Measuring Protein-DNA interactions using Chromatin Immunoprecipitation and NGS (ChIP-Seq) How is Biological Complexity Achieved?
Mediated by Transcription Factors (TFs)
2 Introduction to Systems Biology Regulation of Gene Expression by Transcription Factors
TF TF activation trans-acting TF factorsTF TF TF TF
1 2 3 cis-regulatory elements Gene repression
3 Introduction to Systems Biology The big point is:
“…how these TFs orchestrate the expression of thousands of genes in a genome to create such a spectrum of biological diversity remains a mystery…”
Several methods have been developed in the last several years to study TF-DNA interactions and to understand the function of TFs.
4 Introduction to Systems Biology HTP Methods for studying TF-DNA interactions
• Systematic Evolution of Ligands by Exponential Enrichment - SELEX (obsolete) • Yeast-1-Hybrid (Y1H) • Bacterial-1-Hybrid (B1H) • Protein Binding Microarrays • Chromatin Immunoprecipitation followed by chip (ChIP-Chip) or followed by Sequencing (ChIP-Seq)
5 Introduction to Systems Biology How does ChIP-Seq work?
TF is specifically bound to its cognate DNA sequence
6 Introduction to Systems Biology At any given time, my TF is bound in many locations
7 Introduction to Systems Biology Chromatin IP
8 Introduction to Systems Biology Chromatin IP
300 bp
9 Introduction to Systems Biology Next Generation Sequencing
Let’s see this in action
10 Introduction to Systems Biology Sequencing and mapping of DNA fragments
300bp 5’ 3’
37 bp
So, what’s up with the remaining 263 bases?
11 Introduction to Systems Biology Sequencing and mapping of DNA fragments
Chromosome
300bp But we only have 37bp of each 300 bp!!!
12 Introduction to Systems Biology ChIP-Seq
13 Introduction to Systems Biology ChIP-Seq allows for the analysis of more events
• Since we do not use a pre-defined set of sequences to design and investigate a microarray, we can use ChIP-Seq to study many different types of protein-DNA interactions.
• Additionally, modifications of ChIP have been designed to study DNA modifications such as DNA methylation, using met-DNA binding proteins. These methods are known as: – MRE-Seq (Methylation-sensitive Restriction Enzyme) – MeDIP-Seq (Methylated DNA Immuno Precipitation)
14 Introduction to Systems Biology Using ChIP-Seq to understand transcriptional processes
15 Introduction to Systems Biology Pol II => transcriptional events
16 Introduction to Systems Biology Pol II pausing and release factors => dynamics
17 Introduction to Systems Biology Super elongation complex => dynamics
18 Introduction to Systems Biology Histone modifications => Epigenetics
19 Introduction to Systems Biology Histone modifications => Epigenetics
Type of Histone modification H3K4 H3K9 H3K14 H3K27 H3K79 H4K20 H2BK5 mono- activation activation activation activation activation activation methylation di-methylation repression repression activation activation, tri-methylation activation repression repression repression repression acetylation activation activation H3K4me3 is found in actively transcribed promoters, particularly just after the transcription start site. H3K9me3 is found in constitutively repressed genes. H3K27me is found in facultatively repressed genes. H3K36me3 is found in actively transcribed gene bodies. H3K9ac is found in actively transcribed promoters. H3K14ac is found in actively transcribed promoters.
20 Introduction to Systems Biology Mediator and Cohesin complexes => Chromatin structure
21 Introduction to Systems Biology Polycomb and Trithorax => repressed/active promoters
22 Introduction to Systems Biology All of these can be brought together to give us a complete picture
