced or diminished binding by the RNA polymerase II complex. Conclusion Together, our results indicate that STAT1 binds proximally to regions of the genome that exhibit regulated transcriptional activity. This finding establishes a generalized basis for the positioning of STAT1 binding sites within the genome, and supports a role for STAT1 in the direct recruitment of the RNA polymerase II complex to the promoters of IFN-&#195;&#8240;&#194;&#161;-responsive genes.</abstract>        <author>            <id>1</id>            <author_shortname>Wormald S</author_shortname>            <author_fullname>Samuel Wormald</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Hilton DJ</author_shortname>            <author_fullname>Douglas J Hilton</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Smyth GK</author_shortname>            <author_fullname>Gordon K Smyth</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Speed TP</author_shortname>            <author_fullname>Terence P Speed</author_fullname>            <author_affiliation>1,3</author_affiliation>        </author>        <institution>Division of Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia</institution>        <institution>Division of Molecular Medicine, The Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia</institution>        <institution>Department of Statistics, University of California, Berkeley, California, USA</institution>    </publication>    <publication pub_id="71">        <status>On</status>        <application>CGS</application>        <title>Comparative genome sequencing of Escherichia coli allows observation of bacterial evolution on a laboratory timescale</title>        <journal>Nat. Genet.</journal>        <issue>2006 Nov 5; [Epub ahead of print]</issue>        <pubdate>2006-11-05</pubdate>        <epubdate>2006-11-05</epubdate>        <url>http://dx.doi.org/10.1038/ng1906</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>We applied whole-genome resequencing of Escherichia coli to monitor the acquisition and fixation of mutations that conveyed a selective growth advantage during adaptation to a glycerol-based growth medium. We identified 13 different de novo mutations in five different E. coli strains and monitored their fixation over a 44-d period of adaptation. We obtained proof that the observed spontaneous mutations were responsible for improved fitness by creating single, double and triple site-directed mutants that had growth rates matching those of the evolved strains. The success of this new genome-scale approach indicates that real-time evolution studies will now be practical in a wide variety of contexts.</abstract>        <author>            <id>1</id>            <author_shortname>Herring CD</author_shortname>            <author_fullname>Christopher D Herring</author_fullname>            <author_affiliation>1,6,7</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Raghunathan A</author_shortname>            <author_fullname>Anu Raghunathan</author_fullname>            <author_affiliation>1,6,7</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Honisch C</author_shortname>            <author_fullname>Christiane Honisch</author_fullname>            <author_affiliation>2,7</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Patel T</author_shortname>            <author_fullname>Trina Patel</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Applebee MK</author_shortname>            <author_fullname>Kenyon Applebee</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Joyce AR</author_shortname>            <author_fullname>Andrew R Joyce</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Albert TJ</author_shortname>            <author_fullname>Thomas J Albert</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Blattner FR</author_shortname>            <author_fullname>Frederick R Blattner</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>van den Boom D</author_shortname>            <author_fullname>Dirk van den Boom</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Cantor CR</author_shortname>            <author_fullname>Charles R Cantor</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Palsson BO</author_shortname>            <author_fullname>Bernhard &#216; Palsson</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Department of Bioengineering,</institution>        <institution>Bioinformatics Program, and</institution>        <institution>Department of Chemistry and Biochemistry, University of California, San Diego, California 92093, USA.</institution>        <institution>Nimblegen Systems, Inc., Madison, Wisconsin 53711, USA.</institution>        <institution>Laboratory of Genetics, University of Wisconsin, Madison, Wisconsin 53706, USA.</institution>        <institution>Current addresses: Mascoma Corporation, Lebanon, New Hampshire 03766, USA (C.D.H.); Division of Infectious Diseases, Mt. Sinai School of Medicine, New York, New York, 10029, USA (A.R.).</institution>        <institution>These authors contributed equally to this work. Correspondence should be addressed to B.&#216;.P. (bpalsson@bioeng.ucsd.edu).</institution>    </publication>    <publication pub_id="72">        <status>On</status>        <application>DNase Hypersensitivity</application>        <title>Identifying gene regulatory elements by genomic microarray mapping of DNaseI hypersensitive sites</title>        <journal>Genome Res.</journal>        <issue>Oct 2006; 16: 1310 - 1319</issue>        <pubdate>2006-09-08</pubdate>        <epubdate>2006-09-08</epubdate>        <url>http://dx.doi.org/10.1101/gr.5373606</url>        <url_pdf></url_pdf>        <url_supplemental>http://www.genome.org/cgi/content/full/gr.5373606/DC1</url_supplemental>        <abstract>The identification of cis-regulatory elements is central to understanding gene transcription. Hypersensitivity of cis-regulatory elements to digestion with DNaseI remains the gold-standard approach to locating such elements. Traditional methods used to identify DNaseI hypersensitive sites are cumbersome and can only be applied to short stretches of DNA at defined locations. Here we report the development of a novel genomic array-based approach to DNaseI hypersensitive site mapping (ADHM) that permits precise, large-scale identification of such sites from as few as 5 million cells. Using ADHM we identified all previously recognized hematopoietic regulatory elements across 200 kb of the mouse T-cell acute lymphocytic leukemia-1 (Tal1) locus, and, in addition, identified two novel elements within the locus, which show transcriptional regulatory activity. We further validated the ADHM protocol by mapping the DNaseI hypersensitive sites across 250 kb of the human TAL1 locus in CD34+ primary stem/progenitor cells and K562 cells and by mapping the previously known DNaseI hypersensitive sites across 240 kb of the human alpha-globin locus in K562 cells. ADHM provides a powerful approach to identifying DNaseI hypersensitive sites across large genomic regions.</abstract>        <author>            <id>1</id>            <author_shortname>Follows GA</author_shortname>            <author_fullname>George A. Follows</author_fullname>            <author_affiliation>1,4</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Dhami P</author_shortname>            <author_fullname>Pawan Dhami</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Gottgens B</author_shortname>            <author_fullname>Berthold G&#246;ttgens</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Bruce AW</author_shortname>            <author_fullname>Alexander W. Bruce</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Campbell PJ</author_shortname>            <author_fullname>Peter J. Campbell</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Dillon SC</author_shortname>            <author_fullname>Shane C. Dillon</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Smith AM</author_shortname>            <author_fullname>Aileen M. Smith</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Koch C</author_shortname>            <author_fullname>Christoph Koch</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Donaldson IJ</author_shortname>            <author_fullname>Ian J. Donaldson</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Scott MA</author_shortname>            <author_fullname>Mike A. Scott</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Dunham I</author_shortname>            <author_fullname>Ian Dunham</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>12</id>            <author_shortname>Janes ME</author_shortname>            <author_fullname>Mary E. Janes</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>13</id>            <author_shortname>Vetrie D</author_shortname>            <author_fullname>David Vetrie</author_fullname>            <author_affiliation>2,3</author_affiliation>        </author>        <author>            <id>14</id>            <author_shortname>Green AR</author_shortname>            <author_fullname>Anthony R. Green</author_fullname>            <author_affiliation>1,3</author_affiliation>        </author>        <institution>Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 2XY, United Kingdom</institution>        <institution>Human Genetics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, United Kingdom</institution>        <institution>These authors contributed equally to this work.</institution>        <institution>Corresponding author.</institution>    </publication>		<publication pub_id="73">        <status>On</status>        <application>DNA Methylation</application>        <title>Genome-wide analysis of Arabidopsis thaliana DNA methylation uncovers an interdependence between methylation and transcription</title>        <journal>Nat. Genet.</journal>        <issue>39, 61 - 69 (2006). Epub 2006 Nov 26.</issue>        <pubdate>2006-11-26</pubdate>        <epubdate>2006-11-26</epubdate>        <url>http://dx.doi.org/10.1038/ng1929</url>        <url_pdf></url_pdf>        <url_supplemental>http://www.nature.com/ng/journal/v39/n1/suppinfo/ng1929_S1.html</url_supplemental>        <abstract>Cytosine methylation, a common form of DNA modification that antagonizes transcription, is found at transposons and repeats in vertebrates, plants and fungi. Here we have mapped DNA methylation in the entire Arabidopsis thaliana genome at high resolution. DNA methylation covers transposons and is present within a large fraction of A. thaliana genes. Methylation within genes is conspicuously biased away from gene ends, suggesting a dependence on RNA polymerase transit. Genic methylation is strongly influenced by transcription: moderately transcribed genes are most likely to be methylated, whereas genes at either extreme are least likely. In turn, transcription is influenced by methylation: short methylated genes are poorly expressed, and loss of methylation in the body of a gene leads to enhanced transcription. Our results indicate that genic transcription and DNA methylation are closely interwoven processes.</abstract>        <author>            <id>1</id>            <author_shortname>Zilberman D</author_shortname>            <author_fullname>Daniel Zilberman</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Gehring M</author_shortname>            <author_fullname>Mary Gehring</author_fullname>            <author_affiliation>1,2</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Tran RK</author_shortname>            <author_fullname>Robert K Tran1</author_fullname>            <author_affiliation>1,2,3</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Ballinger T</author_shortname>            <author_fullname>Tracy Ballinger</author_fullname>            <author_affiliation>1,2</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Henikoff S</author_shortname>            <author_fullname>Steven Henikoff</author_fullname>            <author_affiliation>1,2</author_affiliation>        </author>        <institution>Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, Washington 98109, USA</institution>        <institution>Howard Hughes Medical Institute</institution>        <institution>Present address: University of California Davis Genome Center, 451 E. Health Sciences Drive, Davis, California 95616, USA</institution>    </publication>		<publication pub_id="74">        <status>On</status>        <application>CGH</application>        <title>A high resolution map of segmental DNA copy number variation in the mouse genome</title>        <journal>PLoS Genet.</journal>        <issue>2007 Jan 5;3(1):e3. Epub 2006 Nov 22.</issue>        <pubdate>2007-01-05</pubdate>        <epubdate>2007-01-05</epubdate>        <url>http://dx.doi.org/10.1371/journal.pgen.0030003</url>				<url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>Submicroscopic (less than 2 Mb) segmental DNA copy number changes are a recently recognized source of genetic variability between individuals. The biological consequences of copy number variants (CNVs) are largely undefined. In some cases, CNVs that cause gene dosage effects have been implicated in phenotypic variation. CNVs have been detected in diverse species, including mice and humans. Published studies in mice have been limited by resolution and strain selection. We chose to study 21 well-characterized inbred mouse strains that are the focus of an international effort to measure, catalog, and disseminate phenotype data. We performed comparative genomic hybridization using long oligomer arrays to characterize CNVs in these strains. This technique increased the resolution of CNV detection by more than an order of magnitude over previous methodologies. The CNVs range in size from 21 to 2,002 kb. Clustering strains by CNV profile recapitulates aspects of the known ancestry of these strains. Most of the CNVs (77.5%) contain annotated genes, and many (47.5%) colocalize with previously mapped segmental duplications in the mouse genome. We demonstrate that this technique can identify copy number differences associated with known polymorphic traits. The phenotype of previously uncharacterized strains can be predicted based on their copy number at these loci. Annotation of CNVs in the mouse genome combined with sequence-based analysis provides an important resource that will help define the genetic basis of complex traits.</abstract>        <author>            <id>1</id>            <author_shortname>Graubert T</author_shortname>            <author_fullname>Timothy Graubert</author_fullname>            <author_affiliation>1,6</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Cahan P</author_shortname>            <author_fullname>Patrick Cahan</author_fullname>            <author_affiliation>1,7</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Edwin D</author_shortname>            <author_fullname>Deepa Edwin</author_fullname>            <author_affiliation>1,7</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Selzer R</author_shortname>            <author_fullname>Rebecca Selzer</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Richmond T</author_shortname>            <author_fullname>Todd Richmond</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Eis P</author_shortname>            <author_fullname>Peggy Eis</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Shannon W</author_shortname>            <author_fullname>William Shannon</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Li X</author_shortname>            <author_fullname>Xia Li</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>McLeod H</author_shortname>            <author_fullname>Howard McLeod</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Cheverud JM</author_shortname>            <author_fullname>James M Cheverud</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Ley T</author_shortname>            <author_fullname>Timothy Ley</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <institution>Department of Medicine, Division of Oncology, Stem Cell Biology Section, Washington University, St. Louis, Missouri, United States of America</institution>        <institution>NimbleGen Systems, Inc., Madison, Wisconsin, United States of America</institution>        <institution>Department of Medicine, Division of General Medical Science, Washington University, St. Louis, Missouri, United States of America</institution>        <institution>Department of Medicine, Division of Oncology, Molecular Oncology Section, Washington University, St. Louis, Missouri, United States of America</institution>        <institution>Department of Anatomy and Neurobiology, Washington University, St. Louis, Missouri, United States of America</institution>        <institution>To whom correspondence should be addressed. E-mail: graubert@medicine.wustl.edu</institution>        <institution>These authors contributed equally to this work.</institution>    </publication>	<publication pub_id="75">        <status>On</status>        <application>CGS</application>        <title>Tracking the Evolution of the SARS Coronavirus Using High-Throughput, High-Density Resequencing Arrays</title>        <journal>Genome Res.</journal>        <issue>14:398-405, 2004</issue>        <pubdate>2004-03-01</pubdate>        <epubdate>2004-03-01</epubdate>        <url>http://dx.doi.org/10.1101/gr.2141004</url>        <url_pdf>http://www.genome.org/cgi/reprint/14/3/398</url_pdf>        <url_supplemental>http://www.genome.org/cgi/content/full/14/3/398/DC1</url_supplemental>        <abstract>Mutations in the SARS-Coronavirus (SARS-CoV) can alter its clinical presentation, and the study of its mutation patterns in human populations can facilitate contact tracing. Here, we describe the development and validation of an oligonucleotide resequencing array for interrogating the entire 30-kb SARS-CoV genome in a rapid, cost-effective fashion. Using this platform, we sequenced SARS-CoV genomes from Vero cell culture isolates of 12 patients and directly from four patient tissues. The sequence obtained from the array is highly reproducible, accurate (&#62;99.99% accuracy) and capable of identifying known and novel variants of SARS-CoV. Notably, we applied this technology to a field specimen of probable SARS and rapidly deduced its infectious source. We demonstrate that array-based resequencing-by-hybridization is a fast, reliable, and economical alternative to capillary sequencing for obtaining SARS-CoV genomic sequence on a population scale, making this an ideal platform for the global monitoring of SARS-CoV and other small-genome pathogens.</abstract>        <author>            <id>1</id>            <author_shortname>Wong CW</author_shortname>            <author_fullname>Christopher W. Wong</author_fullname>            <author_affiliation>1,4</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Albert TJ</author_shortname>            <author_fullname>Thomas J. Albert</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Vega VB</author_shortname>            <author_fullname>Vinsensius B. Vega</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Norton JE</author_shortname>            <author_fullname>Jason E. Norton</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Cutler DJ</author_shortname>            <author_fullname>David J. Cutler</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Richmond TA</author_shortname>            <author_fullname>Todd A. Richmond</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Stanton LW</author_shortname>            <author_fullname>Lawrence W. Stanton</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Liu ET</author_shortname>            <author_fullname>Edison T. Liu</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Miller LD</author_shortname>            <author_fullname>Lance D. Miller</author_fullname>            <author_affiliation>1,4</author_affiliation>        </author>        <institution>Genome Institute of Singapore, Singapore 138672, Republic of Singapore</institution>        <institution>NimbleGen Systems, Inc., Madison, Wisconsin 53711, USA</institution>        <institution>Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA</institution>        <institution>Corresponding authors.E-MAIL wongc@gis.a-star.edu.sg; FAX +65-64789060. E-MAIL millerl@gis.a-star.edu.sg; FAX +65-64789060.</institution>    </publication>	<publication pub_id="76">        <status>On</status>        <application>CGH</application>        <title>Palladin Mutation Causes Familial Pancreatic Cancer and Suggests a New Cancer Mechanism</title>        <journal>PLoS Medicine</journal>        <issue>2006 Dec;3(12):e516.