 |
|
2.1M Whole-Genome Tiling Sets
The higher density of the 2.1M array format means fewer arrays are needed to analyze the whole genome of complex organisms at high resolution. Whole-genome formats provide two options: a 10-array set at 100bp probe interval or a 4-array set at >200bp probe interval. For model organisms, a single 2.1M array slide has enough capacity to tile through the entire genome (55bp probe interval for D. melanogaster and 40bp probe interval for C. elegans).
385K Whole-Genome Tiling Sets
These sets tile uniformly across all unique regions of a genome at an average probe spacing of 100bp or less. A. thaliana, C. elegans, D. melanogaster, E. coli and S. cerevisiae are currently available.
For all Whole-Genome designs, you have the option of ordering the entire genome set or individual arrays within the set.
Arabidopsis thaliana
| Description |
 |
Catalog Number |
Design Name |
Chromo-
some |
Tiling Start |
Tiling Stop |
| Whole Genome 3-Array Set |
 |
05224209001 |
A. tha ChIP 385K WG-T Set-3 Arr Del |
 |
05224217001 |
A. tha ChIP 385K WG-T Set-3 Arr Ser |
|
See Below |
| Array 1 of 3 |
|
05542863001 |
A. tha ChIP 385K WG-T 1-3 Arr Del |
|
05544912001 |
A. tha ChIP 385K WG-T 1-3 Arr Ser |
|
chr1 |
106 |
30,432,534 |
| chr2 |
1,001 |
9,687,876 |
| Array 2 of 3 |
|
05542871001 |
A. tha ChIP 385K WG-T 2-3 Arr Del |
|
05544939001 |
A. tha ChIP 385K WG-T 2-3 Arr Ser |
|
chr2 |
9,687,916 |
19,704,755 |
| chr3 |
1 |
23,470,742 |
| chr4 |
1,001 |
6,133,069 |
| Array 3 of 3 |
|
05542880001 |
A. tha ChIP 385K WG-T 3-3 Arr Del |
|
05544947001 |
A. tha ChIP 385K WG-T 3-3 Arr Ser |
|
chr4 |
6,133,109 |
18,584,924 |
| chr5 |
1 |
26,992,695 |
Caenorhabditis elegans
| 2.1M Caenorhabditis elegans |
| Description |
|
Catalog Number |
Design Name |
Chromo-
some |
Tiling Start |
Tiling Stop |
| Array 1 of 1 |
|
05542316001 |
C. ele ChIP 2.1M WG-T Arr Del |
|
05544378001 |
C. ele ChIP 2.1M WG-T Arr Ser |
|
chr I |
489 |
15,072,205 |
| chr II |
321 |
15,279,085 |
| chr III |
126 |
13,783,367 |
| chr IV |
285 |
17,492,284 |
| chr V |
331 |
20,919,326 |
| chr X |
251 |
17,718,642 |
|
| 385K Caenorhabditis elegans |
| Description |
|
Catalog Number |
Design Name |
Chromo-
some |
Tiling Start |
Tiling Stop |
| Whole Genome 3-Array Set |
|
05224225001 |
C. ele ChIP 385K WG-T Set-3 Arr Del |
|
05224233001 |
C. ele ChIP 385K WG-T Set-3 Arr Ser |
|
See Below |
| Array 1 of 3 |
 |
C4533-01-01 |
C elegans ChIP01 |
|
chrI |
1 |
15,080,469 |
| chrII |
1 |
15,279,267 |
| chrIII |
1 |
3,178,860 |
| Array 2 of 3 |
|
C4533-02-01 |
C elegans ChIP02 |
|
chrIII |
3,178,896 |
13,783,302 |
| chrIV |
1 |
17,493,781 |
| chrM |
1 |
13,738 |
| chrV |
1 |
5,341,967 |
| Array 3 of 3 |
|
C4533-03-01 |
C elegans ChIP03 |
|
chrV |
5,342,003 |
20,922,215 |
| chrX |
1 |
17,718,822 |
|
Drosophila melanogaster
| 2.1M Drosophila melanogaster |
| Description |
|
Catalog Number |
Design Name |
Chromo-
some |
Tiling Start |
Tiling Stop |
| Array 1 of 1 |
|
05542308001 |
D. mel ChIP 2.1M WG-T Arr Del |
|
05544360001 |
D. mel ChIP 2.1M WG-T Arr Ser |
|
chr 2L |
5131 |
23,011,180 |
| chr2Lhet |
2,538 |
367,898 |
| chr 2Rhet |
11 |
3,288,675 |
| chr 2R |
3036 |
21,146,403 |
| chr 3L |
19,811 |
24,541,069 |
| chr 3Lhet |
231 |
2,555,369 |
| chr 3R |
6 |
27,898,700 |
| chr 3Rhet |
1 |
2,517,409 |
| chr 4 |
1 |
1,347,833 |
| chr M |
6 |
19,373 |
| chr U |
2,051 |
10,048,502 |
| chr X |
116 |
22,422,248 |
| chr Xhet |
666 |
203,681 |
| chr Yhet |
1 |
346,895 |
|
| 385K Drosophila melanogaster |
| Description |
|
Catalog Number |
Design Name |
Chromo-
some |
Tiling Start |
Tiling Stop |
| Whole Genome 3 Array Set |
|
05224268001 |
D. mel ChIP 385K WG-T Set-3 Arr Del |
|
05224276001 |
D. mel ChIP 385K WG-T Set-3 Arr Ser |
|
See Below |
| Array 1 of 3 |
|
C4532-01-01 |
DMEL ChIP Set 1 |
|
chr2L |
155 |
22,403,093 |
| chr2R |
1 |
18,175,156 |
| Array 2 of 3 |
|
C4532-02-01 |
DMEL ChIP Set 2 |
|
chr2R |
18,175,203 |
20,766,579 |
| chr2h |
1,769 |
1,693,289 |
| chr3L |
94 |
23,764,120 |
| chr3R |
1 |
13,202,299 |
| Array 3 of 3 |
|
C4532-03-01 |
DMEL ChIP Set 3 |
|
chr3R |
13,202,346 |
27,904,842 |
| chr3h |
436 |
2,953,888 |
| chr4 |
1,724 |
1,281,635 |
| chr4h |
2,261 |
88,040 |
| chrM |
1 |
14,774 |
| chrU |
168,223 |
8,721,504 |
| chrX |
10,779 |
22,223,781 |
| chrXh |
3,311 |
358,411 |
| chrYh |
1,862 |
394,283 |
|
Human
| 2.1M Human |