Scale 2 kb syboss_mm9 chr17: 35,641,500 35,642,000 35,642,500 35,643,000 35,643,500 35,644,000 35,644,500 35,645,000 35,645,500 35,646,000 35,646,500 35,647,000 35,647,500 35,648,000 35,648,500 35,649,000 35,649,500 User Supplied Track User Track TopHat junctions junctions RefSeq Genes Pou5f1 Tcf19 Pou5f1 Tcf19 Tcf19 Repeating Elements by RepeatMasker RepeatMasker Mouse mRNAs from GenBank AB375278 BC004617 M34381 AK004231 AK145321 AK088479 BC068268 AK077414 AB221654 AK078697 X52437 AK207827 AB375276 AB375269 AB375270 AB375272 AB375271 AB375273 AB375277 HM346525 HM346526 AB375274 AB375275 197 _ RNA-seq Xiao2012
d0_RNA_seq_1_Xiao2012
1 _ 112 _ Pol_II
Pol_II_0hr_Lin2011
1 _ 93 _ Poll II Pause
Spt5_Rahl2010
1 _ 145 _ Poll II Pause
NelfA_Rahl2010
1 _ 44 _ Poll II Pause
Ctr9_Rahl2010
1 _ 73 _ Super Elongation Complex
AFF4_0hr_Lin2011
1 _ 23 _ Super Elongation Complex
Cdk9_0hr_Lin2011
1 _ 77 _ Super Elongation Complex
ELL2_0hr_Lin2011
1 _ 144 _ Transcription Factors
TAF1_Xiao2012
1 _ 104 _ Mediator
Med1_Kagey2010
1 _ 161 _ Mediator
Med12_Kagey2010
1 _ 142 _ Cohesin
Nipbl_Kagey2010
1 _ 145 _ Cohesin
Smc1_Kagey2010
1 _ 154 _ Cohesin
Smc3_Kagey2010
1 _ 201 _ Cohesin
Rad21_Nitzsche2011
1 _ 50 _ Transcription Factors
Oct4
1 _ 69 _ Transcription Factors
Nanog_Chen2008
1 _ 68 _ Transcription Factors
Sox2_Chen2008
1 _ 57 _ Transcription Factors
Essrb_Chen2008
1 _ 144 _ Transcription Factors
Tcfcp2l1_Chen2008
1 _ 63 _ Transcription Factors
Klf4_Chen2008
1 _ 37 _ Transcription Factors
CTCF_Chen2008
1 _ 39 _ Transcription Factors
cMyc_Chen2008
1 _ 131 _ Transcription Factors
E2f1_Chen2008
1 _ 297 _ Transcription Factors
iFlag_Hoxc9_d5_Mazzoni2011
1 _ 296 _ Transcription Factors
Rfx1_np_Creyghton2010
1 _ 155 _ Transcription Factors
REST_Rahl2011
1 _ 180 _ Transcription Factors
CTCF_Handoko2011
1 _ 59 _ Transcription Factors
Cdx2_Nishiyama2009
1 _ 183 _ Transcription Factors
Olig2_d4_Mazzoni2011
1 _ 69 _ Transcription Factors
Zfx1_Chen2008
1 _ 45 _ Transcription Factors
n_Myc_Chen2008
1 _ 59 _ Transcription Factors
iHoxc9_V5_Mazzoni2011
1 _ 58 _ Transcription Factors
Stat3_Chen2008
1 _ 72 _ TF Co-Factors
MCAF1_Rahl2011
1 _ 75 _ Chromation Organisation and Remodeling
CHD7_Schnetz2010
1 _ 239 _ Chromation Organisation and Remodeling
Lamin_B_Handoko2011
1 _ 75 _ Histone Modifiers
p300_Schnetz2010
1 _ 70 _ Histone Modifiers
Eset_Yuan2009
1 _ 148 _ Chromatin State Xiao2012
d0_H3K27ac_Xiao2012
1 _ 198 _ Chromatin State Xiao2012
d0_H3K27me3_Xiao2012
1 _ 136 _ Chromatin State Xiao2012
d0_H3K36me3_Xiao2012
1 _ 128 _ Chromatin State Xiao2012
d0_H3K4me1_Xiao2012
1 _ 128 _ Chromatin State Xiao2012
d0_H3K4me2_Xiao2012
1 _ 369 _ Chromatin State Xiao2012
d0_H3K4me3_Xiao2012
1 _ 206 _ Chromatin State Xiao2012
d0_H2AZ_Xiao2012
1 _ 255 _ Chromatin State Xiao2012
MeDIP_seq_Xiao2012
1 _ 12 _ Chromatin State Xiao2012
MRE_seq_Xiao2012
1 _ 212 _ Trithorax
Dpy_30_Jiang2011
1 _ 64 _ Trithorax
Rbpp5_Ang2011
1 _ 69 _ Trithorax
23 Wdr5_Ang2011
1 _ 59 _ Trithorax
WDR5_FL_Ang2011
1 _ 123 _ Polycomb Introduction to Systems Biology Ring1b_Rahl2011
1 _ 24 Introduction to Systems Biology 25 Introduction to Systems Biology Analysing ChIP-Seq data Analysis pipeline
27 Introduction to Systems Biology Analysis pipeline
28 Introduction to Systems Biology Analysis pipeline
sratoolkit
FastQC
fastx-toolkit
Commonalmostto all pipelinesNGS bowtie ChIP-Seq specific
29 Introduction to Systems Biology Analysis pipeline Automated Automated Human intervention intervention Human Requires
30 Introduction to Systems Biology Alignment to genome
• As for any other NGS application, we need to align our reads to a base genome.
• ChIP-Seq uses bowtie
• For ChIP-Seq we only want uniquely mapped sequences and allowing up to 2 mismatches – Usual parameters are –m 1 –n 2 –e 70 –l 28
Peak calling!
• Mostly used algorithm is MACS (Model-based Analysis of ChIP-Seq) – http://liulab.dfci.harvard.edu/MACS/index.html
Simple description:
Usually stringent threshold required for low false discovery rates (FDRs) due to the correction for multiple testing Peak calling!