</issue>        <pubdate>2006-12-12</pubdate>        <epubdate>2006-12-12</epubdate>        <url>http://dx.doi.org/10.1371/journal.pmed.0030516</url>        <url_pdf>http://medicine.plosjournals.org/perlserv/?request=get-pdf&#38;file=10.1371_journal.pmed.0030516-L.pdf</url_pdf>        <url_supplemental></url_supplemental>        <abstract>Background<br/>Pancreatic cancer is a deadly disease. Discovery of the mutated genes that cause the inherited form(s) of the disease may shed light on the mechanism(s) of oncogenesis. Previously we isolated a susceptibility locus for familial pancreatic cancer to chromosome location 4q32–34. In this study, our goal was to discover the identity of the familial pancreatic cancer gene on 4q32 and determine the function of that gene.<br/>Methods and Findings<br/>A customized microarray of the candidate chromosomal region affecting pancreatic cancer susceptibility revealed the greatest expression change in palladin (PALLD), a gene that encodes a component of the cytoskeleton that controls cell shape and motility. A mutation causing a proline (hydrophobic) to serine (hydrophilic) amino acid change (P239S) in a highly conserved region tracked with all affected family members and was absent in the non-affected members. The mutational change is not a known single nucleotide polymorphism. Palladin RNA, measured by quantitative RT-PCR, was overexpressed in the tissues from precancerous dysplasia and pancreatic adenocarcinoma in both familial and sporadic disease. Transfection of wild-type and P239S mutant palladin gene constructs into HeLa cells revealed a clear phenotypic effect: cells expressing P239S palladin exhibited cytoskeletal changes, abnormal actin bundle assembly, and an increased ability to migrate.<br/>Conclusions<br/>These observations suggest that the presence of an abnormal palladin gene in familial pancreatic cancer and the overexpression of palladin protein in sporadic pancreatic cancer cause cytoskeletal changes in pancreatic cancer and may be responsible for or contribute to the tumor's strong invasive and migratory abilities.</abstract>        <author>            <id>1</id>            <author_shortname>Pogue-Geile KL</author_shortname>            <author_fullname>Kay L. Pogue-Geile</author_fullname>            <author_affiliation>1,9,10</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Chen R</author_shortname>            <author_fullname>Ru Chen</author_fullname>            <author_affiliation>2,9</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Bronner MP</author_shortname>            <author_fullname>Mary P. Bronner</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Crnogorac-Jurcevic TC</author_shortname>            <author_fullname>Tatjana Crnogorac-Jurcevic</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Moyes KM</author_shortname>            <author_fullname>Kara White Moyes</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Dowen S</author_shortname>            <author_fullname>Sally Dowen</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Otey CA</author_shortname>            <author_fullname>Carol A. Otey</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Crispin DA</author_shortname>            <author_fullname>David A. Crispin</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>George RD</author_shortname>            <author_fullname>Ryan D. George</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Whitcomb DC</author_shortname>            <author_fullname>David C. Whitcomb</author_fullname>            <author_affiliation>1,6,7</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Brentnall TA</author_shortname>            <author_fullname>Teresa A. Brentnall</author_fullname>            <author_affiliation>2,8</author_affiliation>        </author>        <institution>Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America</institution>        <institution>Department of Medicine, University of Washington, Seattle, Washington, United States of America</institution>        <institution>Department of Pathology, Cleveland Clinic Foundation, Cleveland, Ohio, United States of America</institution>        <institution>Molecular Oncology Unit, Cancer Research United Kingdom, Barts and the London School of Medicine and Dentistry, London, United Kingdom</institution>        <institution>Department of Cell and Molecular Physiology, University of North Carolina at Chapel Hill, North Carolina, United States of America</institution>        <institution>Department of Cell Biology and Physiology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America</institution>        <institution>Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America</institution>        <institution>To whom correspondence should be addressed. E-mail: teribr@u.washington.edu</institution>        <institution>These authors contributed equally to this work.</institution>        <institution>Current address: Division of Pathology, National Surgical Breast and Bowel Project, Pittsburgh, Pennsylvania, United States of America</institution>    </publication>		<publication pub_id="77">        <status>On</status>        <application>ChIP-chip</application>        <title>Prospero Acts as a Binary Switch between Self-Renewal and Differentiation in Drosophila Neural Stem Cells</title>        <journal>Dev. Cell</journal>        <issue>Vol 11, 775-789, December 2006</issue>        <pubdate>2006-12-04</pubdate>        <epubdate>2006-12-04</epubdate>        <url>http://dx.doi.org/10.1016/j.devcel.2006.09.015</url>        <url_pdf>http://download.developmentalcell.com/pdfs/1534-5807/PIIS1534580706004084.pdf</url_pdf>        <url_supplemental>http://www.developmentalcell.com/cgi/content/full/11/6/775/DC1/</url_supplemental>        <abstract>Stem cells have the remarkable ability to give rise to both self-renewing and differentiating daughter cells. Drosophila neural stem cells segregate cell-fate determinants from the self-renewing cell to the differentiating daughter at each division. Here, we show that one such determinant, the homeodomain transcription factor Prospero, regulates the choice between stem cell self-renewal and differentiation. We have identified the in vivo targets of Prospero throughout the entire genome. We show that Prospero represses genes required for self-renewal, such as stem cell fate genes and cell-cycle genes. Surprisingly, Prospero is also required to activate genes for terminal differentiation. We further show that in the absence of Prospero, differentiating daughters revert to a stem cell-like fate: they express markers of self-renewal, exhibit increased proliferation, and fail to differentiate. These results define a blueprint for the transition from stem cell self-renewal to terminal differentiation.</abstract>        <author>            <id>1</id>            <author_shortname>Choksi SP</author_shortname>            <author_fullname>Semil P. Choksi</author_fullname>            <author_affiliation>1,2,4</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Southall TD</author_shortname>            <author_fullname>Tony D. Southall</author_fullname>            <author_affiliation>1,4</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Bossing T</author_shortname>            <author_fullname>Torsten Bossing</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Edoff K</author_shortname>            <author_fullname>Karin Edoff</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>de Wit E</author_shortname>            <author_fullname>Elzo de Wit</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Fischer BE</author_shortname>            <author_fullname>Bettina E. Fischer</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>van Steensel B</author_shortname>            <author_fullname>Bas van Steensel</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Micklem G</author_shortname>            <author_fullname>Gos Micklem</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Brand AH</author_shortname>            <author_fullname>Andrea H. Brand</author_fullname>            <author_affiliation>1,5</author_affiliation>        </author>        <institution>The Gurdon Institute and Department of Physiology, Development and Neuroscience, University of Cambridge, Tennis Court Road, Cambridge CB2 1QN, United Kingdom</institution>        <institution>Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, United Kingdom</institution>        <institution>Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands</institution>        <institution>These authors contributed equally to this work.</institution>        <institution>Corresponding author. Andrea H. Brand: ahb@mole.bio.cam.ac.uk</institution>    </publication>	<publication pub_id="78">        <status>On</status>        <application>ChIP-chip</application>        <title>Identification of genes directly regulated by the oncogene ZNF217 using ChIP-chip assays</title>        <journal>J. Biol. Chem.</journal>        <issue>2007 Mar 30;282(13):9703-12. Epub 2007 Jan 26</issue>        <pubdate>2007-03-30</pubdate>        <epubdate>2007-01-26</epubdate>        <url>http://dx.doi.org/10.1074/jbc.M611752200</url>        <url_pdf>http://www.jbc.org/cgi/reprint/M611752200v1.pdf</url_pdf>        <url_supplemental></url_supplemental>        <abstract>It has been proposed that ZNF217, which is amplified at 20q13 in various tumors, plays a key role during neoplastic transformation. ZNF217 has been purified in complexes that contain repressor proteins such as CtBP2, suggesting that it acts as a transcriptional repressor. However, the function of ZNF217 has not been well characterized due to a lack of known target genes. Using a global ChIP-chip approach, we have identified thousands of ZNF217 binding sites in three tumor cell lines (MCF7, SW480, and Ntera2). Further analysis of ZNF217 in Ntera2 cells has shown that many promoters are bound by ZNF217 and CtBP2, and that a subset of these promoters are activated upon removal of ZNF217. Thus, our in vivo studies corroborate the in vitro biochemical analyses of ZNF217-containing complexes and support the hypothesis that ZNF217 functions as a transcriptional repressor. Gene ontology analysis shows that ZNF217 targets in Ntera2 cells are involved in organ development, suggesting that one function of ZNF217 may be to repress differentiation. Accordingly, we show that differentiation of Ntera2 cells with retinoic acid leads to down-regulation of ZNF217. Our identification of thousands of ZNF217 target genes will enable further studies of the consequences of aberrant expression of ZNF217 during neoplastic transformation.</abstract>        <author>            <id>1</id>            <author_shortname>Krig SR</author_shortname>            <author_fullname>Sheryl R. Krig</author_fullname>            <author_affiliation>1,4</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Jin VX</author_shortname>            <author_fullname>Victor X. Jin</author_fullname>            <author_affiliation>1,4</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Bieda MC</author_shortname>            <author_fullname>Mark C. Bieda</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>O&#8217;Geen H</author_shortname>            <author_fullname>Henriette O&#8217;Geen</author_fullname>            <author_affiliation>1</author_affiliation>        </author>		<author>            <id>5</id>            <author_shortname>Yaswen P</author_shortname>            <author_fullname>Paul Yaswen</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Green R</author_shortname>            <author_fullname>Roland Green</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Farnham PJ</author_shortname>            <author_fullname>Peggy J. Farnham</author_fullname>            <author_affiliation>1,5</author_affiliation>        </author>        <institution>Department of Pharmacology and the Genome Center, University of California-Davis, Davis, CA 95616</institution>        <institution>NimbleGen Systems Inc, Madison, WI</institution>        <institution>Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720</institution>        <institution>Co-first authors</institution>        <institution>Address correspondance to: Peggy J. Farnham, Department of Pharmacology and the Genome Center, University of California-Davis, One Shields Avenue, Davis, CA 95616; Tel (530) 754-4988; Fax (530) 752-0436; pjfarnham@ucdavis.edu</institution>    </publication>	<publication pub_id="79">        <status>On</status>        <application>ChIP-chip</application>        <title>1,25-Dihydroxyvitamin D3 Regulates the Expression of Low-Density Lipoprotein Receptor-Related Protein 5 via Deoxyribonucleic Acid Sequence Elements Located Downstream of the Start Site of Transcription</title>        <journal>Mol. Endocrinol.</journal>        <issue>2006 Sep;20(9):2215-30. Epub 2006 Apr 13.</issue>        <pubdate>2006-09-01</pubdate>        <epubdate>2006-04-13</epubdate>        <url>http://dx.doi.org/10.1210/me.2006-0102</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>The skeleton is a direct target of vitamin D action, where the hormone modulates the proliferation of osteoblast precursors, their differentiation into mature osteoblasts, and their functional activity. Some of these effects of vitamin D are reminiscent of those orchestrated by the Wnt signaling pathway wherein stimulation of the membrane receptor Frizzled and its coreceptor LRP5 leads to activation of ß-catenin and subsequent transcription-mediated changes in osteoblast biology. Indeed, LRP5 is now known to play a particularly important role in bone formation such that the loss of this component results in a reduction in osteoblast number, a delay in mineralization, and a reduction in peak bone mineral density. Interestingly, we discovered during the course of a vitamin D receptor (VDR) chromatin immunoprecipitation/DNA microarray analysis that 1,25-(OH)2D3 could induce binding of the VDR to sites within the Lrp5 gene locus. VDR and retinoid X receptor binding was evident both in primary osteoblasts as well as in osteoblasts of cell line origin. Importantly, this interaction between 1,25-(OH)2D3-activated VDR and the Lrp5 gene led to both a modification in chromatin structure within the Lrp5 locus and the induction of Lrp5 mRNA transcripts in vivo as well as in vitro. One of these sites within the Lrp5 locus was discovered to confer vitamin D response to a heterologous promoter when introduced into osteoblastic cells, permitting both the identification and characterization of the vitamin D response element located within. Interestingly, additional studies revealed that whereas the regulatory region in the mouse Lrp5 gene was highly conserved in the human genome, the vitamin D response element was not. Our studies show that 1,25-(OH)2D3 can enhance the expression of a critical component of the Wnt signaling pathway that is known to impact osteogenesis.</abstract>        <author>            <id>1</id>            <author_shortname>Fretz JA</author_shortname>            <author_fullname>Jackie A. Fretz</author_fullname>            <author_affiliation></author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Zella LA</author_shortname>            <author_fullname>Lee A. Zella</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Kim S</author_shortname>            <author_fullname>Sungtae Kim</author_fullname>            <author_affiliation>1</author_affiliation>        </author>		<author>            <id>4</id>            <author_shortname>Shevde NK</author_shortname>            <author_fullname>Nirupama K. Shevde</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Pike JW</author_shortname>            <author_fullname>J. Wesley Pike</author_fullname>            <author_affiliation>1,2</author_affiliation>        </author>        <institution>Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706</institution>        <institution>Address all correspondence and requests for reprints to: J. Wesley Pike, Department of Biochemistry, University of Wisconsin-Madison, 433 Babcock Drive, Madison, Wisconsin 53706. E-mail: pike@biochem.wisc.edu.</institution>    </publication>	<publication pub_id="80">        <status>On</status>        <application>ChIP-chip</application>        <title>Enhancers Located within Two Introns of the Vitamin D Receptor Gene Mediate Transcriptional Autoregulation by 1,25-Dihydroxyvitamin D<sub>3</sub></title>        <journal>Mol. Endocrinol.</journal>        <issue>2006 Jun;20(6):1231-47</issue>        <pubdate>2006-06-01</pubdate>        <epubdate>2006-02-23</epubdate>        <url>http://dx.doi.org/10.1210/me.2006-0015</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>The biological actions of 1,25-(OH)2D3 are mediated by the vitamin D receptor (VDR), a protein that binds to target genes and alters their expression. 1,25-(OH)2D3 is also capable of inducing transcription of the VDR gene itself. In the present study, we explored both the capacity of 1,25-(OH)2D3 to induce VDR gene expression in bone cells and the mechanism instrumental to this up-regulation. After establishing the ability of 1,25-(OH)2D3 to stimulate VDR mRNA up-regulation both in bone in vivo and in osteoblastic cells, we screened the mouse VDR gene locus from 20 kb upstream of the gene’s transcriptional start site (TSS) to 10 kb downstream of the final exon to identify VDR binding sites using chromatin immunoprecipitation-DNA microarray (ChIP-chip) analysis. Three conserved regions were identified 20, 27, and 29 kb downstream of the TSS. VDR binding to these sites in response to 1,25-(OH)2D3 was confirmed by ChIP analysis and was accompanied by differential localization of retinoid X receptor, histone acetylation, and RNA polymerase II recruitment. One of these regions was able to confer 1,25-(OH)2D3 regulation to downstream promoters, thereby permitting identification and characterization of the regulatory element located within. Importantly, a highly conserved region within the human VDR gene analogous to that discovered in the mouse was also capable of mediating 1,25-(OH)2D3 response. Our results demonstrate that 1,25-(OH)2D3 and its receptor autoregulate the expression of the VDR gene. The location of these regulatory regions and their apparent distances from the TSS are consistent with new findings suggesting the emerging relevance of distant enhancers.</abstract>        <author>            <id>1</id>            <author_shortname>Zella LA</author_shortname>            <author_fullname>Lee A. Zella</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Kim S</author_shortname>            <author_fullname>Sungtae Kim</author_fullname>            <author_affiliation>1</author_affiliation>        </author>		<author>            <id>3</id>            <author_shortname>Shevde NK</author_shortname>            <author_fullname>Nirupama K. Shevde</author_fullname>            <author_affiliation>1</author_affiliation>        </author>		<author>            <id>4</id>            <author_shortname>Pike JW</author_shortname>            <author_fullname>J. Wesley Pike</author_fullname>            <author_affiliation>1,2</author_affiliation>        </author>        <institution>Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706</institution>        <institution>Address all correspondence and requests for reprints to: J. Wesley Pike, Department of Biochemistry, University of Wisconsin-Madison, 433 Babcock Drive, Madison, Wisconsin 53706. E-mail: pike@biochem.wisc.edu.</institution>    </publication>		<publication pub_id="81">        <status>On</status>        <application>ChIP-chip</application>        <title>Activation of Receptor Activator of NF-&#954;B Ligand Gene Expression by 1,25-Dihydroxyvitamin D3 Is Mediated through Multiple Long-Range Enhancers</title>        <journal> Mol. Cell Biol.