| Description |
|
Catalog Number |
Design Name |
Chromo-
some |
Tiling Start |
Tiling Stop |
| Whole Genome 10 Array Set |
|
05327768001 |
Human ChIP 2.1M WG-T Set-10 Arr Del |
|
05340624001 |
Human ChIP 2.1M WG-T Set-10 Arr Ser |
|
See Below |
| Array 1 of 10 |
|
05542359001 |
Human ChIP 2.1M WG-T 1-10 Arr Del |
|
05544408001 |
Human ChIP 2.1M WG-T 1-10 Arr Ser |
|
chr1 |
505 |
247,195,005 |
| chr2 |
321 |
60,027,826 |
| Array 2 of 10 |
|
05542367001 |
Human ChIP 2.1M WG-T 2-10 Arr Del |
|
05544416001 |
Human ChIP 2.1M WG-T 2-10 Arr Ser |
|
chr2 |
6,002,792 |
242,951,119 |
| chr3 |
35,001 |
106,782,710 |
| Array 3 of 10 |
|
05542375001 |
Human ChIP 2.1M WG-T 3-10 Arr Del |
|
05544424001 |
Human ChIP 2.1M WG-T 3-10 Arr Ser |
|
chr3 |
106,782,825 |
199,501,827 |
| chr4 |
216 |
191,273,063 |
| chr5 |
64,925 |
2,304,112 |
| Array 4 of 10 |
|
05542383001 |
Human ChIP 2.1M WG-T 4-10 Arr Del |
|
05544432001 |
Human ChIP 2.1M WG-T 4-10 Arr Ser |
|
chr5 |
2,304,217 |
180,857,866 |
| chr6 |
5,001 |
111,919,925 |
| Array 5 of 10 |
|
05542391001 |
Human ChIP 2.1M WG-T 5-10 Arr Del |
|
05544459001 |
Human ChIP 2.1M WG-T 5-10 Arr Ser |
|
chr6 |
111,920,005 |
170,899,992 |
| chr7 |
49,317 |
158,821,424 |
| chr8 |
521 |
74,730,105 |
| Array 6 of 10 |
|
05542405001 |
Human ChIP 2.1M WG-T 6-10 Arr Del |
|
05544467001 |
Human ChIP 2.1M WG-T 6-10 Arr Ser |
|
chr8 |
74,730,205 |
146,274,826 |
| chr9 |
516 |
140,273,252 |
| chr10 |
1,773,517 |
101,402,412 |
| Array 7 of 10 |
|
05542413001 |
Human ChIP 2.1M WG-T 7-10 Arr Del |
|
05544475001 |
Human ChIP 2.1M WG-T 7-10 Arr Ser |
|
chr10 |
101,402,517 |
135,374,737 |
| chr11 |
62,725 |
134,452,384 |
| chr12 |
17,426 |
121,385,801 |
| Array 8 of 10 |
|
05542421001 |
Human ChIP 2.1M WG-T 8-10 Arr Del |
|
05544483001 |
Human ChIP 2.1M WG-T 8-10 Arr Ser |
|
chr12 |
121,385,901 |
132,349,534 |
| chr13 |
17,918,026 |
114,142,980 |
| chr14 |
18,070,206 |
106,368,585 |
| chr15 |
18,260,026 |
100,338,915 |
| chr16 |
23 |
6,248,313 |
| Array 9 of 10 |
|
05542430001 |
Human ChIP 2.1M WG-T 9-10 Arr Del |
|
05544491001 |
Human ChIP 2.1M WG-T 9-10 Arr Ser |
|
chr16 |
6,248,403 |
88,827,254 |
| chr17 |
16 |
78,774,742 |
| chr18 |
623 |
76,117,153 |
| chr19 |
11,316 |
63,811,651 |
| chr20 |
8,016 |
16,260,605 |
| Array 10 of 10 |
|
05542448001 |
Human ChIP 2.1M WG-T 10-10 Arr Del |
|
05544505001 |
Human ChIP 2.1M WG-T 10-10 Arr Ser |
|
chr20 |
16,260,705 |
62,435,704 |
| chr21 |
9,720,013 |
46,944,102 |
| chr22 |
14,430,026 |
49,590,821 |
| chrX |
112 |
154,912,711 |
| chrY |
112 |
57,771,911 |
| chr1 random |
721 |
1,663,126 |
| chr2 random |
21 |
185,401 |
| chr3 random |
16 |
749,118 |
| chr4 random |
16 |
842,224 |
| chr5 random |
11 |
143,119 |
| chr6 random |
26 |
1,875,407 |
| chr7 random |
101 |
549,124 |
| chr8 random |
16 |
943,723 |
| chr9 random |
402 |
1,146,305 |
| chr10 random |
6 |
112,020 |
| chr11 random |
26 |
214,804 |
| chr13 random |
6 |
186,723 |
| chr15 random |
1 |
784,221 |
| chr16 random |
6 |
105,409 |
| chr17 random |
26 |
2,617,303 |
| chr18 random |
16 |
4,126 |
| chr19 random |
701 |
301,702 |
| chr21 random |
1 |
1,679,604 |
| chr22 random |
21 |
256,508 |
| chrX random |
6 |
1,718,603 |
|
| 2.1M Human - Economy |
| Description |
|
Catalog Number |
Design Name |
Chromo-
some |
Tiling Start |
Tiling Stop |
| Whole Genome 4 Array Set |
|
05327890001 |
Human ChIP 2.1M Econ WG-T Set-4 Arr Del |
|
05340632001 |
Human ChIP 2.1M Econ WG-T Set-4 Arr Ser |
|
See Below |
| Array 1 of 4 |
|
05542456001 |
Human ChIP 2.1M Econ WG-T 1-4 Arr Del |
|
05544513001 |
Human ChIP 2.1M Econ WG-T 1-4 Arr Ser |
|
chr1 |
1,313 |
247,195,565 |
| chr1 random |
1,611 |
1,662,469 |
| chr2 |
2,201 |
242,751,149 |
| chr2 random |
21 |
185,571 |
| chr3 |
35,003 |
199,434,305 |
| chr3 random |
1 |
749,178 |
| ch4 |
6,126 |
44,383,123 |
| Array 2 of 4 |
|
05542464001 |
Human ChIP 2.1M Econ WG-T 2-4 Arr Del |
|
05544521001 |
Human ChIP 2.1M Econ WG-T 2-4 Arr Ser |
|
chr4 |
44,382,481 |
191,260,303 |
| chr4 random |
1 |
842,414 |
| chr5 |
64,884 |
180,837,023 |
| chr5 random |
230 |
141,377 |
| chr6 |
5,003 |
170,896,992 |
| chr6 random |
443 |
1,875,467 |
| chr7 |
34,003 |
158,821,318 |
| chr7 random |
68 |
547,255 |
| chr8 |
1 |
72,911,264 |
| Array 3 of 4 |
|
05542472001 |
Human ChIP 2.1M Econ WG-T 3-4 Arr Del |
|
05544530001 |
Human ChIP 2.1M Econ WG-T 3-4 Arr Ser |
|
chr8 |
72,910,390 |