– MACS allows for the calling of peaks using a background (control) track or against reactions performed in different conditions
Scale 100 kb chr6: 122600000 122650000 122700000 122750000 Consensus CDS CCDS Ensembl Genes Ensembl Genes RefSeq Genes Apobec1 Dppa3 Nanog Slc2a3 Apobec1 Nanogpd Gdf3 MicroRNAs from miRBase miRNA Gap Locations Gap Mouse mRNAs from GenBank Mouse mRNAs HEK4me3 - Marson2008 HEK4me3
c-Myc - Chen008 c-Myc
Klf4 - Chen008 Klf4
Sox2 - Chen2008 Sox2
STAT3 - Chen2008 STAT3
Zfx - Chen2008 Zfx
H3K36me3 - Marson2008 H3K36me3 WHAT DOES THIS ALL MEAN??? 217.431 _ H3K79me2 - Marson2008 H3K79me2 0 _ 227.795 _ Marson2008_macs14 Nanog_mES_rep2 0.0007 _ Tcf3 - Marson2008 Tcf3
Suz12 - Marson2008 Suz12
Suz12 - Chen2008 Suz12
CTCF - Chen2008 CTCF
E2f1 - Chen008 E2f1
GFP - Chen2008 GFP
Nanog - Chen008 Nanog
Oct4 - Chen2008 Oct4
p300 - Chen2008 p300
Smad - Chen2008 Smad
Tcfcp2I - Chen008 Tcfcp2I s_2_1_J1_ES_hMeDIP_1 Fics2011_MeDIP-Seq s_2_1_J1_ES_hMeDIP_2 Fics2011_MeDIP-Seq s_2_1_Np95___ES_hMeDIP_2 Fics2011_MeDIP-Seq s_3_1_Np95___ES_MeDIP_2 Fics2011_MeDIP-Seq s_2_2_J1_ES_hMeDIP_1 Fics2011_MeDIP-Seq s_2_2_J1_ES_hMeDIP_2 Fics2011_MeDIP-Seq s_2_2_Np95___ES_hMeDIP_2 Fics2011_MeDIP-Seq s_2_ES_J1_mRNA_Seq Fics2011_MeDIP-Seq s_3_1_J1_ES_MeDIP_2 Fics2011_MeDIP-Seq s_3_2_J1_ES_MeDIP_2 Fics2011_MeDIP-Seq s_3_1_pMEF_MeDIP Fics2011_MeDIP-Seq s_3_1_Tet1_Tet2_KD_ES_hMeDIP_1 Fics2011_MeDIP-Seq s_3_2_Np95___ES_MeDIP_2 Fics2011_MeDIP-Seq s_3_2_pMEF_MeDIP Fics2011_MeDIP-Seq s_3_2_Tet1_Tet2_KD_ES_hMeDIP_1 Fics2011_MeDIP-Seq s_4_1_E14_ES_hMeDIP_2 Fics2011_MeDIP-Seq s_4_1_Tet1_Tet2_KD_ES_hMeDIP_2 Fics2011_MeDIP-Seq s_4_2_E14_ES_hMeDIP_1 Fics2011_MeDIP-Seq s_4_2_E14_ES_hMeDIP_2 Fics2011_MeDIP-Seq s_4_2_Tet1_Tet2_KD_ES_hMeDIP_2 Fics2011_MeDIP-Seq s_4_Mock_KD_ES_J1_2_mRNA_Seq Fics2011_MeDIP-Seq s_3_Mock_KD_ES_J1_1_mRNA_Seq Fics2011_MeDIP-Seq s_6_Mock_KD_ES_J1_3_mRNA_Seq Fics2011_MeDIP-Seq s_4_Tet1_2_KD_ES_J1_1_mRNA_Seq Fics2011_MeDIP-Seq s_5_Tet_1_2_KD_ES_J1_2_mRNA_Seq Fics2011_MeDIP-Seq s_7_Tet_1_2_KD_ES_J1_3_mRNA_Seq Fics2011_MeDIP-Seq s_7_2_Tet1_KD_ES_MeDIP Fics2011_MeDIP-Seq s_5_1_E14_ES_MeDIP_1 Fics2011_MeDIP-Seq s_5_1_E14_ES_MeDIP_2 Fics2011_MeDIP-Seq s_5_1_Tet1_Tet2_KD_ES_MeDIP_2 Fics2011_MeDIP-Seq s_5_2_E14_ES_MeDIP_1 Fics2011_MeDIP-Seq s_5_2_E14_ES_MeDIP_2 Fics2011_MeDIP-Seq s_6_1_E14_EB_hMeDIP_2 Fics2011_MeDIP-Seq s_6_1_Tet1_KD_ES_hMeDIP Fics2011_MeDIP-Seq s_6_2_E14_EB_hMeDIP_1 Fics2011_MeDIP-Seq s_6_2_E14_EB_hMeDIP_2 Fics2011_MeDIP-Seq s_6_2_Tet1_KD_ES_hMeDIP Fics2011_MeDIP-Seq s_7_1_E14_EB_MeDIP_1 Fics2011_MeDIP-Seq s_7_1_E14_EB_MeDIP_2 Fics2011_MeDIP-Seq s_7_1_J1_ES_MeDIP_1 Fics2011_MeDIP-Seq s_7_1_Np95___ES_MeDIP_1 Fics2011_MeDIP-Seq s_7_1_Tet1_KD_ES_MeDIP Fics2011_MeDIP-Seq s_7_1_Tet1_Tet2_KD_ES_MeDIP_1 Fics2011_MeDIP-Seq s_7_2_E14_EB_MeDIP_1 Fics2011_MeDIP-Seq s_7_2_E14_EB_MeDIP_2 Fics2011_MeDIP-Seq s_7_2_J1_ES_MeDIP_1 Fics2011_MeDIP-Seq s_7_2_Np95___ES_MeDIP_1 Fics2011_MeDIP-Seq s_7_2_Tet1_Tet2_KD_ES_MeDIP_1 Fics2011_MeDIP-Seq s_8_1_pMEF_hMeDIP Fics2011_MeDIP-Seq s_8_2_Np95___ES_hMeDIP_1 Fics2011_MeDIP-Seq s_8_2_pMEF_hMeDIP Fics2011_MeDIP-Seq Downstream analysis: the biology!