</journal>        <issue>2006 Sep;26(17):6469-86</issue>        <pubdate>2006-09-01</pubdate>        <epubdate>2006-09-01</epubdate>        <url>http://dx.doi.org/10.1128/MCB.00353-06</url>        <url_pdf>http://mcb.asm.org/cgi/reprint/26/17/6469.pdf</url_pdf>        <url_supplemental>http://mcb.asm.org/cgi/content/full/26/17/6469/DC1</url_supplemental>        <abstract>RANKL is a tumor necrosis factor (TNF)-like factor secreted by mesenchymal cells, osteoblast derivatives, and T cells that is essential for osteoclastogenesis. In osteoblasts, RANKL expression is regulated by two major calcemic hormones, 1,25-dihydroxyvitamin D3 [1,25(OH)2D3] and parathyroid hormone (PTH), as well as by several inflammatory/osteoclastogenic cytokines; the molecular mechanisms for this regulation are unclear. To identify such mechanisms, we screened a DNA microarray which tiled across the entire mouse RankL gene locus at a 50-bp resolution using chromatin immunoprecipitation (ChIP)-derived DNA precipitated with antibodies to the vitamin D receptor (VDR) and the retinoid X receptor (RXR). Five sites of dimer interaction were observed on the RankL gene centered at 16, 22, 60, 69, and 76 kb upstream of the TSS. These regions contained binding sites for not only VDR and RXR, but also the glucocorticoid receptor (GR). The most distant of these regions, termed the distal control region (RL-DCR), conferred both VDR-dependent 1,25(OH)2D3 and GR-dependent glucocorticoid (GC) responses. We mapped these activities to an unusual but functionally active vitamin D response element and to several potential GC response elements located over a more extensive region within the RL-DCR. An evolutionarily conserved region within the human RANKL gene contained a similar vitamin D response element and exhibited an equivalent behavior. Importantly, hormonal activation of the RankL gene was also associated with chromatin modification and RNA polymerase II recruitment. Our studies demonstrate that regulation of RankL gene expression by 1,25(OH)2D3 is complex and mediated by at least five distal regions, one of which contains a specific element capable of mediating direct transcriptional activation.</abstract>        <author>            <id>1</id>            <author_shortname>Kim S</author_shortname>            <author_fullname>Sungtae Kim</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Yamazaki M</author_shortname>            <author_fullname>Miwa Yamazaki</author_fullname>            <author_affiliation>1</author_affiliation>        </author>		<author>            <id>3</id>            <author_shortname>Zella LA</author_shortname>            <author_fullname>Lee A. Zella</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Shevde NK</author_shortname>            <author_fullname>Nirupama K. Shevde</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Pike JW</author_shortname>            <author_fullname>J. Wesley Pike</author_fullname>            <author_affiliation>1,2</author_affiliation>        </author>        <institution>Department of Biochemistry, University of Wisconsin—Madison, Madison, Wisconsin 53706</institution>        <institution>Corresponding author. Mailing address: Department of Biochemistry, University of Wisconsin—Madison, 433 Babcock Dr., Madison, WI 53706. Phone: (608) 262-8229. Fax: (608) 263-7609. E-mail: pike@biochem.wisc.edu.</institution>	</publication>	<publication pub_id="82">        <status>On</status>        <application>ChIP-chip</application>        <title>Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome</title>        <journal>Nat. Genet.</journal>        <issue>2007 Mar;39(3):311-8. Epub 2007 Feb 4.</issue>        <pubdate>2007-02-04</pubdate>        <epubdate>2007-02-04</epubdate>        <url>http://dx.doi.org/10.1038/ng1966</url>        <url_pdf></url_pdf>        <url_supplemental>http://www.nature.com/ng/journal/v39/n3/suppinfo/ng1966_S1.html</url_supplemental>        <abstract>Eukaryotic gene transcription is accompanied by acetylation and methylation of nucleosomes near promoters, but the locations and roles of histone modifications elsewhere in the genome remain unclear. We determined the chromatin modification states in high resolution along 30 Mb of the human genome and found that active promoters are marked by trimethylation of Lys4 of histone H3 (H3K4), whereas enhancers are marked by monomethylation, but not trimethylation, of H3K4. We developed computational algorithms using these distinct chromatin signatures to identify new regulatory elements, predicting over 200 promoters and 400 enhancers within the 30-Mb region. This approach accurately predicted the location and function of independently identified regulatory elements with high sensitivity and specificity and uncovered a novel functional enhancer for the carnitine transporter SLC22A5 (OCTN2). Our results give insight into the connections between chromatin modifications and transcriptional regulatory activity and provide a new tool for the functional annotation of the human genome.</abstract>        <author>            <id>1</id>            <author_shortname>Heintzman ND</author_shortname>            <author_fullname>Nathaniel D. Heintzman</author_fullname>            <author_affiliation>1,2</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Stuart RK</author_shortname>            <author_fullname>Rhona K. Stuart</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Hon G</author_shortname>            <author_fullname>Gary Hon</author_fullname>            <author_affiliation>1,3</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Fu Y</author_shortname>            <author_fullname>Yutao Fu</author_fullname>            <author_affiliation></author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Ching CW</author_shortname>            <author_fullname>Christina W. Ching</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Hawkins RD</author_shortname>            <author_fullname>R. David Hawkins</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Barrera LO</author_shortname>            <author_fullname>Leah O. Barrera</author_fullname>            <author_affiliation>3</author_affiliation>        </author>		<author>            <id>8</id>            <author_shortname>Van Calcar S</author_shortname>            <author_fullname>Sara Van Calcar</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Qu C</author_shortname>            <author_fullname>Chunxu Qu</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Ching KA</author_shortname>            <author_fullname>Keith A. Ching</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Wang W</author_shortname>            <author_fullname>Wei Wang</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>12</id>            <author_shortname>Weng Z</author_shortname>            <author_fullname>Zhiping Weng</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>13</id>            <author_shortname>Green RD</author_shortname>            <author_fullname>Roland D. Green</author_fullname>            <author_affiliation>7</author_affiliation>        </author>        <author>            <id>14</id>            <author_shortname>Crawford GE</author_shortname>            <author_fullname>Gregory E. Crawford</author_fullname>            <author_affiliation>8</author_affiliation>        </author>        <author>            <id>15</id>            <author_shortname>Ren B</author_shortname>            <author_fullname>Bing Ren</author_fullname>            <author_affiliation>1,9,10</author_affiliation>        </author>        <institution>Ludwig Institute for Cancer Research, University of California San Diego (UCSD) School of Medicine, 9500 Gilman Drive, La Jolla, California 92093-0653 USA.</institution>        <institution>Biomedical Sciences Graduate Program, University of California San Diego (UCSD) School of Medicine, 9500 Gilman Drive, La Jolla, California 92093-0653 USA.</institution>        <institution>Program in Bioinformatics and University of California San Diego (UCSD) School of Medicine, 9500 Gilman Drive, La Jolla, California 92093-0653 USA.</institution>        <institution>Bioinformatics Program, Boston University, 24 Cummington Street, 1002, Boston, Massachusetts 02215 USA.</institution>        <institution>Department of Chemistry and Biochemistry, UCSD, 9500 Gilman Drive, La Jolla, California 92093 USA.</institution>        <institution>Biomedical Engineering Department, Boston University, 44 Cummington Street, Boston, MA 02215.</institution>        <institution>NimbleGen Systems, Inc., 1 Science Court, Madison, Wisconsin 53711 USA.</institution>        <institution>Institute for Genome Sciences &#38; Policy and Department of Pediatrics, Duke University, 101 Science Drive, Durham, North Carolina 27708, USA.</institution>        <institution>Department of Cellular and Molecular Medicine, University of California San Diego (UCSD) School of Medicine, 9500 Gilman Drive, La Jolla, California 92093-0653 USA.</institution>        <institution>Correspondence should be addressed to Bing Ren biren@ucsd.edu</institution>    </publication>	<publication pub_id="83">        <status>On</status>        <application>CGH</application>        <title>Efficient high-resolution deletion discovery in Caenorhabditis elegans by array comparative genomic hybridization</title>        <journal>Genome Res.</journal>        <issue>2007 Mar;17(3):337-47. Epub 2007 Jan 31.</issue>        <pubdate>2007-03-17</pubdate>        <epubdate>2007-01-31</epubdate>        <url>http://dx.doi.org/10.1101/gr.5690307</url>        <url_pdf></url_pdf>        <url_supplemental>http://www.genome.org/cgi/content/full/gr.5690307/DC1</url_supplemental>        <abstract>We have developed array Comparative Genomic Hybridization for Caenorhabditis elegans as a means of screening for novel induced deletions in this organism. We designed three microarrays consisting of overlapping 50-mer probes to annotated exons and micro-RNAs, the first with probes to chromosomes X and II, the second with probes to chromosome II alone, and a third to the entire genome. These arrays were used to reliably detect both a large (50 kb) multigene deletion and a small (1 kb) single-gene deletion in homozygous and heterozygous samples. In one case, a deletion breakpoint was resolved to fewer than 50 bp. In an experiment designed to identify new mutations we used the X:II and II arrays to detect deletions associated with lethal mutants on chromosome II. One is an 8-kb deletion targeting the ast-1 gene on chromosome II and another is a 141-bp deletion in the gene C06A8.1. Others span large sections of the chromosome, up to &#62;750 kb. As a further application of array Comparative Genomic Hybridization in C. elegans we used the whole-genome array to detect the extensive natural gene content variation (almost 2%) between the N2 Bristol strain and the strain CB4856, a strain isolated in Hawaii and JU258, a strain isolated in Madeira.</abstract>        <author>            <id>1</id>            <author_shortname>Maydan JS</author_shortname>            <author_fullname>Jason S. Maydan</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Flibotte S</author_shortname>            <author_fullname>Stephane Flibotte</author_fullname>            <author_affiliation>2</author_affiliation>        </author>		<author>            <id>3</id>            <author_shortname>Edgley ML</author_shortname>            <author_fullname>Mark L. Edgley</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Lau J</author_shortname>            <author_fullname>Joanne Lau</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Selzer RR</author_shortname>            <author_fullname>Rebecca R. Selzer</author_fullname>            <author_affiliation>5</author_affiliation>        </author>		<author>            <id>6</id>            <author_shortname>Richmond TA</author_shortname>            <author_fullname>Todd A. Richmond</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Pofahl NJ</author_shortname>            <author_fullname>Nathan J. Pofahl</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Thomas JH</author_shortname>            <author_fullname>James H. Thomas</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Moerman DG</author_shortname>            <author_fullname>Donald G. Moerman</author_fullname>            <author_affiliation>1,3,6</author_affiliation>        </author>        <institution>Department of Zoology, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada</institution>        <institution>Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z 4S6 Canada</institution>        <institution>Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada</institution>        <institution>Department of Genome Sciences, University of Washington, Seattle, Washington 98195-7730, USA</institution>        <institution>NimbleGen Systems Inc., Madison, Wisconsin 53711, USA</institution>        <institution>Corresponding author. E-mail moerman@zoology.ubc.ca; fax (604) 822-2416.</institution>	</publication>	<publication pub_id="84">        <status>On</status>        <application>ChIP-chip</application>        <title>Genome-wide mapping of ORC and Mcm2p binding sites on tiling arrays and identification of essential ARS consensus sequences in S. cerevisiae</title>        <journal>BMC Genomics</journal>        <issue>2006, 7:276</issue>        <pubdate>2006-10-26</pubdate>        <epubdate>2006-10-26</epubdate>        <url>http://dx.doi.org/10.1186/1471-2164-7-276</url>        <url_pdf>http://www.biomedcentral.com/content/pdf/1471-2164-7-276.pdf</url_pdf>        <url_supplemental>http://www.biomedcentral.com/1471-2164/7/276/additional/</url_supplemental>        <abstract>Background:Eukaryotic replication origins exhibit different initiation efficiencies and activation times within S-phase. Although local chromatin structure and function influences origin activity, the exact mechanisms remain poorly understood. A key to understanding the exact features of chromatin that impinge on replication origin function is to define the precise locations of the DNA sequences that control origin function. In S. cerevisiae, Autonomously Replicating Sequences (ARSs) contain a consensus sequence (ACS) that binds the Origin Recognition Complex (ORC) and is essential for origin function. However, an ACS is not sufficient for origin function and the majority of ACS matches do not function as ORC binding sites, complicating the specific identification of these sites. Results: To identify essential origin sequences genome-wide, we utilized a tiled oligonucleotide array (NimbleGen) to map the ORC and Mcm2p binding sites at high resolution. These binding sites define a set of potential Autonomously Replicating Sequences (ARSs), which we term nimARSs. The nimARS set comprises 529 ORC and/or Mcm2p binding sites, which includes 95% of known ARSs, and experimental verification demonstrates that 94% are functional. The resolution of the analysis facilitated identification of potential ACSs (nimACSs) within 370 nimARSs. Cross-validation shows that the nimACS predictions include 58% of known ACSs, and experimental verification indicates that 82% are essential for ARS activity. Conclusion: These findings provide the most comprehensive, accurate, and detailed mapping of ORC binding sites to date, adding to the emerging picture of the chromatin organization of the budding yeast genome.</abstract>        <author>            <id>1</id>            <author_shortname>Xu W</author_shortname>            <author_fullname>Weihong Xu</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Aparicio JG</author_shortname>            <author_fullname>Jennifer G. Aparicio</author_fullname>            <author_affiliation>1</author_affiliation>        </author>		<author>            <id>3</id>            <author_shortname>Aparicio OM</author_shortname>            <author_fullname>Oscar M. Aparicio</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Tavare S</author_shortname>            <author_fullname>Simon Tavar&#233;</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Molecular and Computational Biology Program, University of Southern California, Los Angeles, CA, USA</institution>    </publication>	<publication pub_id="85">        <status>On</status>        <application>ChIP-chip</application>        <title>High-throughput mapping of the chromatin structure of human promoters</title>        <journal>Nat. Biotechnol.</journal>        <issue>2007 Feb;25(2):244-8. Epub 2007 Jan 14.</issue>        <pubdate>2007-02-25</pubdate>        <epubdate>2007-01-14</epubdate>        <url>http://dx.doi.org/10.1038/nbt1279</url>        <url_pdf></url_pdf>        <url_supplemental>http://www.nature.com/nbt/journal/v25/n2/suppinfo/nbt1279_S1.html</url_supplemental>        <abstract>Our understanding of how chromatin structure influences cellular processes such as transcription and replication has been limited by a lack of nucleosome-positioning data in human cells. We describe a high-resolution microarray approach combined with an analysis algorithm to examine nucleosome positioning in 3,692 promoters within seven human cell lines. Unlike unexpressed genes without transcription-preinitiation complexes at their promoters, expressed genes or genes containing preinitiation complexes exhibit characteristic nucleosome-free regions at their transcription start sites. The combination of these nucleosome data with chromatin immunoprecipitation-chip analyses reveals that the melanocyte master regulator microphthalmia-associated transcription factor (MITF) predominantly binds nucleosome-free regions, supporting the model that nucleosomes limit sequence accessibility. This study presents a global view of human nucleosome positioning and provides a high-throughput tool for analyzing chromatin structure in development and disease.</abstract>        <author>            <id>1</id>            <author_shortname>Ozsolak F</author_shortname>            <author_fullname>Fatih Ozsolak</author_fullname>            <author_affiliation>1,4</author_affiliation>        </author>		<author>            <id>2</id>            <author_shortname>Song JS</author_shortname>            <author_fullname>Jun S. Song</author_fullname>            <author_affiliation>2,3,4</author_affiliation>        </author>		<author>            <id>3</id>            <author_shortname>Liu XS</author_shortname>            <author_fullname>X. Shirley Liu</author_fullname>            <author_affiliation>2,3,5</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Fisher DE</author_shortname>            <author_fullname>David E. Fisher</author_fullname>            <author_affiliation>1,5</author_affiliation>        </author>        <institution>Melanoma Program in Medical Oncology, and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Children's Hospital Boston, Harvard Medical School, 44 Binney Street, Boston, Massachusetts 02115, USA</institution>        <institution>Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 44 Binney Street, Boston, Massachusetts 02115, USA</institution>        <institution>Harvard School of Public Health, 677 Huntington Avenue, Boston, Massachusetts 02115, USA</institution>        <institution>These authors contributed equally to this work.</institution>        <institution>Correspondence should be addressed to David E Fisher david_fisher@dfci.harvard.edu or X Shirley Liu xsliu@jimmy.harvard.edu</institution>    </publication>	<publication pub_id="86">        <status>On</status>        <application>ChIP-chip</application>        <title>X chromosome repression by localization of the C. elegans dosage compensation machinery to sites of transcription initiation</title>        <journal>Nat. Genet.</journal>        <issue>39(3), 403 - 408 (2007)</issue>        <pubdate>2007-02-11</pubdate>        <epubdate>2007-02-11</epubdate>        <url>http://dx.doi.org/10.1038/ng1983</url>        		<url_pdf></url_pdf>        <url_supplemental>http://www.nature.com/ng/journal/v39/n3/suppinfo/ng1983_S1.html</url_supplemental>        <abstract>Among organisms with chromosome-based mechanisms of sex determination, failure to equalize expression of X-linked genes between the sexes is typically lethal. In C. elegans, XX hermaphrodites halve transcription from each X chromosome to match the output of XO males. Here, we mapped the binding location of the condensin homolog DPY-27 and the zinc finger protein SDC-3, two components of the C. elegans dosage compensation complex (DCC). We observed strong foci of DCC binding on X, surrounded by broader regions of localization. Binding foci, but not adjacent regions of localization, were distinguished by clusters of a 10-bp DNA motif, suggesting a recruitment-and-spreading mechanism for X recognition. The DCC was preferentially bound upstream of genes, suggesting modulation of transcriptional initiation and polymerase-coupled spreading. Stronger DCC binding upstream of genes with high transcriptional activity indicated a mechanism for tuning DCC activity at specific loci. These data aid in understanding how proteins involved in higher-order chromosome dynamics can regulate transcription at individual loci.