146,273,068 |
| chr8 random |
621 |
943,633 |
| chr9 |
1,577 |
140,269,382 |
| chr9 random |
1,152 |
1,146,350 |
| chr10 |
50,003 |
135,372,404 |
| chr10 random |
1 |
113,198 |
| chr11 |
50,003 |
134,450,777 |
| chr11 random |
1 |
214,910 |
| chr12 |
17,432 |
132,289,534 |
| chr13 |
17,918,003 |
114,127,903 |
| chr13 random |
1 |
186,321 |
| chr14 |
18,126,854 |
41,598,996 |
| Array 4 of 4 |
|
05542499001 |
Human ChIP 2.1M Econ WG-T 4-4 Arr Del |
|
05544548001 |
Human ChIP 2.1M Econ WG-T 4-4 Arr Ser |
|
chr14 |
41,599,466 |
106,360,585 |
| chr15 |
18,303,913 |
100,337,245 |
| chr15 random |
1 |
784,131 |
| chr16 |
1,145 |
88,820,562 |
| chr16 random |
487 |
105,389 |
| chr17 |
1 |
78,654,674 |
| chr17 random |
1 |
2,617,421 |
| chr18 |
1,148 |
76,116,178 |
| chr18 random |
1 |
4,020 |
| chr19 |
11,003 |
63,806,013 |
| chr19 random |
8,248 |
301,762 |
| chr20 |
8,003 |
62,432,795 |
| chr21 |
9,734,029 |
46,941,590 |
| chr21 random |
1 |
1,679,594 |
| chr22 |
14,430,003 |
49,590,901 |
| chr22 random |
1 |
256,573 |
| chrM |
1 |
16,340 |
| chrX |
449 |
154,911,455 |
| chrX random |
1 |
1,718,581 |
| chrY |
449 |
57,770,655 |
|
Mouse
| 2.1M Mouse |
| Description |
|
Catalog Number |
Design Name |
Chromo-
some |
Tiling Start |
Tiling Stop |
| Whole Genome 10 Array Set |
|
05327903001 |
Human ChIP 2.1M WG-T Set-10 Arr Del |
|
05340659001 |
Mouse ChIP 2.1M WG-T Set-10 Arr Ser |
|
See Below |
| Array 1 of 10 |
|
05542502001 |
Mouse ChIP 2.1M WG-T 1-10 Arr Del |
|
05544556001 |
Mouse ChIP 2.1M WG-T 1-10 Arr Ser |
|
chr1 |
3,000,001 |
197,069,505 |
| chr1 random |
122 |
172,060 |
| chr2 |
3,006,249 |
65,850,316 |
| Array 2 of 10 |
|
05542529001 |
Mouse ChIP 2.1M WG-T 2-10 Arr Del |
|
05544564001 |
Mouse ChIP 2.1M WG-T 2-10 Arr Ser |
|
chr2 |
65,850,426 |
181,926,682 |
| chr3 |
3,000,001 |
143,307,173 |
| Array 3 of 10 |
|
05542537001 |
Mouse ChIP 2.1M WG-T 3-10 Arr Del |
|
05544572001 |
Mouse ChIP 2.1M WG-T 3-10 Arr Ser |
|
chr3 |
143,307,273 |
159,871,956 |
| chr4 |
3,006,417 |
155,029,602 |
| chr5 |
3,000,001 |
92,685,503 |
| Array 4 of 10 |
|
05542545001 |
Mouse ChIP 2.1M WG-T 4-10 Arr Del |
|
05544599001 |
Mouse ChIP 2.1M WG-T 4-10 Arr Ser |
|
chr5 |
92,685,618 |
152,002,850 |
| chr5 random |
26 |
2,921,095 |
| chr6 |
3,000,001 |
149,525,609 |
| chr7 |
3,000,201 |
56,647,386 |
| Array 5 of 10 |
|
05542553001 |
Mouse ChIP 2.1M WG-T 5-10 Arr Del |
|
05544602001 |
Mouse ChIP 2.1M WG-T 5-10 Arr Ser |
|
chr7 |
54,647,476 |
145,133,950 |
| chr7 random |
141 |
243,809 |
| chr8 |
3,000,026 |
132,084,963 |
| chr8 random |
16 |
206,850 |
| chr9 |
3,000,026 |
40,163,952 |
| Array 6 of 10 |
|
05542561001 |
Mouse ChIP 2.1M WG-T 6-10 Arr Del |
|
05544629001 |
Mouse ChIP 2.1M WG-T 6-10 Arr Ser |
|
chr9 |
40,164,052 |
124,000,522 |
| chr9 random |
26 |
16,954 |
| chr10 |
3,000,218 |
129,957,189 |
| chr10 random |
26 |
10,495 |
| chr11 |
3,000,001 |
41,900,738 |
| Array 7 of 10 |
|
05542570001 |
Mouse ChIP 2.1M WG-T 7-10 Arr Del |
|
05544637001 |
Mouse ChIP 2.1M WG-T 7-10 Arr Ser |
|
chr11 |
41,900,844 |
121,798,365 |
| chr12 |
3,000,001 |
120,463,003 |
| chr13 |
3,001,721 |
55,855,799 |
| Array 8 of 10 |
|
05542588001 |
Mouse ChIP 2.1M WG-T 8-10 Arr Del |
|
05544645001 |
Mouse ChIP 2.1M WG-T 8-10 Arr Ser |
|
chr13 |
55,855,898 |
120,614,219 |
| chr13 random |
6 |
436,079 |
| chr14 |
3,001,139 |
123,976,001 |
| chr15 |
3,000,001 |
76,463,380 |
| Array 9 of 10 |
|
05542596001 |
Mouse ChIP 2.1M WG-T 9-10 Arr Del |
|
05544653001 |
Mouse ChIP 2.1M WG-T 9-10 Arr Ser |
|
chr15 |
76,463,500 |
103,492,425 |
| chr15 random |
16 |
105,806 |
| chr16 |
3,000,056 |
98,252,277 |
| chr17 |
3,000,001 |
95,177,318 |
| chr17 random |
51 |
88,976 |
| chr18 |
3,000,001 |
40,478,067 |
| Array 10 of 10 |
|
05542600001 |
Mouse ChIP 2.1M WG-T 10-10 Arr Del |
|
05544661001 |
Mouse ChIP 2.1M WG-T 10-10 Arr Ser |
|
chr18 |
40,478,152 |
90,736,262 |
| chr19 |
3,000,156 |
61,321,048 |
| chrM |
26 |
16,224 |
| chrUn random |
126 |
1,539,898 |
| chrX |
3,000,026 |
165,555,977 |
| chrX random |
326 |
39,548 |
| chrY |
6 |
2,729,267 |
| chrY random |
6,484 |
14,577,549 |
|
| 2.1M Mouse - Economy |
| Description |
|
Catalog Number |
Design Name |
Chromo-
some |
Tiling Start |
Tiling Stop |
| Whole Genome 4 Array Set |
|
05327911001 |
Mouse ChIP 2.1M Econ WG-T Set-4 Arr Del |
|
05340667001 |
Mouse ChIP 2.1M Econ WG-T Set-4 Arr Ser |
|
See Below |