</abstract>        <author>            <id>1</id>            <author_shortname>Ercan S</author_shortname>            <author_fullname>Sevinc Ercan</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Giresi PG</author_shortname>            <author_fullname>Paul G. Giresi</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Whittle CM</author_shortname>            <author_fullname>Christina M. Whittle</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Zhang X</author_shortname>            <author_fullname>Xinmin Zhang</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Green RD</author_shortname>            <author_fullname>Roland D. Green</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Lieb JD</author_shortname>            <author_fullname>Jason D. Lieb</author_fullname>            <author_affiliation>1,3</author_affiliation>        </author>        <institution>Department of Biology and Carolina Center for the Genome Sciences, 202 Fordham Hall, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-3280, USA</institution>        <institution>NimbleGen Systems, Inc., 1 Science Court, Madison, Wisconsin 53711, USA</institution>        <institution>Correspondence should be addressed to Jason D Lieb jlieb@bio.unc.edu</institution>    </publication>	<publication pub_id="87">        <status>On</status>        <application>ChIP-chip</application>        <title>The Transition between Transcriptional Initiation and Elongation in E. coli Is Highly Variable and Often Rate Limiting</title>        <journal>Mol. Cell.</journal>        <issue>2006 Dec 8;24(5):747-57</issue>        <pubdate>2006-12-07</pubdate>        <epubdate>2006-12-07</epubdate>        <url>http://dx.doi.org/10.1016/j.molcel.2006.10.030</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>We perform a genome-wide analysis of the transition between transcriptional initiation and elongation in Escherichia coli by determining the association of core RNA polymerase (RNAP) and the promoter-recognition factor &#963;70 with respect to RNA transcripts. We identify 1286 &#963;70-associated promoters, including many internal to known operons, and demonstrate that &#963;70 is usually released very rapidly from elongating RNAP complexes. On average, RNAP density is higher at the promoter than in the coding sequence, although the ratio is highly variable among different transcribed regions. Strikingly, a significant fraction of RNAP-bound promoters is not associated with transcriptional activity, perhaps due to an intrinsic energetic barrier to promoter escape. Thus, the transition from transcriptional initiation to elongation is highly variable, often rate limiting, and in some cases is essentially blocked such that RNAP is effectively &#8220;poised&#8221; to transcribe only under the appropriate environmental conditions. The genomic pattern of RNAP density in E. coli differs from that in yeast and mammalian cells.</abstract>        <author>            <id>1</id>            <author_shortname>Reppas NB</author_shortname>            <author_fullname>Nikos B. Reppas</author_fullname>            <author_affiliation>1,3,4</author_affiliation>        </author>		<author>            <id>2</id>            <author_shortname>Wade JT</author_shortname>            <author_fullname>Joseph T. Wade</author_fullname>            <author_affiliation>2,4</author_affiliation>        </author>		<author>            <id>3</id>            <author_shortname>Church GM</author_shortname>            <author_fullname>George M. Church</author_fullname>            <author_affiliation>3</author_affiliation>        </author>		<author>            <id>4</id>            <author_shortname>Struhl K</author_shortname>            <author_fullname>Kevin Struhl</author_fullname>            <author_affiliation>2,5</author_affiliation>        </author>		<institution>Harvard University Biophysics Program, Harvard Medical School, Boston, Massachusetts 02115</institution>        <institution>Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts 02115</institution>        <institution>Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115</institution>        <institution>These authors contributed equally to this work.</institution>        <institution>Corresponding author: kevin@hms.harvard.edu</institution>    </publication>	<publication pub_id="88">        <status>On</status>        <application>DNA Methylation</application>        <title>Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome</title>        <journal>Nat. Genet.</journal>        <issue>2007 Apr;39(4):457-66. Epub 2007 Mar 4.</issue>        <pubdate>2007-03-04</pubdate>        <epubdate>2007-03-04</epubdate>        <url>http://dx.doi.org/10.1038/ng1990</url>        <url_pdf></url_pdf>        <url_supplemental>http://www.nature.com/ng/journal/v39/n4/suppinfo/ng1990_S1.html</url_supplemental>		<abstract>To gain insight into the function of DNA methylation at cis-regulatory regions and its impact on gene expression, we measured methylation, RNA polymerase occupancy and histone modifications at 16,000 promoters in primary human somatic and germline cells. We find CpG-poor promoters hypermethylated in somatic cells, which does not preclude their activity. This methylation is present in male gametes and results in evolutionary loss of CpG dinucleotides, as measured by divergence between humans and primates. In contrast, strong CpG island promoters are mostly unmethylated, even when inactive. Weak CpG island promoters are distinct, as they are preferential targets for de novo methylation in somatic cells. Notably, most germline-specific genes are methylated in somatic cells, suggesting additional functional selection. These results show that promoter sequence and gene function are major predictors of promoter methylation states. Moreover, we observe that inactive unmethylated CpG island promoters show elevated levels of dimethylation of Lys4 of histone H3, suggesting that this chromatin mark may protect DNA from methylation.</abstract>        <author>            <id>1</id>            <author_shortname>Weber M</author_shortname>            <author_fullname>Michael Weber</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Hellmann I</author_shortname>            <author_fullname>Ines Hellmann</author_fullname>            <author_affiliation>2,3</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Stadler MB</author_shortname>            <author_fullname>Michael B. Stadler</author_fullname>            <author_affiliation>1</author_affiliation>        </author>		<author>            <id>4</id>            <author_shortname>Ramos L</author_shortname>            <author_fullname>Liliana Ramos</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>P&#228;bo S</author_shortname>            <author_fullname>Svante Päbo</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Rebhan M</author_shortname>            <author_fullname>Michael Rebhan</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Sch&#252;beler D</author_shortname>            <author_fullname>Dirk Schübeler</author_fullname>            <author_affiliation>1,5</author_affiliation>        </author>        <institution></institution>        <institution>Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, CH-4058 Basel, Switzerland.</institution>        <institution>Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, D-04103 Leipzig, Germany.</institution>        <institution>University of Copenhagen, Universitetsparken 15, Copenhagen, Denmark, 2100.</institution>        <institution>Department of Obstetrics and Gynaecology, Radboud University Nijmegen Medical Center, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands.</institution>        <institution>Correspondence should be addressed to Dirk Sch&#252;beler dirk@fmi.ch</institution>    </publication>	<publication pub_id="89">        <status>On</status>        <application>CGH</application>        <title>A comprehensive analysis of common copy-number variations in the human genome</title>        <journal>Am. J. Hum. Genet.</journal>        <issue>2007 Jan;80(1):91-104. Epub 2006 Dec 5.</issue>        <pubdate>2007-01-01</pubdate>        <epubdate>2006-12-05</epubdate>        <url>http://dx.doi.org/10.1086/510560</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>Segmental copy-number variations (CNVs) in the human genome are associated with developmental disorders and susceptibility to diseases. More importantly, CNVs may represent a major genetic component of our phenotypic diversity. In this study, using a whole-genome array comparative genomic hybridization assay, we identified 3,654 autosomal segmental CNVs, 800 of which appeared at a frequency of at least 3%. Of these frequent CNVs, 77% are novel. In the 95 individuals analyzed, the two most diverse genomes differed by at least 9 Mb in size or varied by at least 266 loci in content. Approximately 68% of the 800 polymorphic regions overlap with genes, which may reflect human diversity in senses (smell, hearing, taste, and sight), rhesus phenotype, metabolism, and disease susceptibility. Intriguingly, 14 polymorphic regions harbor 21 of the known human microRNAs, raising the possibility of the contribution of microRNAs to phenotypic diversity in humans. This in-depth survey of CNVs across the human genome provides a valuable baseline for studies involving human genetics.</abstract>        <author>            <id>1</id>            <author_shortname>Wong KK</author_shortname>            <author_fullname>Kendy K. Wong</author_fullname>            <author_affiliation>1,7</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>deLeeuw RJ</author_shortname>            <author_fullname>Ronald J. deLeeuw</author_fullname>            <author_affiliation>1,7</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Dosanjh NS</author_shortname>            <author_fullname>Nirpjit S. Dosanjh</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Kimm LR</author_shortname>            <author_fullname>Lindsey R. Kimm</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Cheng Z</author_shortname>            <author_fullname>Ze Cheng</author_fullname>            <author_affiliation>6</author_affiliation>        </author>		<author>            <id>6</id>            <author_shortname>Horsman DE</author_shortname>            <author_fullname>Douglas E. Horsman</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>MacAulay C</author_shortname>            <author_fullname>Calum MacAulay</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Ng RT</author_shortname>            <author_fullname>Raymond T. Ng</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Brown CJ</author_shortname>            <author_fullname>Carolyn J. Brown</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Eichler EE</author_shortname>            <author_fullname>Evan E. Eichler</author_fullname>            <author_affiliation>6</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Lam WL</author_shortname>            <author_fullname>Wan L. Lam</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <institution>Departments of Cancer Genetics and Developmental Biology, British Columbia Cancer Research Centre.</institution>        <institution>Department of Cancer Imaging, British Columbia Cancer Research Centre.</institution>        <institution>Department of Pathology, British Columbia Cancer Agency.</institution>        <institution>Department of Computer Science, University of British Columbia, Vancouver.</institution>        <institution>Department of Medical Genetics, University of British Columbia, Vancouver.</institution>        <institution>Department of Genome Sciences, University of Washington School of Medicine, and Howard Hughes Medical Institute, Seattle</institution>        <institution>These two authors contributed equally to this work.</institution>    </publication>	<publication pub_id="90">        <status>On</status>        <application>CGH</application>        <title>Cytogenetic instability in ovarian epithelial cells from women at risk of ovarian cancer</title>        <journal>Cancer Res.</journal>        <issue>2006 Sep 15;66(18):9017-25.</issue>        <pubdate>2006-09-15</pubdate>        <epubdate>2006-09-15</epubdate>        <url>http://dx.doi.org/10.1158/0008-5472.CAN-06-0222</url>        <url_pdf>http://cancerres.aacrjournals.org/cgi/reprint/66/18/9017.pdf</url_pdf>        <url_supplemental>http://cancerres.aacrjournals.org/cgi/content/full/66/18/9017/DC1</url_supplemental>        <abstract>Fanconi anemia is an inherited cancer predisposition disease characterized by cytogenetic and cellular hypersensitivity to cross-linking agents. Seeking evidence of Fanconi anemia protein dysfunction in women at risk of ovarian cancer, we screened ovarian surface epithelial cells from 25 primary cultures established from 22 patients using cross-linker hypersensitivity assays. Samples were obtained from (a) women at high risk for ovarian cancer with histologically normal ovaries, (b) ovarian cancer patients, and (c) a control group with no family history of breast or ovarian cancer. In chromosomal breakage assays, all control cells were mitomycin C (MMC) resistant, but eight samples (five of the six high-risk and three of the eight ovarian cancer) were hypersensitive. Lymphocytes from all eight patients were MMC resistant. Only one of the eight patients had a BRCA1 germ-line mutation and none had BRCA2 mutations, but FANCD2 was reduced in five of the eight. Ectopic expression of normal FANCD2 cDNA increased FANCD2 protein and induced MMC resistance in both hypersensitive lines tested. No FANCD2 coding region or promoter mutations were found, and there was no genomic loss or promoter methylation in any Fanconi anemia genes. Therefore, in high-risk women with no BRCA1 or BRCA2 mutations, tissue-restricted hypersensitivity to cross-linking agents is a frequent finding, and chromosomal breakage responses to MMC may be a sensitive screening strategy because cytogenetic instability identified in this way antedates the onset of carcinoma. Inherited mutations that result in tissue-specific FANCD2 gene suppression may represent a cause of familial ovarian cancer.</abstract>        <author>            <id>1</id>            <author_shortname>Pejovic T</author_shortname>            <author_fullname>Tanja Pejovic</author_fullname>            <author_affiliation>1,2,3</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Yates JE</author_shortname>            <author_fullname>Jane E. Yates</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Liu HY</author_shortname>            <author_fullname>Hong Y. Liu</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Hays LE</author_shortname>            <author_fullname>Laura E. Hays</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Akkari Y</author_shortname>            <author_fullname>Yassmine Akkari</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Torimaru Y</author_shortname>            <author_fullname>Yumi Torimaru</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Keeble W</author_shortname>            <author_fullname>Winifred Keeble</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Rathbun RK</author_shortname>            <author_fullname>Keaney Rathbun</author_fullname>            <author_affiliation>3</author_affiliation>        </author>		<author>            <id>9</id>            <author_shortname>Rodgers WH</author_shortname>            <author_fullname>William H. Rodgers</author_fullname>            <author_affiliation>6</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Bale AE</author_shortname>            <author_fullname>Allen E. Bale</author_fullname>            <author_affiliation>7</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Ameziane N</author_shortname>            <author_fullname>Najim Ameziane</author_fullname>            <author_affiliation>8</author_affiliation>        </author>        <author>            <id>12</id>            <author_shortname>Zwaan CM</author_shortname>            <author_fullname>C. Michael Zwaan</author_fullname>            <author_affiliation>10</author_affiliation>        </author>        <author>            <id>13</id>            <author_shortname>Errami A</author_shortname>            <author_fullname>Abdellatif Errami</author_fullname>            <author_affiliation>9</author_affiliation>        </author>        <author>            <id>14</id>            <author_shortname>Thuillier P</author_shortname>            <author_fullname>Philippe Thuillier</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>15</id>            <author_shortname>Cappuccini F</author_shortname>            <author_fullname>Fabio Cappuccini</author_fullname>            <author_affiliation>1,2</author_affiliation>        </author>        <author>            <id>16</id>            <author_shortname>Olson SB</author_shortname>            <author_fullname>Susan B. Olson</author_fullname>            <author_affiliation>4</author_affiliation>        </author>		<author>            <id>17</id>            <author_shortname>Cain JM</author_shortname>            <author_fullname>Joanna M. Cain</author_fullname>            <author_affiliation>1,2</author_affiliation>        </author>        <author>            <id>18</id>            <author_shortname>Bagby GC Jr.</author_shortname>            <author_fullname>Grover C. Bagby, Jr.</author_fullname>            <author_affiliation>3,4,5</author_affiliation>        </author>        <institution>Department of Obstetrics and Gynecology,</institution>        <institution>Center for Women's Health,</institution>        <institution>Oregon Health &#38; Science University Cancer Institute, and</institution>        <institution>Department of Medicine and Molecular and Medical Genetics, Oregon Health &#38; Science University.</institution>        <institution>Northwest Veterans Affairs Cancer Research Center, Portland Veterans Affairs Medical Center, Portland, Oregon.</institution>        <institution>Department of Pathology, University of Maryland, Baltimore, Maryland.</institution>        <institution>Department of Genetics, Yale University School of Medicine, New Haven, Connecticut.</institution>        <institution>Department of Clinical and Human Genetics, Vrije University Medical Center</institution>        <institution>MRC-Holland B.V., Amsterdam, the Netherlands; and</institution>        <institution>Department of Pediatric Oncology, Erasmus Medical Center/Sophia Children's Hospital, Rotterdam, the Netherlands.</institution>    </publication>	<publication pub_id="91">        <status>Off</status>        <application>DNA Methylation</application>        <title>MIRA-Assisted Microarray Analysis, a New Technology for the Determination of DNA Methylation Patterns, Identifies Frequent Methylation of Homeodomain-Containing Genes in Lung Cancer Cells</title>        <journal>Cancer Res.</journal>        <issue>2006 Aug 15;66(16):7939-47.</issue>        <pubdate>2006-08-15</pubdate>        <epubdate>2006-08-15</epubdate>        <url>http://dx.doi.org/10.1158/0008-5472.CAN-06-1888</url>        <url_pdf></url_pdf>        <url_supplemental>http://cancerres.aacrjournals.org/cgi/content/full/66/16/7939/DC1</url_supplemental>        <abstract>We present a straightforward and comprehensive approach for DNA methylation analysis in mammalian genomes. The methylated-CpG island recovery assay (MIRA), which is based on the high affinity of the MBD2/MBD3L1 complex for methylated DNA, has been used to detect cell type-dependent differences in DNA methylation on a microarray platform. The procedure has been verified and applied to identify a series of novel candidate lung tumor suppressor genes and potential DNA methylation markers that contain methylated CpG islands. One gene of particular interest was DLEC1, located at a commonly deleted area on chromosome 3p22-p21.3, which was frequently methylated in primary lung cancers and melanomas. Among the identified methylated genes, homeodomain-containing genes were unusually frequent (11 of the top 50 hits) and were targeted on different chromosomes. These genes included LHX2, LHX4, PAX7, HOXB13, LBX1, SIX2, HOXD3, DLX1, HOXD1, ONECUT2, and PAX9. The data show that MIRA-assisted microarray analysis has a low false-positive rate and has the capacity to catalogue methylated CpG islands on a genome-wide basis. The results support the hypothesis that cancer-associated DNA methylation events do not occur randomly throughout the genome but at least some are targeted by specific mechanisms. (Cancer Res 2006; 66(16): 7939-47).