| Array 1 of 4 |
|
05542618001 |
Mouse ChIP 2.1M Econ WG-T 1-4 Arr Del |
|
05544670001 |
Mouse ChIP 2.1M Econ WG-T 1-4 Arr Ser |
|
chr1 |
3,000,159 |
197,066,856 |
| chr1 random |
104 |
172,149 |
| chr2 |
3,006,248 |
181,926,726 |
| chr3 |
3,000,003 |
159,872,112 |
| chr4 |
3,006,469 |
119,314,705 |
| Array 2 of 4 |
|
05542626001 |
Mouse ChIP 2.1M Econ WG-T 2-4 Arr Del |
|
05544688001 |
Mouse ChIP 2.1M Econ WG-T 2-4 Arr Ser |
|
chr4 |
119,312,724 |
155,029,701 |
| chr5 |
3,004,823 |
152,002,929 |
| chr5 random |
357 |
2,921,247 |
| chr6 |
3,000,003 |
149,525,594 |
| chr7 |
3,002,500 |
145,134,020 |
| chr7 random |
830 |
243,836 |
| chr8 |
3,001,012 |
132,084,168 |
| ch8 random |
335 |
206,961 |
| chr9 |
3,038,422 |
39,288,818 |
| Array 3 of 4 |
|
05542634001 |
Mouse ChIP 2.1M Econ WG-T 3-4 Arr Del |
|
05544696001 |
Mouse ChIP 2.1M Econ WG-T 3-4 Arr Ser |
|
chr9 |
39,287,970 |
123,999,613 |
| chr9 random |
1 |
16,784 |
| chr10 |
3,002,173 |
129,952,335 |
| chr10 random |
1 |
10,556 |
| chr11 |
3,005,469 |
121,798,411 |
| chr12 |
3,000,003 |
120,463,159 |
| chr13 |
3,002,645 |
120,614,378 |
| chr13 random |
513 |
436,191 |
| chr14 |
3,000,285 |
57,853,012 |
| Array 4 of 4 |
|
05542642001 |
Mouse ChIP 2.1M Econ WG-T 4-4 Arr Del |
|
05544700001 |
Mouse ChIP 2.1M Econ WG-T 4-4 Arr Ser |
|
chr14 |
57,850,947 |
123,975,935 |
| chr15 |
3,002,688 |
103,492,224 |
| chr15 random |
1 |
105,932 |
| chr16 |
3,001,531 |
98,252,333 |
| chr17 |
3,000,003 |
95,177,217 |
| chr17 random |
61 |
88,403 |
| chr18 |
3,000,971 |
90,736,071 |
| chr19 |
3,000,196 |
61,321,190 |
| chrM |
1 |
16,299 |
| chrUn random |
565 |
1,539,866 |
| chrX |
3,007,929 |
165,556,094 |
| chrX random |
3,484 |
39,696 |
| chrY |
151 |
2,729,286 |
| chrY random |
6,494 |
14,577,732 |
|
Rat
| 2.1M Rat |
| Description |
|
Catalog Number |
Design Name |
Chromo-
some |
Tiling Start |
Tiling Stop |
| Whole Genome 10 Array Set |
|
05327920001 |
Rat ChIP 2.1M WG-T Set-10 Arr Del |
|
05340675001 |
Rat ChIP 2.1M WG-T Set-10 Arr Ser |
|
See Below |
| Array 1 of 10 |
|
05542669001 |
Rat ChIP 2.1M WG-T 1-10 Arr Del |
|
05544718001 |
Rat ChIP 2.1M WG-T 1-10 Arr Ser |
|
chr1 |
1 |
267,910,774 |
| chr1 random |
1 |
3,884,837 |
| chr2 |
26 |
13,557,674 |
| Array 2 of 10 |
|
05542677001 |
Rat ChIP 2.1M WG-T 2-10 Arr Del |
|
05544726001 |
Rat ChIP 2.1M WG-T 2-10 Arr Ser |
|
chr2 |
13,557,774 |
258,200,598 |
| chr2 random |
26,460 |
4,341,770 |
| chr3 |
176 |
34,210,905 |
| Array 3 of 10 |
|
05542685001 |
Rat ChIP 2.1M WG-T 3-10 Arr Del |
|
05544734001 |
Rat ChIP 2.1M WG-T 3-10 Arr Ser |
|
chr3 |
34,211,015 |
171,063,124 |
| chr3 random |
5,681 |
1,719,288 |
| chr4 |
171 |
133,406,152 |
| Array 4 of 10 |
|
05542693001 |
Rat ChIP 2.1M WG-T 4-10 Arr Del |
|
05544742001 |
Rat ChIP 2.1M WG-T 4-10 Arr Ser |
|
chr4 |
133,406,273 |
187,125,435 |
| chr4 random |
341 |
2,119,523 |
| Chr5 |
3,947 |
173,096,030 |
| Chr5 random |
31 |
2,145,029 |
| Chr6 |
5,546 |
46,956,292 |
| Array 5 of 10 |
|
05542707001 |
Rat ChIP 2.1M WG-T 5-10 Arr Del |
|
05544769001 |
Rat ChIP 2.1M WG-T 5-10 Arr Ser |
|
Chr6 |
46,956,377 |
147,630,631 |
| Chr6 random |
26 |
1,765,697 |
| Chr7 |
6,147 |
143,002,565 |
| Chr7 random |
136 |
1,172,477 |
| Chr8 |
126 |
29,013,150 |
| Array 6 of 10 |
|
05542715001 |
Rat ChIP 2.1M WG-T 6-10 Arr Del |
|
05544777001 |
Rat ChIP 2.1M WG-T 6-10 Arr Ser |
|
Chr8 |
29,013,250 |
129,041,601 |
| Chr8 random |
62 |
886,901 |
| Chr9 |
3,248 |
113,440,328 |
| Chr9 random |
194 |
1,163,513 |
| chr10 |
1 |
48,202,137 |
| Array 7 of 10 |
|
05542723001 |
Rat ChIP 2.1M WG-T 7-10 Arr Del |
|
05544785001 |
Rat ChIP 2.1M WG-T 7-10 Arr Ser |
|
chr10 |
48,202,262 |
110,718,677 |
| chr10 random |
2 |
869,685 |
| chr11 |
5,218 |
87,757,579 |
| chr11 random |
26 |
1,273,526 |
| chr12 |
531 |
46,782,101 |
| chr12 random |
7,324 |
947,965 |
| chr13 |
16 |
74,362,754 |
| Array 8 of 10 |
|
05542731001 |
Rat ChIP 2.1M WG-T 8-10 Arr Del |
|
05544793001 |
Rat ChIP 2.1M WG-T 8-10 Arr Ser |
|
chr13 |
74,362,854 |
111,151,339 |
| chr13 random |
26 |
608,244 |
| chr14 |
1,887 |
112,193,818 |
| chr14 random |
12 |
1,824,996 |
| chr15 |
666 |
109,757,551 |
| chr15 random |
2 |
1,605,910 |
| chr16 |
6 |
14,661,336 |
| Array 9 of 10 |
|
05542740001 |
Rat ChIP 2.1M WG-T 9-10 Arr Del |
|
05544807001 |
Rat ChIP 2.1M WG-T 9-10 Arr Ser |
|
chr16 |
14,661,446 |
90,232,174 |
| chr16 random |
1 |