</abstract>        <author>            <id>1</id>            <author_shortname>Rauch T</author_shortname>            <author_fullname>Tibor Rauch</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Li H</author_shortname>            <author_fullname>Hongwei Li</author_fullname>            <author_affiliation>1</author_affiliation>        </author>		<author>            <id>3</id>            <author_shortname>Wu X</author_shortname>            <author_fullname>Xiwei Wu</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Pfeifer GP</author_shortname>            <author_fullname>Gerd P. Pfeifer</author_fullname>            <author_affiliation>1</author_affiliation>        </author>		<institution>Divisions of Biology and</institution>        <institution>Biomedical Informatics, Beckman Research Institute of the City of Hope, Duarte, California.</institution>    </publication>	<publication pub_id="92">        <status>Off</status>        <application>DNA Methylation</application>        <title>Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells</title>        <journal>Nat. Genet.</journal>        <issue>2005 Aug;37(8):853-62. Epub 2005 Jul 10.</issue>        <pubdate>2005-07-10</pubdate>        <epubdate>2005-07-10</epubdate>        <url>http://dx.doi.org/10.1038/ng1598</url>        <url_pdf></url_pdf>        <url_supplemental>http://www.nature.com/ng/journal/v37/n8/suppinfo/ng1598_S1.html</url_supplemental>        <abstract>Cytosine methylation is required for mammalian development and is often perturbed in human cancer. To determine how this epigenetic modification is distributed in the genomes of primary and transformed cells, we used an immunocapturing approach followed by DNA microarray analysis to generate methylation profiles of all human chromosomes at 80-kb resolution and for a large set of CpG islands. In primary cells we identified broad genomic regions of differential methylation with higher levels in gene-rich neighborhoods. Female and male cells had indistinguishable profiles for autosomes but differences on the X chromosome. The inactive X chromosome (Xi) was hypermethylated at only a subset of gene-rich regions and, unexpectedly, overall hypomethylated relative to its active counterpart. The chromosomal methylation profile of transformed cells was similar to that of primary cells. Nevertheless, we detected large genomic segments with hypomethylation in the transformed cell residing in gene-poor areas. Furthermore, analysis of 6,000 CpG islands showed that only a small set of promoters was methylated differentially, suggesting that aberrant methylation of CpG island promoters in malignancy might be less frequent than previously hypothesized.</abstract>        <author>            <id>1</id>            <author_shortname>Weber M</author_shortname>            <author_fullname>Michael Weber</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Davies JJ</author_shortname>            <author_fullname>Jonathan J. Davies</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Wittig D</author_shortname>            <author_fullname>David Wittig</author_fullname>            <author_affiliation>1</author_affiliation>        </author>		<author>            <id>4</id>            <author_shortname>Oakeley EJ</author_shortname>            <author_fullname>Edward J. Oakeley</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Haase M</author_shortname>            <author_fullname>Michael Haase</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Lam WL</author_shortname>            <author_fullname>Wan L. Lam</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Schubeler D</author_shortname>            <author_fullname>Dirk Sch&#252;beler</author_fullname>            <author_affiliation>1,4</author_affiliation>        </author>        <institution>Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland.</institution>        <institution>British Columbia Cancer Research Center, Vancouver, British Columbia, Canada.</institution>        <institution>Department of Pathology, Dresden University of Technology, Dresden, Germany.</institution>        <institution>Correspondence should be addressed to Dirk Schübeler dirk@fmi.ch</institution>    </publication>	<publication pub_id="93">        <status>On</status>        <application>CGH</application>        <title>Delineation of a 1Mb breakpoint region at 1p13 in Wilms tumors by fine-tiling oligonucleotide array CGH</title>        <journal>Genes Chromosomes Cancer</journal>        <issue>2007 Jun;46(6):607-15.</issue>        <pubdate>2007-03-16</pubdate>        <epubdate>2007-03-16</epubdate>        <url>http://dx.doi.org/10.1002/gcc.20446</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>Wilms tumor karyotypes frequently exhibit recurrent, large-scale chromosomal imbalances, among the most common of which are concurrent loss of 1p and gain of 1q. We have previously identified a novel breakpoint at 1p13 by 1 Mb-spaced array CGH, and have now undertaken a fine-tiling oligonucleotide array approach to map the region accurately in four tumors exhibiting rearrangements at this locus. The use of a 10 bp-spaced platform revealed that all four tumors in fact harbored different breakpoints, which targeted intragenic sequences in PHTF1, DCLRE1B, and NRAS, and an intergenic region immediately downstream of TRIM33. All four genes and breakpoints were within the 1.78 Mb intervals identified by the genome-wide BAC arrays. The precise breakpoint interval was in each case mapped to a 200-1,200 bp region and was confirmed for one case to lie within intron 3 of DCLRE1B by quantitative PCR. Analysis of local genome architecture revealed no convincing conservation of repetitive sequences or specific translocation/recombination-associated elements within the breakpoint regions. This study highlights the power of fine-tiling oligonucleotide arrays to delineate breakpoint regions identified by genome-wide screens.</abstract>        <author>            <id>1</id>            <author_shortname>Natrajan R</author_shortname>            <author_fullname>Rachael Natrajan</author_fullname>            <author_affiliation>1,6</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Williams RD</author_shortname>            <author_fullname>Richard D. Williams</author_fullname>            <author_affiliation>1,6</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Grigoriadis A</author_shortname>            <author_fullname>Anita Grigoriadis</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Mackay A</author_shortname>            <author_fullname>Alan Mackay</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Fenwick K</author_shortname>            <author_fullname>Kerry Fenwick</author_fullname>            <author_affiliation>3</author_affiliation>        </author>		<author>            <id>6</id>            <author_shortname>Ashworth A</author_shortname>            <author_fullname>Alan Ashworth</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Dome JS</author_shortname>            <author_fullname>Jeffrey S. Dome</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Grundy PE</author_shortname>            <author_fullname>Paul E. Grundy</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Pritchard-Jones K</author_shortname>            <author_fullname>Kathy Pritchard-Jones</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Jones C</author_shortname>            <author_fullname>Chris Jones</author_fullname>            <author_affiliation>1,7</author_affiliation>        </author>        <institution>Paediatric Oncology, Institute of Cancer Research/Royal Marsden NHS Trust, Sutton, UK</institution>        <institution>Ludwig Institute for Cancer Research, University College London, London, UK</institution>        <institution>Breakthrough Breast Cancer Research Centre, Institute of Cancer Research, London, UK</institution>        <institution>Division of Oncology, Children's National Medical Center, Washington DC</institution>        <institution>Departments of Paediatrics and Oncology, University of Alberta, Edmonton, Canada</institution>        <institution>R.N. and R.D.W. contributed equally this work.</institution>        <institution>Correspondence to Chris Jones, Paediatric Oncology, Institute of Cancer Research, 15 Costwold Road, Sutton, Surrey, SM2 5NG, UK</institution>    </publication>	<publication pub_id="94">        <status>On</status>        <application>CGH</application>        <title>Characterization of a recurrent 15q24 microdeletion syndrome</title>        <journal>Hum. Mol. Genet.</journal>        <issue>2007 Mar 1;16(5):567-72. Epub 2007 Mar 14.</issue>        <pubdate>2007-03-14</pubdate>        <epubdate>2007-03-14</epubdate>        <url>http://dx.doi.org/10.1093/hmg/ddm016</url>        <url_pdf>http://hmg.oxfordjournals.org/cgi/reprint/ddm016v1</url_pdf>        <url_supplemental>http://hmg.oxfordjournals.org/cgi/content/full/ddm016/DC1</url_supplemental>        <abstract>We describe multiple individuals with mental retardation and overlapping de novo submicroscopic deletions of 15q24 (1.7-3.9 Mb in size). High resolution analysis showed that in three patients both proximal and distal breakpoints co-localized to highly identical segmental duplications (&#62;51 kb in length, &#62;94% identity), suggesting non-allelic homologous recombination as the likely mechanism of origin. Sequencing studies in a fourth individual provided basepair resolution, and showed that both breakpoints in this case were located in unique sequence. Despite differences in the size and location of the deletions, all four individuals share several major features (growth retardation, microcephaly, digital abnormalities, hypospadias and loose connective tissue) and resemble one another facially (high anterior hair line, broad medial eyebrows, hypertelorism, downslanted palpebral fissures, broad nasal base, long smooth philtrum and full lower lip), indicating that this represents a novel syndrome caused by haploinsufficiency of one or more dosage-sensitive genes in the minimal deletion region. Our results define microdeletion of 15q24 as a novel recurrent genomic disorder.</abstract>        <author>            <id>1</id>            <author_shortname>Sharp AJ</author_shortname>            <author_fullname>Andrew J. Sharp</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Selzer RR</author_shortname>            <author_fullname>Rebecca R. Selzer</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Veltman JA</author_shortname>            <author_fullname>Joris A. Veltman</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Gimelli S</author_shortname>            <author_fullname>Stefania Gimelli</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Gimelli G</author_shortname>            <author_fullname>Giorgio Gimelli</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Striano P</author_shortname>            <author_fullname>Pasquale Striano</author_fullname>            <author_affiliation>6,7</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Coppola A</author_shortname>            <author_fullname>Antonietta Coppola</author_fullname>            <author_affiliation>6</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Regan R</author_shortname>            <author_fullname>Regina Regan</author_fullname>            <author_affiliation>8</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Price SM</author_shortname>            <author_fullname>Sue M. Price</author_fullname>            <author_affiliation>9</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Knoers NV</author_shortname>            <author_fullname>Nine V. Knoers</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Eis PS</author_shortname>            <author_fullname>Peggy S. Eis</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>12</id>            <author_shortname>Brunner HG</author_shortname>            <author_fullname>Han G. Brunner</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>13</id>            <author_shortname>Hennekam RC</author_shortname>            <author_fullname>Raoul C. Hennekam</author_fullname>            <author_affiliation>10</author_affiliation>        </author>        <author>            <id>14</id>            <author_shortname>Knight SJ</author_shortname>            <author_fullname>Samantha J.L. Knight</author_fullname>            <author_affiliation>8</author_affiliation>        </author>        <author>            <id>15</id>            <author_shortname>de Vries BB</author_shortname>            <author_fullname>Bert B.A. de Vries</author_fullname>            <author_affiliation>3</author_affiliation>        </author>         <author>            <id>16</id>            <author_shortname>Zuffardi O</author_shortname>            <author_fullname>Orsetta Zuffardi</author_fullname>            <author_affiliation>4,11</author_affiliation>        </author>        <author>            <id>17</id>            <author_shortname>Eichler EE</author_shortname>            <author_fullname>Evan E. Eichler</author_fullname>            <author_affiliation>1,12,13</author_affiliation>        </author>        <institution>Department of Genome Sciences, University of Washington School of Medicine</institution>        <institution>NimbleGen Systems Inc., Madison, WI 53711, USA</institution>        <institution>Department of Human Genetics, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands</institution>        <institution>Biologia Generale e Genetica Medica, Universit&#225; di Pavia, Pavia, Italy</institution>        <institution>Citogenetica Ospedale Gaslini, Genova, Italy</institution>        <institution>Dipartimento di Scienze Neurologiche, Universit&#225; Federico II, Napoli, Italy</institution>        <institution>Unit&#225; Neuromuscolare Ospedale Gaslini, Genova, Italy</institution>        <institution>Oxford Genetics Knowledge Park, The Wellcome Trust Centre for Human Genetics, Churchill Hospital, Oxford, UK</institution>        <institution>Department of Clinical Genetics, Oxford Radcliffe Hospitals NHS Trust, Churchill Hospital, Oxford, OX3 7LJ, UK</institution>        <institution>Clinical and Molecular Genetics Unit, Institute of Child Health, UCL, London and Department of Pediatrics, AMC, University of Amsterdam, The Netherlands</institution>        <institution>Fondazione IRCSS Policlinico San Matteo, Pavia, Italy</institution>        <institution>Howard Hughes Medical Institute, 1705 NE Pacific St. Seattle, WA, 98195, USA</institution>        <institution>Corresponding author: Evan Eichler, Ph.D., Department of Genome Sciences University of Washington and Howard Hughes Medical Institute, Foege Building S413A, Box 355065, 1705 NE Pacific St., Seattle, WA 98195, Telephone: (206) 543-9526, Fax: (206) 685-7301, E-mail: eee@gs.washington.edu</institution>    </publication>	<publication pub_id="95">        <status>On</status>        <application>ChIP-chip</application>        <title>Histone replacement marks the boundaries of cis-regulatory domains</title>        <journal>Science</journal>        <issue>2007 Mar 9;315(5817):1408-11.</issue>        <pubdate>2007-03-09</pubdate>        <epubdate>2007-03-09</epubdate>        <url>http://dx.doi.org/10.1126/science.1134004</url>        <url_pdf></url_pdf>        <url_supplemental>http://www.sciencemag.org/cgi/content/full/sci;315/5817/1408/DC1</url_supplemental>        <abstract>Cellular memory is maintained at homeotic genes by cis-regulatory elements whose mechanism of action is unknown. We have examined chromatin at Drosophila homeotic gene clusters by measuring, at high resolution, levels of histone replacement and nucleosome occupancy. Homeotic gene clusters display conspicuous peaks of histone replacement at boundaries of cis-regulatory domains superimposed over broad regions of low replacement. Peaks of histone replacement closely correspond to nuclease-hypersensitive sites, binding sites for Polycomb and trithorax group proteins, and sites of nucleosome depletion. Our results suggest the existence of a continuous process that disrupts nucleosomes and maintains accessibility of cis-regulatory elements.</abstract>        <author>            <id>1</id>            <author_shortname>Mito Y</author_shortname>            <author_fullname>Yoshiko Mito</author_fullname>            <author_affiliation>1,2</author_affiliation>        </author>		<author>            <id>2</id>            <author_shortname>Henikoff JG</author_shortname>            <author_fullname>Jorja G. Henikoff</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Henikoff S</author_shortname>            <author_fullname>Steven Henikoff</author_fullname>            <author_affiliation>1,3,4</author_affiliation>        </author>        <institution>Basic Sciences Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109, USA</institution>        <institution>Molecular and Cellular Biology Program, University of Washington, Seattle, WA 98195, USA</institution>        <institution>Howard Hughes Medical Institute, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA</institution>        <institution>To whom correspondence should be addressed. E-mail: steveh@fhcrc.org</institution>    </publication>	<publication pub_id="96">        <status>On</status>        <application>ChIP-chip</application>        <title>Selective silencing of foreign DNA with low GC content by the H-NS protein in Salmonella</title>        <journal>Science</journal>        <issue>2006 Jul 14;313(5784):236-8. Epub 2006 Jun 8.</issue>        <pubdate>2006-07-14</pubdate>        <epubdate>2006-06-08</epubdate>        <url>http://dx.doi.org/10.1126/science.1128794</url>        <url_pdf></url_pdf>        <url_supplemental>http://www.sciencemag.org/cgi/content/full/sci;1128794/DC1</url_supplemental>        <abstract>Horizontal gene transfer plays a major role in microbial evolution. However, newly acquired sequences can decrease fitness unless integrated into preexisting regulatory networks. We found that the histone-like nucleoid structuring protein (H-NS) selectively silences horizontally acquired genes by targeting sequences with GC content lower than the resident genome. Mutations in hns are lethal in Salmonella unless accompanied by compensatory mutations in other regulatory loci. Thus, H-NS provides a previously unrecognized mechanism of bacterial defense against foreign DNA, enabling the acquisition of DNA from exogenous sources while avoiding detrimental consequences from unregulated expression of newly acquired genes. Characteristic GC/AT ratios of bacterial genomes may facilitate discrimination between a cell's own DNA and foreign DNA.</abstract>        <author>            <id>1</id>            <author_shortname>Navarre WW</author_shortname>            <author_fullname>William Wiley Navarre</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Porwollik S</author_shortname>            <author_fullname>Steffen Porwollik</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Wang Y</author_shortname>            <author_fullname>Yipeng Wang</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>McClelland M</author_shortname>            <author_fullname>Michael McClelland</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Rosen H</author_shortname>            <author_fullname>Henry Rosen</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Libby SJ</author_shortname>            <author_fullname>Stephen J. Libby</author_fullname>            <author_affiliation>1,5</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Fang FC</author_shortname>            <author_fullname>Ferric C. Fang</author_fullname>            <author_affiliation>1,2,3,5,6</author_affiliation>        </author>        <institution>Department of Laboratory Medicine, University of Washington, Seattle, WA 98195, USA.</institution>        <institution>Department of Medicine, University of Washington, Seattle, WA 98195, USA.</institution>        <institution>Department of Microbiology, University of Washington, Seattle, WA 98195, USA.</institution>        <institution>Sidney Kimmel Cancer Center, San Diego, CA 92121, USA.</institution>        <institution>These authors contributed equally to this work.</institution>        <institution>To whom correspondence should be addressed. E-mail: fcfang@u.washington.edu</institution>    </publication>	<publication pub_id="97"><!