1,399,983 |
| chr17 |
7 |
97,295,476 |
| chr17 random |
3,168 |
613,253 |
| chr18 |
873 |
87,261,725 |
| chr18 random |
46 |
594,412 |
| chr19 |
21 |
15,708,114 |
| Array 10 of 10 |
|
05542758001 |
Rat ChIP 2.1M WG-T 10-10 Arr Del |
|
05544815001 |
Rat ChIP 2.1M WG-T 10-10 Arr Ser |
|
chr19 |
15,708,224 |
59,213,078 |
| chr19 random |
5,698 |
977,657 |
| Chr20 |
26 |
55,263,625 |
| Chr20 random |
1 |
592,543 |
| chrM |
26 |
16,211 |
| chrX |
12 |
160,699,113 |
| chrX random |
266 |
1,977,439 |
|
Literature
For a complete listing of literature covering all Roche NimbleGen products and services please visit our literature page.
FAQ
| Hide All Topics Show All Topics |
| Experimental Design |
| Do you have a recommended protocol for front-end sample processing for producing ChIP DNA? |
Yes, the protocol that we currently provide is based off a protocol from the Ren laboratory at UCSD. Customers are free to use whichever protocol they would like, but we highly recommend this protocol due to its reproducibility with ChIP-chip arrays. Contact Roche NimbleGen Technical Support if you would like the link to this protocol. |
| Does Roche NimbleGen perform front-end sample processing (e.g. IP and amplification)? |
No, Roche NimbleGen is not currently set up to perform contract ChIP-chip experiments for our customers. |
| What is the minimum number of cells required to perform a Roche NimbleGen recommended ChIP-chip experiment? |
The minimum number of cells used for a successful ChIP-chip experiment is approximately 1 x 107. However, ChIP reactions in which abundant molecules are being immunoprecipitated (e.g. histones and RNA polymerase II) require a lesser number of cells for a successful experiment. Remember, if you are starting with less material you would need to adjust all of the volumes and concentrations in each protocol. |
| What is a suitable negative control for ChIP-chip experiments? |
Many of our customers do not use a negative control (e.g. nonspecific IgG antibody), but it is recommended if this is your first experiment with NimbleGen ChIP-chip. You will want to have your negative control (IgG) co-hybridized with total DNA (input) in order to avoid a high signal/noise ratio that is often seen when the IgG sample is co-hybridized with the immunoprecipitated sample. |
| What other types of controls are used in a ChIP-chip experiment? |
The most common experimental control used when performing ChIP is an isotope control, such as nonspecific IgG or antibodies against GST or GFP. A potential pitfall in using these controls is that since the antibodies do not immunoprecipitate the nonspecific DNA yield is often very low. The resulting hybridization also tends to be much noisier and can result in many false positives due to amplification of trace amount of nonspecific DNA. Another, yet rare, control that is sometimes performed is a ChIP using uncrosslinked chromatin. However, many researchers prefer to perform ChIP with an antibody against the protein of interest in a cell line where the protein has been depleted (by target genetic deletion or siRNA). Alternatively, a cell line that does not express the protein of interest could be used as a negative control. |
| Is it possible to use more than one antibody when performing ChIP or do you recommend using only one antibody per procedure? |
Yes, many researchers combine multiple antibodies in their ChIP reaction to screen for antibodies that work well. If positive results are observed from this combined antibody approach, one can go back and perform ChIP using individual antibodies against the protein of interest. You can also perform experiments to examine the binding sites for a multi-protein complex by using a pooled sample of antibodies against all subunits of the complex. |
| What types of beads should I use to capture the immunoprecipitated DNA? |
There are two competing platforms for ChIP based on what type of beads one uses for immunoprecipitation: agarose protein-A/protein-G beads or magnetic latex immunoglobulin beads. Due to the inherent porosity of the agarose beads, a significant amount of nonspecific DNA binding is observed and consequently a much higher ChIP DNA yield than with magnetic latex beads. Some researchers have claimed that they observe good ChIP results without amplification using agarose beads. However, we recommend magnetic beads because we believe these beads will give much cleaner results. |
| What should I use as a reference sample? |
The majority of our customers use total (input) sample as a reference. Using a nonspecific IgG sample is not a suitable reference. |
|
| Array Design |
| Are the probes designed from both strands? |
No, Roche NimbleGen only designs probes based off of the forward strand. |
| How does Roche NimbleGen address repetitive elements in the genome for ChIP-chip designs? |
When available, we utilize conventional repeat masking, as done by the RepeatMasker program http://www.repeatmasker.org/). However, NimbleGen has no access to the repeat libraries necessary to perform this application, so we rely on third parties to supply this type of masked sequence. However, we find that RepeatMasker is often overly aggressive and can mask 50%-55% of human DNA sequence. We have developed our own method of repeat masking which is dependent on the mean frequency of the 15mers which make up each 50mer oligo. A table is made of the count of all 15mers that appear in the genome, from both strands. Then a 15mer window is slid along each oligo, looking up the count of each 15mer in the table, and calculating the average count. A threshold is set, usually 100 for large eukaryotic genomes, and any probe that exceeds that threshold is eliminated from further consideration. Depending on the region of the genome being evaluated, approximately 20-25% of the DNA is excluded. A similar technique is used by other groups. See the following paper for reference: Bioinformatics. 2006 Jan 15;22(2):134-41. WindowMasker: window-based masker for sequenced genomes; Morgulis A, Gertz EM, Schaffer AA, Agarwala R; National Center for Biotechnology Information, National Institutes of Health, Department of Health and Human Services Building 38A, Room 1003N, 8600 Rockville Pike, Bethesda, MD 20894, USA. |
| Why do you use 100bp spacing in ChIP-chip designs? |
Our research/development staff has performed experiments in which human probe spacing is varied; they observe a much poorer signal/noise ratio as well as a dramatic increase in the number of false positives when spacing is greater than 100-120 bp. |
| What spacing do you recommend for ChIP-chip designs? |
We recommend probe spacing of 100bp or less. |
| What are the human ENCODE biologically significant regions that are tiled on 4x72K and human 2.1M Deluxe Promoter arrays? |
These regions were picked manually by the ENCODE consortium and are deemed biologically significant in terms of transcriptional regulation. The regions include the HOXA cluster (chromosome 7), ß-globin cluster (chromosome 11), and others. |
|
| Sample Processing |
| How much IP sample should be expected prior to the amplification step? |
The amount of IP sample obtained truly depends on the antibody quality and amount of starting material. A standard ChIP reaction yields DNA fragments in the range of ~100ng. |
| Do I need to amplify my ChIP samples? |
Whole genome amplification (WGA) or ligation mediated-PCR (LM-PCR) needs to performed when there is less than 4µg ChIP DNA. NimbleGen recommends the GenomePlex WGA Kits from Sigma-Aldrich. |
| What method should I use to amplify my ChIP samples? |
Past and present NimbleGen ChIP-chip customers have had very good experiences working with the Sigma-Aldrich GenomePlex WGA2 Kit (#WGA2-50RXN). The WGA method seems to be easier and the quality of the amplified DNA is quite good. Many customers find that it is difficult to get LM-PCR to work well. For those that are just beginning ChIP-chip studies, we recommend the use of WGA. |
| What yield should I expect from LM-PCR? |
A standard ChIP reaction yields less than 100ng DNA. After one round of LM-PCR amplification that yield can increase to a few micrograms. If more DNA is needed, a second round of amplification can be performed. |
| Does LM-PCR cause bias in the sample? |
We have found that there tends to be more bias with samples amplified by LM-PCR compared to WGA. We recommend running the amplified DNA on an agarose gel to check for the presence of multiple DNA bands, which indicate poor sample quality. Generally, DNA amplified by WGA gives a smear rather than multiple bands. |
| Can I use T7 amplification for generating ChIP DNA? |