--	This is a duplicate that is also listed under CGH -->        <status>Off</status>        <application>CGS</application>        <title>Defining Genomic Islands and Uropathogen-Specific Genes in Uropathogenic Escherichia coli</title>        <journal>J. Bacteriol.</journal>        <issue>2007 Mar 9; [Epub ahead of print]</issue>        <pubdate>2007-03-09</pubdate>        <epubdate>2007-03-09</epubdate>        <url>http://dx.doi.org/10.1126/science.1134004</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>Uropathogenic Escherichia coli (UPEC) are responsible for the majority of uncomplicated urinary tract infections, which can present clinically as cystitis or pyelonephritis. UPEC strain CFT073, isolated from the blood of a patient with acute pyelonephritis, was most cytotoxic and most virulent in mice among our strain collection. Based on the genome sequence of CFT073, microarrays were utilized in comparative genomic hybridization (CGH) analysis of a panel of uropathogenic and fecal/commensal E. coli isolates. Genomic DNA from seven UPEC (three pyelonephritis, four cystitis) isolates and three fecal/commensal strains including K-12 MG1655 was hybridized to the CFT073 microarray. The CFT073 genome contains 5379 genes; CGH analysis revealed that 2820 (52.4%) of these genes were common to all 11 E. coli strains, yet only 173 UPEC-specific genes were found in all UPEC strains by CGH but in none of the fecal/commensal strains. When the sequence of three additional sequenced UPEC strains (UTI89, 536, F11) and a commensal strain (HS) were added to the analysis, 131 genes present in all UPEC strains but in no fecal/commensal strains were identified. Seven previously unrecognized genomic islands (&#62;30 kb) were delineated by CGH in addition to the three known pathogenicity islands. These genomic islands comprise 672 of the 5231 kb (12.8%) genome, demonstrating the importance of horizontal transfer for UPEC and the mosaic structure of the genome. UPEC strains contain a greater number of iron acquisition systems than fecal/commensal strains, reflective of adaptation to the iron-limiting urinary tract environment. Each strain displayed distinct differences in the number and type of known virulence factors. The large number of hypothetical genes in the CFT073 genome, especially those shown to be UPEC-specific, strongly suggest that many urovirulence factors remain uncharacterized.</abstract>        <author>            <id>1</id>            <author_shortname>Lloyd AL</author_shortname>            <author_fullname>Amanda L. Lloyd</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Rasko DA</author_shortname>            <author_fullname>David A. Rasko</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Mobley HL</author_shortname>            <author_fullname>Harry L. T. Mobley</author_fullname>            <author_affiliation>1,3</author_affiliation>        </author>        <institution>Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI 48109, USA</institution>        <institution>Department of Microbiology, University of Texas Southwestern Medical Center, Dallas, TX 75235, USA</institution>        <institution>To whom correspondence should be addressed. Email: hmobley@med.umich.edu.</institution>    </publication>	<publication pub_id="98">        <status>On</status>        <application>CGS</application>        <title>Whole-Genome Analysis of the Methyl tert-Butyl Ether-Degrading Beta-Proteobacterium Methylibium petroleiphilum PM1</title>        <journal>J. Bacteriol.</journal>        <issue>2007 Mar;189(5):1931-45. Epub 2006 Dec 8.</issue>        <pubdate>2007-03-01</pubdate>        <epubdate>2006-12-08</epubdate>        <url>http://dx.doi.org/10.1128/JB.01259-06</url>        <url_pdf></url_pdf>        <url_supplemental>http://jb.asm.org/cgi/content/full/189/5/1931/DC1</url_supplemental>        <abstract>Methylibium petroleiphilum PM1 is a methylotroph distinguished by its ability to completely metabolize the fuel oxygenate methyl tert-butyl ether (MTBE). Strain PM1 also degrades aromatic (benzene, toluene, and xylene) and straight-chain (C(5) to C(12)) hydrocarbons present in petroleum products. Whole-genome analysis of PM1 revealed an approximately 4-Mb circular chromosome and an approximately 600-kb megaplasmid, containing 3,831 and 646 genes, respectively. Aromatic hydrocarbon and alkane degradation, metal resistance, and methylotrophy are encoded on the chromosome. The megaplasmid contains an unusual t-RNA island, numerous insertion sequences, and large repeated elements, including a 40-kb region also present on the chromosome and a 29-kb tandem repeat encoding phosphonate transport and cobalamin biosynthesis. The megaplasmid also codes for alkane degradation and was shown to play an essential role in MTBE degradation through plasmid-curing experiments. Discrepancies between the insertion sequence element distribution patterns, the distributions of best BLASTP hits among major phylogenetic groups, and the G+C contents of the chromosome (69.2%) and plasmid (66%), together with comparative genome hybridization experiments, suggest that the plasmid was recently acquired and apparently carries the genetic information responsible for PM1&#39;s ability to degrade MTBE. Comparative genomic hybridization analysis with two PM1-like MTBE-degrading environmental isolates ( approximately 99% identical 16S rRNA gene sequences) showed that the plasmid was highly conserved (ca. 99% identical), whereas the chromosomes were too diverse to conduct resequencing analysis. PM1's genome sequence provides a foundation for investigating MTBE biodegradation and exploring the genetic regulation of multiple biodegradation pathways in M. petroleiphilum and other MTBE-degrading beta-proteobacteria.</abstract>        <author>            <id>1</id>            <author_shortname>Kane SR</author_shortname>            <author_fullname>Staci R. Kane</author_fullname>            <author_affiliation>1,5,6</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Chakicherla AY</author_shortname>            <author_fullname>Anu Y. Chakicherla</author_fullname>            <author_affiliation>1,5</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Chain PS</author_shortname>            <author_fullname>Patrick S.G. Chain</author_fullname>            <author_affiliation>1,4</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Schmidt R</author_shortname>            <author_fullname>Radomir Schmidt</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Shin MW</author_shortname>            <author_fullname>Maria W. Shin</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Legler TC</author_shortname>            <author_fullname>Tina C. Legler</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Scow KM</author_shortname>            <author_fullname>Kate M. Scow</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Larimer FW</author_shortname>            <author_fullname>Frank W. Larimer</author_fullname>            <author_affiliation>3,4</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Lucas SM</author_shortname>            <author_fullname>Susan M. Lucas</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Richardson PM</author_shortname>            <author_fullname>Paul M. Richardson</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Hristova KR</author_shortname>            <author_fullname>Krassimira R. Hristova</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <institution>Lawrence Livermore National Laboratory, Livermore, California</institution>        <institution>Department of Land Air and Water Resources, University of California, Davis, California</institution>        <institution>Genome Analysis Group, Oak Ridge National Laboratory, Oak Ridge, Tennessee</institution>        <institution>Joint Genome Institute Production Genomics Facility, Walnut Creek, California</institution>        <institution>S.R.K. and A.Y.C. gave equal contributions to this study.</institution>        <institution>Corresponding author. Mailing address: Lawrence Livermore National Laboratory, 7000 East Avenue, L-542, Livermore, CA 94550. Phone: (925) 422-7897. Fax: (925) 422-3800. E-mail: kane11@llnl.gov.</institution>    </publication>	<publication pub_id="99">        <status>On</status>        <application>Expression</application>        <title>Potential targets of FOXL2, a transcription factor involved in craniofacial and follicular development, identified by transcriptomics</title>        <journal>PNAS</journal>        <issue>2007 Feb 27;104(9):3330-3335. Epub 2007 Feb 20.</issue>        <pubdate>2007-02-27</pubdate>        <epubdate>2007-02-20</epubdate>        <url>http://dx.doi.org/10.1073/pnas.0611326104</url>        <url_pdf></url_pdf>        <url_supplemental>http://www.pnas.org/cgi/content/full/0611326104/DC1</url_supplemental>        <abstract>FOXL2 is a gene encoding a forkhead transcription factor, whose mutations are responsible for the blepharophimosis-ptosis-epicanthus inversus syndrome that often involves premature ovarian failure. FOXL2 is one of the earliest ovarian markers and it offers, along with its targets, an excellent model to study ovarian development and function in normal and pathological conditions. We have recently shown that the aromatase gene is a target of FOXL2, and only three other targets have been reported so far. To detect potential transcriptional targets of FOXL2, we used DNA chips and quantitative PCR to compare the transcriptomes of granulosa-like cells overexpressing, or not, FOXL2. This analysis showed that mediators of inflammation, apoptotic and transcriptional regulators, genes involved in cholesterol metabolism, and genes encoding enzymes and transcription factors involved in reactive oxygen species detoxification were up-regulated. On the other hand, FOXL2 down-regulated the transcription of several genes involved in proteolysis and signal transduction and in transcription regulation. A bioinformatic analysis was conducted to discriminate between potential target promoters activated and repressed by FOXL2. In addition, the promoters of strongly activated genes were enriched in forkhead recognition sites, suggesting that these genes might be direct FOXL2 targets. Altogether, these results provide insight into the activity of FOXL2 and may help in understanding the mechanisms of pathogenesis of FOXL2 mutations if the targets prove to be the same in the ovary.</abstract>        <author>            <id>1</id>            <author_shortname>Batista F</author_shortname>            <author_fullname>Frank Batista</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Vaiman D</author_shortname>            <author_fullname>Daniel Vaiman</author_fullname>            <author_affiliation>1,2</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Dausset J</author_shortname>            <author_fullname>Jean Dausset</author_fullname>            <author_affiliation>3,5</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Fellous M</author_shortname>            <author_fullname>Marc Fellous</author_fullname>            <author_affiliation>1,4</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Veitia RA</author_shortname>            <author_fullname>Reiner A. Veitia</author_fullname>            <author_affiliation>1,4,6</author_affiliation>        </author>        <institution>D&#233;partement de G&#233;n&#233;tique et D&#233;veloppement, Institut Cochin, Institut National de la Sant&#233; et de la Recherche M&#233;dicale U567, Centre National de la Recherche Scientifique Unit&#233; Mixte de Recherche 8104, and Facult&#233; de M&#233;decine Ren&#233; Descartes, Universit&#233; Paris V UM 3, 75014 Paris, France</institution>        <institution>D&#233;partement de G&#233;n&#233;tique Animale, Institut National de la Recherche Agronomique, 75338 Paris Cedex 07, France</institution>        <institution>Fondation Jean Dausset, Centre d'Etude du Polymorphisme Humain, 75010 Paris, France</institution>        <institution>Universit&#233; Denis Diderot/Paris VII, 75005 Paris, France</institution>        <institution>To whom correspondence may be addressed. E-mail: dausset@cephb.fr</institution>        <institution>To whom correspondence may be addressed at: INSERM E21-GDPM et Universit&#233; Paris VII, Institut Cochin, 24, Rue du Faubourg Saint Jacques, 75014 Paris, France. E-mail: veitia@cochin.inserm.fr</institution>    </publication>	<publication pub_id="100">        <status>On</status>        <application>Expression</application>        <title>Carbohydrate starvation causes a metabolically active but nonculturable state in Lactococcus lactis</title>        <journal>Appl. Environ. Microbiol.</journal>        <issue>2007 Apr;73(8):2498-512. Epub 2007 Feb 9.</issue>        <pubdate>2007-04-01</pubdate>        <epubdate>2007-02-09</epubdate>        <url>http://dx.doi.org/10.1128/AEM.01832-06</url>        <url_pdf></url_pdf>        <url_supplemental>http://aem.asm.org/cgi/content/full/73/8/2498/DC1</url_supplemental>        <abstract>turable state wherein sugar metabolism, cell division, and autolysis were repressed allowing the cells to maintain transcription, metabolic activity, and energy production during a state that produced new metabolites not associated with logarithmic growth.</abstract>        <author>            <id>1</id>            <author_shortname>Ganesan B</author_shortname>            <author_fullname>Balasubramanian Ganesan</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Stuart MR</author_shortname>            <author_fullname>Mark R. Stuart</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Weimer BC</author_shortname>            <author_fullname>Bart C. Weimer</author_fullname>            <author_affiliation>1,2</author_affiliation>        </author>        <institution>Western Dairy Center, Center for Integrated BioSystems, Center for Microbe Detection and Physiology, Department of Nutrition and Food Sciences, Utah State University, Logan, UT 84322-4700</institution>        <institution>To whom correspondence should be addressed. Email: bcweimer@cc.usu.edu.</institution>    </publication>	<publication pub_id="101">        <status>On</status>        <application>Expression</application>        <title>In vivo gene expression analysis identifies genes required for enhanced colonization of the mouse urinary tract by uropathogenic Escherichia coli strain CFT073 dsdA</title>        <journal>Infect. Immun.</journal>        <issue>2007 Jan;75(1):278-89. Epub 2006 Oct 30.</issue>        <pubdate>2007-01-01</pubdate>        <epubdate>2006-10-30</epubdate>        <url>http://dx.doi.org/10.1128/IAI.01319-06</url>        <url_pdf></url_pdf>        <url_supplemental>http://iai.asm.org/cgi/content/full/75/1/278/DC1</url_supplemental>        <abstract>Deletional inactivation of the gene encoding D-serine deaminase, dsdA, in uropathogenic Escherichia coli strain CFT073 results in a hypermotile strain with a hypercolonization phenotype in the bladder and kidneys of mice in a model of urinary tract infection (UTI). The in vivo gene expression profiles of CFT073 and CFT073 dsdA were compared by isolating RNA directly from the urine of mice challenged with each strain individually. Hybridization of cDNAs derived from these samples to CFT073-specific microarrays allowed identification of genes that were up- or down-regulated in the dsdA deletion strain during UTI. Up-regulated genes included the known D-serine-responsive gene dsdX, suggesting in vivo intracellular accumulation of D-serine by CFT073 dsdA. Genes encoding F1C fimbriae, both copies of P fimbriae, hemolysin, OmpF, a dipeptide transporter DppA, a heat shock chaperone IbpB, and clusters of open reading frames with unknown functions were also up-regulated. To determine the role of these genes as well as motility in the hypercolonization phenotype, mutants were constructed in the CFT073 dsdA background and tested in competition against the wild type in the murine model of UTI. Strains with deletions of one or both of the two P fimbrial operons, hlyA, fliC, ibpB, c0468, locus c3566 to c3568, or c2485 to c2490 colonized mouse bladders and kidneys at levels indistinguishable from wild type. CFT073 dsdA c2398 and CFT073 dsdA focA maintained a hypercolonization phenotype. A CFT073 dsdA dppA mutant was attenuated 10- to 50-fold in its colonization ability compared to CFT073. Our results support a role for D-serine catabolism and signaling in global virulence gene regulation of uropathogenic E. coli.</abstract>        <author>            <id>1</id>            <author_shortname>Haugen BJ</author_shortname>            <author_fullname>Brian J. Haugen</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Pellett S</author_shortname>            <author_fullname>Shahaireen Pellett</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Redford P</author_shortname>            <author_fullname>Peter Redford</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Hamilton HL</author_shortname>            <author_fullname>Holly L. Hamilton</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Roesch PL</author_shortname>            <author_fullname>Paula L. Roesch</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Welch RA</author_shortname>            <author_fullname>Rodney A. Welch</author_fullname>            <author_affiliation>1,2</author_affiliation>        </author>        <institution>Department of Medical Microbiology and Immunology, University of Wisconsin-Madison, Madison, Wisconsin 53706</institution>        <institution>Corresponding author. Mailing address: Department of Medical Microbiology and Immunology, University of Wisconsin-Madison, Madison, WI 53706. Phone: (608) 263-2700. Fax: (608) 262-8418. E-mail: rawelch@wisc.edu.</institution>    </publication>	<publication pub_id="102">        <status>On</status>        <application>CGH</application>        <title>Recurrent 10q22-23 deletions: A Genomic disorder on 10q associated with cognitive and behavioral abnormalities</title>        <journal>Am. J. Hum. Genet.</journal>        <issue>2007 May;80(5):938-47. Epub 2007 Mar 20.</issue>        <pubdate>2007-05-01</pubdate>        <epubdate>2007-03-20</epubdate>        <url>http://dx.doi.org/10.1086/513607</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>Low copy repeats (LCRs) are genomic features that affect chromosome stability and can produce disease-associated rearrangements. We describe members of three families with deletions in 10q22.3-10q23.31, a region harboring a complex set of LCRs, and demonstrate that rearrangements in this region are associated with behavioral and neurodevelopmental abnormalities including cognitive impairment, autism, hyperactivity and possibly psychiatric disease. Fine mapping of the deletions in members of all 3 families using a custom 10q oligonucleotide array CGH (NimbleGen) and PCR-based methods demonstrate a different deletion in each family. In one proband, the deletion breakpoints are associated with DNA fragments containing non-contiguous sequences of chromosome 10 while in the other two families the breakpoints are within paralogous LCRs, removing approximately 7.2 Mb and 32 genes. Our data provide evidence that the 10q22-q23 genomic region harbors one or more genes important for cognitive and behavioral development and recurrent deletions affecting this interval define a novel genomic disorder.</abstract>        <author>            <id>1</id>            <author_shortname>Balciuniene J</author_shortname>            <author_fullname>Jorune Balciuniene</author_fullname>            <author_affiliation>1,5</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Feng N</author_shortname>            <author_fullname>Ningping Feng</author_fullname>            <author_affiliation>8</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Iyadurai K</author_shortname>            <author_fullname>Kelly Iyadurai</author_fullname>            <author_affiliation>1,2,5</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Hirsch B</author_shortname>            <author_fullname>Betsy Hirsch</author_fullname>            <author_affiliation>3,4,7</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Charnas L</author_shortname>            <author_fullname>Lawrence Charnas</author_fullname>            <author_affiliation>2,4</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Bill BR</author_shortname>            <author_fullname>Brent R. Bill</author_fullname>            <author_affiliation>1,4,5,6</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Easterday MC</author_shortname>            <author_fullname>Mathew C. Easterday</author_fullname>            <author_affiliation>1,2,4,5</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Staaf J</author_shortname>            <author_fullname>Johan Staaf</author_fullname>            <author_affiliation>12</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Oseth L</author_shortname>            <author_fullname>LeAnn Oseth</author_fullname>            <author_affiliation>4,7</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Czapansky-Beilman D</author_shortname>            <author_fullname>Desiree Czapansky-Beilman</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Avramopoulos D</author_shortname>            <author_fullname>Dimitri Avramopoulos</author_fullname>            <author_affiliation>9</author_affiliation>        </author>        <author>            <id>12</id>            <author_shortname>Thomas GH</author_shortname>            <author_fullname>George H. Thomas</author_fullname>            <author_affiliation>8,10,11</author_affiliation>        </author>        <author>            <id>13</id>            <author_shortname>Borg A</author_shortname>            <author_fullname>&#197;ke Borg</author_fullname>            <author_affiliation>12</author_affiliation>        </author>        <author>            <id>14</id>            <author_shortname>Valle D</author_shortname>            <author_fullname>David Valle</author_fullname>            <author_affiliation>8,10</author_affiliation>        </author>        <author>            <id>15</id>            <author_shortname>Schimmenti LA</author_shortname>            <author_fullname>Lisa A. Schimmenti</author_fullname>            <author_affiliation>2,4,5,6</author_affiliation>        </author>        <author>            <id>16</id>            <author_shortname>Selleck SB</author_shortname>            <author_fullname>Scott B. Selleck</author_fullname>            <author_affiliation>1,2,4,5,13</author_affiliation>        </author>        <institution>Department of Genetics, Cell Biology and Development,</institution>        <institution>Department of Pediatrics,</institution>        <institution>Laboratory Medicine and Pathology,</institution>        <institution>Institute of Human Genetics,</institution>        <institution>Developmental Biology Center,</institution>        <institution>Department of Ophthalmology,</institution>        <institution>The University of Minnesota Cancer Center, University of Minnesota, Minneapolis, MN, USA</institution>        <institution>The McKusick-Nathans Institute of Genetic Medicine,</institution>        <institution>Department of Psychiatry,</institution>        <institution>Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA</institution>        <institution>Kennedy Krieger Institute, Baltimore, MD, USA</institution>        <institution>Department of Oncology, University Hospital, Lund, Sweden</institution>        <institution>Corresponding author.</institution>    </publication>	<publication pub_id="103">        <status>On</status>        <application>DNA Methylation</application>        <title>Homeobox gene methylation in lung cancer studied by genome-wide analysis with a microarray-based methylated CpG island recovery assay</title>        <journal>PNAS</journal>        <issue>2007 Mar 27;104(13):5527-32. Epub 2007 Mar 16.</issue>        <pubdate>2007-03-27</pubdate>        <epubdate>2007-03-16</epubdate>        <url>http://dx.doi.org/10.1073/pnas.0701059104</url>        <url_pdf></url_pdf>        <url_supplemental>http://www.pnas.org/cgi/content/full/0701059104/DC1</url_supplemental>        <abstract>De novo methylation of CpG islands is a common phenomenon in human cancer, but the mechanisms of cancer-associated DNA methylation are not known. We have used tiling arrays in combination with the methylated CpG island recovery assay to investigate methylation of CpG islands genome-wide and at high resolution. We find that all four HOX gene clusters on chromosomes 2, 7, 12, and 17 are preferential targets for DNA methylation in cancer cell lines and in early-stage lung cancer. CpG islands associated with many other homeobox genes, such as SIX, LHX, PAX, DLX, and Engrailed, were highly methylated as well. Altogether, more than half (104 of 192) of all CpG island-associated homeobox genes in the lung cancer cell line A549 were methylated. Analysis of paralogous HOX genes showed that not all paralogues undergo cancer-associated methylation simultaneously. The HOXA cluster was analyzed in greater detail. Comparison with ENCODE-derived data shows that lack of methylation at CpG-rich sequences correlates with presence of the active chromatin mark, histone H3 lysine-4 methylation in the HOXA region. Methylation analysis of HOXA genes in primary squamous cell carcinomas of the lung led to the identification of the HOXA7- and HOXA9-associated CpG islands as frequent methylation targets in stage 1 tumors. Homeobox genes are potentially useful as DNA methylation markers for early diagnosis of the disease. The finding of widespread methylation of homeobox genes lends support to the hypothesis that a substantial fraction of genes methylated in human cancer are targets of the Polycomb complex.</abstract>        <author>            <id>1</id>            <author_shortname>Rauch T</author_shortname>            <author_fullname>Tibor Rauch</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Wang Z</author_shortname>            <author_fullname>Zunde Wang</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Zhang X</author_shortname>            <author_fullname>Xinmin Zhang</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Zhong X</author_shortname>            <author_fullname>Xueyan Zhong</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Wu X</author_shortname>            <author_fullname>Xiwei Wu</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Lau SK</author_shortname>            <author_fullname>Sean K. Lau</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Kernstine KH</author_shortname>            <author_fullname>Kemp H. Kernstine</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Riggs AD</author_shortname>            <author_fullname>Arthur D. Riggs</author_fullname>            <author_affiliation>1,6</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Pfeifer GP</author_shortname>            <author_fullname>Gerd P. Pfeifer</author_fullname>            <author_affiliation>1,6</author_affiliation>        </author>        <institution>Division of Biology,</institution>        <institution>Division of Information Sciences,</institution>        <institution>Division of Pathology, and</institution>        <institution>Division of Surgery, Beckman Research Institute of the City of Hope, Duarte, CA 91010</institution>        <institution>NimbleGen Systems, Inc., Madison, WI 53711</institution>        <institution>To whom correspondence may be addressed. Arthur D. Riggs, E-mail: ariggs@coh.org Gerd P. Pfeifer, E-mail: gpfeifer@coh.org</institution>    </publication>	<publication pub_id="104">        <status>On</status>        <application>ChIP-chip</application>        <title>Analysis of the Vertebrate Insulator Protein CTCF-Binding Sites in the Human Genome</title>        <journal>Cell</journal>        <issue>2007 Mar 23;128(6):1231-45.</issue>        <pubdate>2007-03-23</pubdate>        <epubdate>2007-03-23</epubdate>        <url>http://dx.doi.org/10.1016/j.cell.2006.12.048</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>Insulator elements affect gene expression by preventing the spread of heterochromatin and restricting transcriptional enhancers from activation of unrelated promoters. In vertebrates, insulator's function requires association with the CCCTC-binding factor (CTCF), a protein that recognizes long and diverse nucleotide sequences. While insulators are critical in gene regulation, only a few have been reported. Here, we describe 13,804 CTCF-binding sites in potential insulators of the human genome, discovered experimentally in primary human fibroblasts. Most of these sequences are located far from the transcriptional start sites, with their distribution strongly correlated with genes. The majority of them fit to a consensus motif highly conserved and suitable for predicting possible insulators driven by CTCF in other vertebrate genomes. In addition, CTCF localization is largely invariant across different cell types. Our results provide a resource for investigating insulator function and possible other general and evolutionarily conserved activities of CTCF sites.</abstract>        <author>            <id>1</id>            <author_shortname>Kim TH</author_shortname>            <author_fullname>Tae Hoon Kim</author_fullname>            <author_affiliation>1,5,6</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Abdullaev ZK</author_shortname>            <author_fullname>Ziedulla K. Abdullaev</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Smith AD</author_shortname>            <author_fullname>Andrew D. Smith</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Ching KA</author_shortname>            <author_fullname>Keith A. Ching</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Loukinov DI</author_shortname>            <author_fullname>Dmitri I. Loukinov</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Green RD</author_shortname>            <author_fullname>Roland D. Green</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Zhang MQ</author_shortname>            <author_fullname>Michael Q. Zhang</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Lobanenkov VV</author_shortname>            <author_fullname>Victor V. Lobanenkov</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Ren B</author_shortname>            <author_fullname>Bing Ren</author_fullname>            <author_affiliation>1,6</author_affiliation>        </author>        <institution>Ludwig Institute for Cancer Research, Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093-0653, USA</institution>        <institution>National Institutes of Allergy and Infectious Disease, 5640 Fishers Lane, Rockville, MD 20852, USA</institution>        <institution>Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA</institution>        <institution>NimbleGen Systems Inc., 1 Science Court, Madison, WI 53711, USA</institution>        <institution>Present address: Department of Genetics, Yale University School of Medicine, 333 Cedar Street, SHMI 142B, P.O. Box 208005, New Haven, CT 06120-8005, USA.</institution>        <institution>Corresponding author</institution>    </publication>	<publication pub_id="105">        <status>On</status>        <application>ChIP-chip</application>        <title>Transcriptional signature with differential expression of BCL6 target genes accurately identifies BCL6-dependent diffuse large B cell lymphomas</title>        <journal>PNAS</journal>        <issue>2007 Feb 27;104(9):3207-12. Epub 2007 Feb 20.</issue>        <pubdate>2007-02-27</pubdate>        <epubdate>2007-02-20</epubdate>        <url>http://dx.doi.org/10.1073/pnas.0611399104</url>        <url_pdf></url_pdf>        <url_supplemental>http://www.pnas.org/cgi/content/full/0611399104/DC1</url_supplemental>        <abstract>Diffuse large B cell lymphomas (DLBCLs) often express BCL6, a transcriptional repressor required for the formation of normal germinal centers. In a subset of DLBCLs, BCL6 is deregulated by chromosomal translocations or aberrant somatic hypermutation; in other tumors, BCL6 expression may simply reflect germinal center lineage. DLBCLs dependent on BCL6-regulated pathways should exhibit differential regulation of BCL6 target genes. Genomic array ChIP-on-chip was used to identify the cohort of direct BCL6 target genes. This set of genes was enriched in modulators of transcription, chromatin structure, protein ubiquitylation, cell cycle, and DNA damage responses. In primary DLBCLs classified on the basis of gene expression profiles, these BCL6 target genes were clearly differentially regulated in "BCR" tumors, a subset of DLBCLs with increased BCL6 expression and more frequent BCL6 translocations. In a panel of DLBCL cell lines analyzed by expression arrays and classified according to their gene expression profiles, only BCR tumors were highly sensitive to the BCL6 peptide inhibitor, BPI. These studies identify a discrete subset of DLBCLs that are reliant on BCL6 signaling and uniquely sensitive to BCL6 inhibitors. More broadly, these data show how genome-wide identification of direct target genes can identify tumors dependent on oncogenic transcription factors and amenable to targeted therapeutics.</abstract>        <author>            <id>1</id>            <author_shortname>Polo JM</author_shortname>            <author_fullname>Jose M. Polo</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Juszczynski P</author_shortname>            <author_fullname>Przemyslaw Juszczynski</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Monti S</author_shortname>            <author_fullname>Stefano Monti</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Cerchietti L</author_shortname>            <author_fullname>Leandro Cerchietti</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Ye K</author_shortname>            <author_fullname>Kenny Ye</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Greally JM</author_shortname>            <author_fullname>John M. Greally</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Shipp M</author_shortname>            <author_fullname>Margaret Shipp</author_fullname>            <author_affiliation>2,6</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Melnick A</author_shortname>            <author_fullname>Ari Melnick</author_fullname>            <author_affiliation>1,7</author_affiliation>        </author>        <institution>Department of Developmental and Molecular Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461</institution>        <institution>Department of Medical Oncology, Dana–Farber Cancer Institute, 44 Binney Street, Boston, MA 02115</institution>        <institution>Broad Institute, 320 Charles Street, Cambridge, MA 02141</institution>        <institution>Department of Epidemiology and Biostatistics, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461</institution>        <institution>Department of Medical Genetics, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461</institution>        <institution>To whom correspondence may be addressed. E-mail: margaret_shipp@dfci.harvard.edu</institution>        <institution>To whom correspondence may be addressed. E-mail: amelnick@aecom.yu.edu</institution>	</publication>	<publication pub_id="106">        <status>On</status>        <application>CGS</application>        <title>Mutations in rsmG, Encoding a 16S rRNA Methyltransferase, Result in Low-level Streptomycin Resistance and Antibiotic Overproduction in Streptomyces coelicolor A3(2)</title>        <journal>J. Bacteriol.</journal>        <issue>2007 May;189(10):3876-83. Epub 2007 Mar 23.</issue>        <pubdate>2007-05-01</pubdate>        <epubdate>2007-03-23</epubdate>        <url>http://dx.doi.org/10.1128/JB.01776-06</url>        <url_pdf></url_pdf>        <url_supplemental>http://jb.asm.org/cgi/content/full/189/10/3876/DC1</url_supplemental>        <abstract>Certain str mutations that confer high- or low-level streptomycin resistance result in overproduction of antibiotics by Streptomyces spp. The str mutations that confer the high-level resistance occur within rpsL, which encodes the ribosomal protein S12, while those that cause low-level resistance are not as well known. We have used comparative genome sequencing to determine that low-level resistance is caused by mutations of rsmG, which encodes an S-adenosylmethionine (SAM)-dependent 16S rRNA methyltransferase containing a SAM binding motif. Deletion of rsmG from wild-type S. coelicolor resulted in acquisition of streptomycin resistance and overproduction of the antibiotic actinorhodin. Introduction of wild-type rsmG into the deletion mutant completely abrogated the effects of the rsmG deletion, confirming that rsmG mutation underlies the observed phenotype. Consistent with earlier work using a spontaneous rsmG mutant, the ΔrsmG strain exhibited increased SAM synthetase activity, which mediated the overproduction of antibiotic. Moreover, HPLC analysis showed that the ΔrsmG mutant lacked a 7-methylguanosine modification in the 16S rRNA (possibly at position G518, which corresponds to G527 of E. coli). Like certain rpsL mutants, the ΔrsmG mutant exhibited enhanced protein synthetic activity during the late growth phase. Unlike rpsL mutants, however, the ΔrsmG mutant showed neither greater stability of the 70S ribosomal complex nor increased expression of ribosome recycling factor, suggesting that the mechanism underlying increased protein synthesis differs in rsmG and rpsL mutants. Finally, spontaneous rsmG mutations arose at a 100~1,000-fold higher frequency than rpsL mutations. These findings provide new insight into the role of rRNA modification in activating secondary metabolism in Streptomyces.</abstract>        <author>            <id>1</id>            <author_shortname>Nishimura K</author_shortname>            <author_fullname>Kenji Nishimura</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Hosaka T</author_shortname>            <author_fullname>Takeshi Hosaka</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Tokuyama S</author_shortname>            <author_fullname>Shinji Tokuyama</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Okamoto S</author_shortname>            <author_fullname>Susumu Okamoto</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Ochi K</author_shortname>            <author_fullname>Kozo Ochi</author_fullname>            <author_affiliation>1,2</author_affiliation>        </author>        <institution>National Food Research Institute, Tsukuba, Ibaraki 305-8642, and Department of Applied Biological Chemistry, Faculty of Agriculture, Shizuoka University, Shizuoka 422-8529, Japan</institution>        <institution>To whom correspondence should be addressed. Email: kochi@affrc.go.jp</institution>    </publication>	<publication pub_id="107">        <status>On</status>        <application>CGS</application>        <title>Loss of a conserved 7-methylguanosine modification in 16S rRNA confers low-level streptomycin resistance in bacteria</title>        <journal>Mol. Microbiol.</journal>        <issue>2007 Feb;63(4):1096-106.</issue>        <pubdate>2007-02-01</pubdate>        <epubdate>2007-01-18</epubdate>        <url>http://dx.doi.org/10.1111/j.1365-2958.2006.05585.x</url>        <url_pdf>http://www3.interscience.wiley.com/cgi-bin/fulltext/118541847/PDFSTART</url_pdf>        <url_supplemental></url_supplemental>        <abstract>Streptomycin has been an important drug for the treatment of tuberculosis since its discovery in 1944. But numerous strains of Mycobacterium tuberculosis, the bacterial pathogen that causes tuberculosis, are now streptomycin resistant. Although such resistance is often mediated by mutations within rrs, a 16S rRNA gene or rpsL, which encodes the ribosomal protein S12, these mutations are found in a limited proportion of clinically isolated streptomycin-resistant M. tuberculosis strains. Here we have succeeded in identifying a mutation that confers low-level streptomycin resistance to bacteria, including M. tuberculosis. We found that mutations within the gene gidB confer low-level streptomycin resistance and are an important cause of resistance found in 33% of resistant M. tuberculosis isolates. We further clarified that the gidB gene encodes a conserved 7-methylguanosine (m7G) methyltransferase specific for the 16S rRNA, apparently at position G527 located in the so-called 530 loop. Thus, we have identified gidB as a new streptomycin-resistance locus and uncovered a resistance mechanism that is mediated by loss of a conserved m7G modification in 16S rRNA. The clinical significance of M. tuberculosis gidB mutation also is noteworthy, as gidB mutations emerge spontaneously at a high frequency of 10-6 and, once emerged, result in vigorous emergence of high-level streptomycin-resistant mutants at a frequency more than 2000 times greater than that seen in wild-type strains. Further studies on the precise function of GidB may provide a basis for developing strategies to suppress pathogenic bacteria, including M. tuberculosis.