Yes, but we have found that customers need to adequately remove all RNA and protein from the sample. T7 amplified samples frequently arrive at Roche NimbleGen with RNA and protein contamination, which results in low labeling yields and subsequently less than adequate amounts of labeled sample to perform an array hybridization. |
| Why does NimbleGen use 7mer primers for short fragment labeling? |
We use 7mer primers because they seem to be much more efficient at labeling short DNA fragments (<200 bp). We consistently have trouble obtaining adequate yields for short DNA fragments when using 9mer primers. |
| What is the difference between 7mer and 9mer labeling for the same samples if it is above 200bp? |
We are still unclear on the differences between 7mer and 9mer sample labeling for larger DNA fragments. We are in the process of running experiments to test for any differences. Preliminary data has shown that the differences are quite small. |
| Will all ChIP DNA eventually be labeled using 7mer primers? |
Labeling ChIP DNA using exclusively 7mer random primers is a possibility. We are in the process of validating 7mer labeling for ChIP-chip. As of now, all ChIP samples should still be labeled using 9mers. |
| What is the expected yield from labeling reactions? |
One microgram each of IP and total sample are labeled with Cy5 or Cy3, respectively, using a 9mer primer. Our labeling procedure yields 28µg +/- 10µg for ChIP-chip samples. Samples <200bp yield an average of 10µg per labeling reaction. |
| Which dye should I use for my experimental and reference sample? |
We normally label the total sample (input) with Cy3 and the IP sample with Cy5. In the case that you would want to perform a dye swap experiment, you would label total sample (input) with Cy5 and the IP sample with Cy3. |
| Do I need to order another chip if I am going to perform dye swaps or co-hybridize DNA immunoprecipitated using a nonspecific antibody control? |
Yes, to perform a dye swap or co-hybridize DNA immunoprecipitated using a nonspecific antibody control you would need to order additional chips. |
|
| Sample Requirements for Service |
| What are the sample requirements for ChIP-chip? |
For a 385K or 4x72K ChIP-chip array, 3.5µg of ChIP DNA is required per array and for a 2.1M ChIP-chip array, 6.5µg of ChIP DNA is required. In both cases, the concentration of the ChIP DNA should be between 250-1000ng/µl, and the majority of the fragments should greater than 200bp long. The A260/A280 ratios should be at least 1.7 and the A260/A230 ratios should be at least 1.6. |
| What is the normal ChIP-chip fragment size? What if my fragments are smaller? |
DNA shearing from a typical ChIP experiment yields 200-1000bp fragments. If the majority of DNA fragments are below 200bp, Roche NimbleGen will have difficulty obtaining adequate labeling yields and hence may not have sufficient material to hybridize to the arrays. However, if your fragments are between 100-1000bp, with the majority of fragments >200bp, than your samples should work well. |
| How much ChIP DNA do I need to supply if I order the 10 array set? |
The 10 array set requires approximately 65µg DNA. For a more economical choice for analyzing whole genome protein/DNA interactions, you can use the 4 array set (human and mouse), which requires at least 26µg DNA. |
| What if my sample concentration or yield is less then required? |
If your sample does not meet our QC requirements you will be contacted by Roche NimbleGen for replacement samples. If you are unable to supply replacements you still have the option of proceeding with the experiment; however, there may be extra charges attached to this sample and the success of your experiment will not be guaranteed. |
|
| Data Analysis |
| Does Roche NimbleGen normalize ChIP-chip data? |
No, there is no normalization of ChIP-chip data. However, we do scale the GFF files by subtracting the bi-weight mean for the log-ratio values from each log-ratio value. |
| Does Roche NimbleGen scale ChIP-chip data? |
Yes, Roche NimbleGen scales the ratios in the .gff files by subtracting the bi-weight mean for the log-ratio values from each log-ratio value. If you would like more information about how to calculate a Tukey bi-weight mean scaling please go to Bi-Weight Scale. |
| Why does Roche NimbleGen use Tukey's bi-weight function for scaling ChIP-chip data? |