</abstract>        <author>            <id>1</id>            <author_shortname>Okamoto S</author_shortname>            <author_fullname>Susumu Okamoto</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Tamaru A</author_shortname>            <author_fullname>Aki Tamaru</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Nakajima C</author_shortname>            <author_fullname>Chie Nakajima</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Nishimura K</author_shortname>            <author_fullname>Kenji Nishimura</author_fullname>            <author_affiliation>1,4</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Tanaka Y</author_shortname>            <author_fullname>Yukinori Tanaka</author_fullname>            <author_affiliation>1,4</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Tokuyama S</author_shortname>            <author_fullname>Shinji Tokuyama</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Suzuki Y</author_shortname>            <author_fullname>Yasuhiko Suzuki</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Ochi K</author_shortname>            <author_fullname>Kozo Ochi</author_fullname>            <author_affiliation>1,5</author_affiliation>        </author>        <institution>Microbial Function Laboratory, National Food Research Institute, 2-1-12 Kannondai, Tsukuba, Ibaraki 305-8642, Japan.</institution>        <institution>Bacteriology Division, Osaka Prefectural Institute of Public Health, 1-3-69 Nakamichi, Higashinari-ku, Osaka 537-0025, Japan.</institution>        <institution>Department of Global Epidemiology, Research Center for Zoonosis Control, Hokkaido University, Kita 18, Nishi 9, Kita-ku, Sapporo 060-0818, Japan.</institution>        <institution>Department of Applied Biological Chemistry, Faculty of Agriculture, Shizuoka University, 836 Ohya, Shizuoka 422-8529, Japan.</institution>        <institution>E-mail kochi@affrc.go.jp; Tel. (+81) 29 838 8125; Fax (+81) 29 838 7996.</institution>    </publication>	<publication pub_id="108">        <status>On</status>        <application>CGH</application>        <title>Defining Genomic Islands and Uropathogen-Specific Genes in Uropathogenic Escherichia coli</title>        <journal>J. Bacteriol.</journal>        <issue>2007 May;189(9):3532-46. Epub 2007 Mar 9.</issue>        <pubdate>2007-05-01</pubdate>        <epubdate>2007-03-09</epubdate>        <url>http://dx.doi.org/10.1128/JB.01744-06</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>Uropathogenic Escherichia coli (UPEC) strains are responsible for the majority of uncomplicated urinary tract infections, which can present clinically as cystitis or pyelonephritis. UPEC strain CFT073, isolated from the blood of a patient with acute pyelonephritis, was most cytotoxic and most virulent in mice among our strain collection. Based on the genome sequence of CFT073, microarrays were utilized in comparative genomic hybridization (CGH) analysis of a panel of uropathogenic and fecal/commensal E. coli isolates. Genomic DNA from seven UPEC (three pyelonephritis and four cystitis) isolates and three fecal/commensal strains, including K-12 MG1655, was hybridized to the CFT073 microarray. The CFT073 genome contains 5,379 genes; CGH analysis revealed that 2,820 (52.4%) of these genes were common to all 11 E. coli strains, yet only 173 UPEC-specific genes were found by CGH to be present in all UPEC strains but in none of the fecal/commensal strains. When the sequences of three additional sequenced UPEC strains (UTI89, 536, and F11) and a commensal strain (HS) were added to the analysis, 131 genes present in all UPEC strains but in no fecal/commensal strains were identified. Seven previously unrecognized genomic islands (>30 kb) were delineated by CGH in addition to the three known pathogenicity islands. These genomic islands comprise 672 kb of the 5,231-kb (12.8%) genome, demonstrating the importance of horizontal transfer for UPEC and the mosaic structure of the genome. UPEC strains contain a greater number of iron acquisition systems than do fecal/commensal strains, which is reflective of the adaptation to the iron-limiting urinary tract environment. Each strain displayed distinct differences in the number and type of known virulence factors. The large number of hypothetical genes in the CFT073 genome, especially those shown to be UPEC specific, strongly suggests that many urovirulence factors remain uncharacterized.</abstract>        <author>            <id>1</id>            <author_shortname>Lloyd AL</author_shortname>            <author_fullname>Amanda L. Lloyd</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Rasko DA</author_shortname>            <author_fullname>David A. Rasko</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Mobley HL</author_shortname>            <author_fullname>Harry L. T. Mobley</author_fullname>            <author_affiliation>1,3</author_affiliation>        </author>        <institution>Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan 48109</institution>        <institution>Department of Microbiology, University of Texas Southwestern Medical Center, Dallas, Texas 75235</institution>        <institution>Corresponding author. Mailing address: Department of Microbiology and Immunology, University of Michigan Medical School, 5641 Medical Science Bldg. II, 1150 West Medical Center Dr., Ann Arbor, MI 48109-0620. Phone: (734) 764-1466. Fax: (734) 763-7163. E-mail: hmobley@med.umich.edu</institution>    </publication>	<publication pub_id="109">        <status>On</status>        <application>ROMA</application>        <title>Strong Association of De Novo Copy Number Mutations with Autism</title>        <journal>Science</journal>        <issue>2007 Apr 20;316(5823):445-9. Epub 2007 Mar 15.</issue>        <pubdate>2007-04-20</pubdate>        <epubdate>2007-03-15</epubdate>        <url>http://dx.doi.org/10.1126/science.1138659</url>        <url_pdf></url_pdf>        <url_supplemental>http://www.sciencemag.org/cgi/content/full/sci;1138659/DC1</url_supplemental>        <abstract>We tested the hypothesis that de novo copy number variation (CNV) is associated with autism spectrum disorders (ASDs). We performed comparative genomic hybridization (CGH) on the genomic DNA of patients and unaffected subjects to detect copy number variants not present in their respective parents. Candidate genomic regions were validated by higher-resolution CGH, paternity testing, cytogenetics, fluorescence in situ hybridization, and microsatellite genotyping. Confirmed de novo CNVs were significantly associated with autism (P = 0.0005). Such CNVs were identified in 12 out of 118 (10%) of patients with sporadic autism, in 2 out of 77 (3%) of patients with an affected first-degree relative, and in 2 out of 196 (1%) of controls. Most de novo CNVs were smaller than microscopic resolution. Affected genomic regions were highly heterogeneous and included mutations of single genes. These findings establish de novo germline mutation as a more significant risk factor for ASD than previously recognized.</abstract>        <author>            <id>1</id>            <author_shortname>Sebat J</author_shortname>            <author_fullname>Jonathan Sebat</author_fullname>            <author_affiliation>1,15</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Lakshmi B</author_shortname>            <author_fullname>B. Lakshmi</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Malhotra D</author_shortname>            <author_fullname>Dheeraj Malhotra</author_fullname>            <author_affiliation>1,15</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Troge J</author_shortname>            <author_fullname>Jennifer Troge</author_fullname>            <author_affiliation>1,15</author_affiliation>        </author>        <author>            <id>5</id>            <author_shortname>Lese-Martin C</author_shortname>            <author_fullname>Christa Lese-Martin</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Walsh T</author_shortname>            <author_fullname>Tom Walsh</author_fullname>            <author_affiliation>3</author_affiliation>        </author>		<author>            <id>7</id>            <author_shortname>Yamrom B</author_shortname>            <author_fullname>Boris Yamrom</author_fullname>            <author_affiliation>1</author_affiliation>        </author>         <author>            <id>8</id>            <author_shortname>Yoon S</author_shortname>            <author_fullname>Seungtai Yoon</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>9</id>            <author_shortname>Krasnitz A</author_shortname>            <author_fullname>Alex Krasnitz</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>10</id>            <author_shortname>Kendall J</author_shortname>            <author_fullname>Jude Kendall</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>11</id>            <author_shortname>Leotta A</author_shortname>            <author_fullname>Anthony Leotta</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>12</id>            <author_shortname>Pai D</author_shortname>            <author_fullname>Deepa Pai</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>13</id>            <author_shortname>Zhang R</author_shortname>            <author_fullname>Ray Zhang</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>14</id>            <author_shortname>Lee YH</author_shortname>            <author_fullname>Yoon-Ha Lee</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>15</id>            <author_shortname>Hicks J</author_shortname>            <author_fullname>James Hicks</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>16</id>            <author_shortname>Spence SJ</author_shortname>            <author_fullname>Sarah J. Spence</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>17</id>            <author_shortname>Lee AT</author_shortname>            <author_fullname>Annette T. Lee</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>18</id>            <author_shortname>Puura K</author_shortname>            <author_fullname>Kaija Puura</author_fullname>            <author_affiliation>6</author_affiliation>        </author>        <author>            <id>19</id>            <author_shortname>Lehtimaki T</author_shortname>            <author_fullname>Terho Lehtim&#228;ki</author_fullname>            <author_affiliation>7</author_affiliation>        </author>        <author>            <id>20</id>            <author_shortname>Ledbetter D</author_shortname>            <author_fullname>David Ledbetter</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>21</id>            <author_shortname>Gregersen PK</author_shortname>            <author_fullname>Peter K. Gregersen</author_fullname>            <author_affiliation>5</author_affiliation>        </author>        <author>            <id>22</id>            <author_shortname>Bregman J</author_shortname>            <author_fullname>Joel Bregman</author_fullname>            <author_affiliation>8</author_affiliation>        </author>        <author>             <id>23</id>            <author_shortname>Sutcliffe JS</author_shortname>            <author_fullname>James S. Sutcliffe</author_fullname>            <author_affiliation>9</author_affiliation>        </author>        <author>            <id>24</id>            <author_shortname>Jobanputra V</author_shortname>            <author_fullname>Vaidehi Jobanputra</author_fullname>            <author_affiliation>10</author_affiliation>        </author>        <author>            <id>25</id>            <author_shortname>Chung W</author_shortname>            <author_fullname>Wendy Chung</author_fullname>            <author_affiliation>10</author_affiliation>        </author>        <author>            <id>26</id>            <author_shortname>Warburton D</author_shortname>            <author_fullname>Dorothy Warburton</author_fullname>            <author_affiliation>10</author_affiliation>        </author>        <author>            <id>27</id>            <author_shortname>King MC</author_shortname>            <author_fullname>Mary-Claire King</author_fullname>            <author_affiliation>3</author_affiliation>        </author>        <author>            <id>28</id>            <author_shortname>Skuse D</author_shortname>            <author_fullname>David Skuse</author_fullname>            <author_affiliation>11</author_affiliation>        </author>        <author>            <id>29</id>            <author_shortname>Geschwind DH</author_shortname>            <author_fullname>Daniel H. Geschwind</author_fullname>            <author_affiliation>12</author_affiliation>        </author>        <author>            <id>30</id>            <author_shortname>Gilliam TC</author_shortname>            <author_fullname>T. Conrad Gilliam</author_fullname>            <author_affiliation>13</author_affiliation>        </author>        <author>            <id>31</id>            <author_shortname>Ye K</author_shortname>            <author_fullname>Kenny Ye</author_fullname>            <author_affiliation>14</author_affiliation>        </author>        <author>            <id>32</id>            <author_shortname>Wigler M</author_shortname>            <author_fullname>Michael Wigler</author_fullname>            <author_affiliation>1,16</author_affiliation>        </author>        <institution>Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA.</institution>        <institution>Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA.</institution>        <institution>Department of Medicine and Genome Sciences, University of Washington, Seattle, WA 98195–7720, USA.</institution>        <institution>Pediatrics and Neurodevelopmental Psychiatry Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892–1255, USA.</institution>        <institution>Feinstein Institute for Medical Research, North Shore–Long Island Jewish Health System, Manhasset, NY 11030, USA.</institution>        <institution>Department of Child Psychiatry, University of Tampere, Medical School, Tampere, Finland.</institution>        <institution>Department of Clinical Chemistry, University Hospital of Tampere and University of Tampere, Medical School, Tampere, Finland.</institution>        <institution>Fay J. Lindner Center for Autism and Developmental Disorders, North Shore–Long Island Jewish Health System, 4300 Hempstead Turnpike, Bethpage, NY 11714, USA.</institution>         <institution>Center for Molecular Neuroscience, Vanderbilt University, Nashville, TN 37232–8548, USA.</institution>        <institution>Departments of Genetics and Development, and Pediatrics, Columbia University, New York, NY 10027, USA.</institution>        <institution>Behavioural and Brain Sciences Unit, Institute of Child Health, University College London, 30 Guilford Street, London WCIN 1EH, UK.</institution>        <institution>Interdepartmental Program in the Neurosciences, Program in Neurogenetics, Neurology Department, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095–1769, USA.</institution>        <institution>Department of Human Genetics, The University of Chicago, 920 East 58th Street, Chicago, IL 60637, USA.</institution>        <institution>Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA.</institution>        <institution>These authors contributed equally to this work.</institution>        <institution>To whom correspondence should be addressed. E-mail: sebat@cshl.edu (J.S.); wigler@cshl.edu (M.W.)</institution>    </publication>	<publication pub_id="110">        <status>On</status>        <application>Expression</application>        <title>Comparative high-density microarray analysis of gene expression during growth of Lactobacillus helveticus in milk versus rich culture medium</title>        <journal>Appl. Environ. Microbiol.</journal>        <issue>73(8):2661-72. Epub 2007 Feb 23</issue>        <pubdate>2007-04-01</pubdate>        <epubdate>2007-02-23</epubdate>        <url>http://dx.doi.org/10.1128/AEM.00005-07</url>        <url_pdf></url_pdf>        <url_supplemental></url_supplemental>        <abstract>Lactobacillus helveticus CNRZ32 is used by the dairy industry to modulate cheese flavor. The compilation of a draft genome sequence for this strain allowed us to identify and completely sequence 168 genes potentially important for the growth of this organism in milk or for cheese flavor development. The primary aim of this study was to investigate the expression of these genes during growth in milk and MRS medium by using microarrays. Oligonucleotide probes against each of the completely sequenced genes were compiled on maskless photolithography-based DNA microarrays. Additionally, the entire draft genome sequence was used to produce tiled microarrays in which noninterrupted sequence contigs were covered by consecutive 24-mer probes and associated mismatch probe sets. Total RNA isolated from cells grown in skim milk or in MRS to mid-log phase was used as a template to synthesize cDNA, followed by Cy3 labeling and hybridization. An analysis of data from annotated gene probes identified 42 genes that were upregulated during the growth of CNRZ32 in milk (P &#60; 0.05), and 25 of these genes showed upregulation after applying Bonferroni's adjustment. The tiled microarrays identified numerous additional genes that were upregulated in milk versus MRS. Collectively, array data showed the growth of CNRZ32 in milk-induced genes encoding cell-envelope proteinases, oligopeptide transporters, and endopeptidases as well as enzymes for lactose and cysteine pathways, de novo synthesis, and/or salvage pathways for purines and pyrimidines and other functions. Genes for a hypothetical phosphoserine utilization pathway were also differentially expressed. Preliminary experiments indicate that cheese-derived, phosphoserine-containing peptides increase growth rates of CNRZ32 in a chemically defined medium. These results suggest that phosphoserine is used as an energy source during the growth of L. helveticus CNRZ32.</abstract>        <author>            <id>1</id>            <author_shortname>Smeianov VV</author_shortname>            <author_fullname>Vladimir V. Smeianov</author_fullname>            <author_affiliation>1</author_affiliation>        </author>        <author>            <id>2</id>            <author_shortname>Wechter P</author_shortname>            <author_fullname>Patrick Wechter</author_fullname>            <author_affiliation>1,5</author_affiliation>        </author>        <author>            <id>3</id>            <author_shortname>Broadbent JR</author_shortname>            <author_fullname>Jeffery R. Broadbent</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>4</id>            <author_shortname>Hughes JE</author_shortname>            <author_fullname>Joanne E. Hughes</author_fullname>            <author_affiliation>3</author_affiliation>        </author>		<author>            <id>5</id>            <author_shortname>Rodriguez BT</author_shortname>            <author_fullname>Beatriz T. Rodríguez</author_fullname>            <author_affiliation>2</author_affiliation>        </author>        <author>            <id>6</id>            <author_shortname>Christensen TK</author_shortname>            <author_fullname>Tove K. Christensen</author_fullname>            <author_affiliation>4,6</author_affiliation>        </author>        <author>            <id>7</id>            <author_shortname>Ardo Y</author_shortname>            <author_fullname>Ylva Ardö</author_fullname>            <author_affiliation>4</author_affiliation>        </author>        <author>            <id>8</id>            <author_shortname>Steele JL</author_shortname>            <author_fullname>James L. Steele</author_fullname>            <author_affiliation>1,7</author_affiliation>        </author>        <institution>Department of Food Science, University of Wisconsin—Madison, Madison, Wisconsin</institution>        <institution>Department of Nutrition and Food Sciences, Utah State University, Logan, Utah</institution>        <institution>Department of Biology, Utah State University, Logan, Utah</institution>        <institution>Department of Food Science, The Royal Veterinary and Agricultural University, Frederiksberg, Denmark</institution>		<institution>Present address: U.S. Vegetable Laboratory, 2700 Savannah Highway, Charleston, SC 29414.</institution>        <institution>Present address: Lactosan A/S, Nordbakken 2, DK-5750 Ringe, Denmark.</institution>        <institution>Corresponding author. Mailing address: 1605 Linden Dr., Department of Food Science, University of Wisco