RMA looks at all the arrays in a set and normalizes the data for all the arrays. ChIP-chip is a two color array with the reference on the array and is therefore thought of as a stand alone experiment. RMA is not an appropriate analysis for ChIP chip data. The Tukey bi-weight function is used to account for differences in the dyes on the array, whereas RMA is used to account for differences between arrays so that the arrays can be compared. |
| Are there any statistical tests that are applied to my ChIP-chip data? |
Yes, we determine the false discovery rate (FDR) for each peak identified from the scaled log2-ratio data. First the scaled log2-ratio data is analyzed to identify peaks above a specified cutoff value. Assume we find 20 peaks that are above this cutoff value; the scaled log2-ratio data is then randomized 20 different times and after each permutation it is determined how many peaks are still above the cutoff value. So, if after randomizing the data 20 times we find that there are 2 peaks above the cutoff value, the FDR is 10% (which is a reasonably good FDR value). FDR values can differ depending on the peak height and number of probes comprising the peak. On SignalMap, the peaks will be color coded corresponding to FDR value for quick identification of statistically significant peaks. |
| Other than the pair files, does Roche NimbleGen supply any other raw data files? |
No, pair files are the only raw data files that Roche NimbleGen includes in delivered ChIP-chip data. |
| What are the background measurements for a ChIP-chip array? |
For any ChIP-chip array, the signal is a mix of non-specific signal, “background”, and specific signal. The information given from random probes do not represent true background measurements, but rather non-specific binding events. Currently, we do not calculate background. |
| Is there another way to analyze ChIP-chip data? |
Yes, the following five sites have been developed to analyze ChIP-chip data.
1. M-peak: Nature. 2005 436(7052):876-80
2.TAMALPAIS Server: 2006 Genome Research 16:595.
3.ACME (in R language): Methods Enzymol. 2006;411:270-82.
4.ChIPOTle: Genome Biology 2005, 6:R97. For the Perl version, go to ChIPOTle Peak Finder
5.Model-based Analysis of 2-Color Arrays MA2C. |
|
| Service Deliverables |
| Can I get the images for my data? |
Yes, we can supply the raw data array images (.tif) upon request. Scaled log2-ratio data (.gff) files and peak (.gff) files are included in your deliverable data. |
| Can I get a graphical representation of all probes for a catalog design so I can see what regions of the genome have coverage? |
Yes, we can generate this information in GFF for all of catalog designs. You will need a copy of Roche NimbleGen's SignalMap software to view the GFF files. A free, 30-day demo version of SignalMap is available for download. |
| Does Roche NimbleGen generate reports listing the most significant binding/modification events for my ChIP-chip experiment? |
Yes, we are including two promoter reports that map the peaks from your ChIP-chip data relative to the transcription start site of a gene. For instance, if a peak is called within the promoter region of a gene, the report lists the approximate location of the peak as a negative position (upstream of the start site) or positive position (downstream). Also listed are accession number of the gene, gene ID, chromosome position, among others. These reports narrow the genomic regions to look at when moving forward to validate your ChIP-chip data (e.g. gel mobility shift assay). |
|
| Capabilities |
| What if a customer has non-nucleosome samples that have an average length of <200bp? |
If non-nucleosome samples are <200bp, we utilize 7mer random primers to label DNA. However, at this time such samples will be marked as “at risk” because there has not been sufficient validation using 7mers for non-nucleosome samples. |
| Can I use NimbleGen ChIP-chip to map DNase I hypersensitive sites? |
Yes, NimbleGen ChIP-chip arrays can be used to map DNase I hypersensitive sites. A manuscript by Crawford et al. (2006. Nature Methods. 3:503-509) describes the use of NimbleGen tiled arrays to map hypersensitive sites. Other manuscripts mapping hypersensitive sites using NimbleGen arrays include Sabo et al. (2006. Nature Methods. 3:511-518) and Follows et al. (2006. Genome Research. 16:1310-1319). |
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