- Having called the peaks, we need to find out what they are and how biologically relevant they are.. - We need to find the DNA motifs bound by the TF - We need to call the peaks: what genes might be regulated by the TF? - We need to compare / integrate various datasets to get the full story
- Many tools have been created for this, but I like Homer (Heinz S, Benner C, Spann N, Bertolino E et Mol Cell 2010 May 28;38(4):576-589.) http://biowhat.ucsd.edu/homer/ngs/index.html
Downstream analysis of processed tracks
• Homer can take macs14 output files (bed files) and perform several statistics and analysis functions: – Motif finding – Gene Ontology enrichment analysis (GO, KEGG, Interpro, pfam, etc) – Peak association – Motif mapping on promoters – Statistics (heat-maps, histograms, etc)
36 Introduction to Systems Biology Motif finding
Oct-4 Sox-2 Nanog
Hoxc9 Essrb Rfx1
Olig2 Klf4
Stat3 Zfx cMyc
Tcfcp2l1 nMyc
37 CTCF REST Introduction to Systems Biology Peak identification: “closest annotation”
PeakID Chr Start End Peak Score Annotation Distance to TSS Nearest PromoterID Nearest Unigene Nearest Refseq Nearest Ensembl Gene Name MACS_peak_1 chr1 9738113 9738647 30.01 promoter-TSS (NM_173443) 83 NM_173443 Mm.440951 NM_173443 ENSMUSG00000045210 Vcpip1 MACS_peak_2 chr1 9934163 9934369 31.27 promoter-TSS (NR_024067) -67 NR_024067 Mm.28311 NR_024067 Snhg6 MACS_peak_3 chr1 10223144 10223424 31.27 promoter-TSS (NM_001102430) -533 NM_001102430 Mm.229141 NM_001102430 ENSMUSG00000067851 Arfgef1 MACS_peak_4 chr1 14299912 14300325 31.13 5' UTR (NM_010164, exon 1 of 18) 162 NM_010164 Mm.250185 NM_010164 ENSMUSG00000025932 Eya1 MACS_peak_5 chr1 15550790 15551134 31.27 intron (NM_001098528, intron 1 of 1) -244777 NM_009352 Mm.4306 NM_009352 ENSMUSG00000025925 Terf1 MACS_peak_6 chr1 16231057 16231519 30.64 intron (NM_025303, intron 12 of 13) 135325 NM_133832 Mm.274376 NM_133832 ENSMUSG00000025921 Rdh10 MACS_peak_7 chr1 16646897 16647322 31.01 promoter-TSS (NM_026456) -163 NM_026456 Mm.289248 NM_026456 ENSMUSG00000079658 Tceb1 MACS_peak_8 chr1 18048238 18048692 30.72 Intergenic 78667 NM_030033 Mm.319913 NM_030033 ENSMUSG00000025774 Crisp4 MACS_peak_9 chr1 28405286 28405514 31.27 Intergenic 431697 NM_001013750 Mm.214178 NM_001013750 ENSMUSG00000048411 Gm597 MACS_peak_10 chr1 33545031 33545274 31.27 intron (NM_008922, intron 10 of 13) 181451 NM_008922 Mm.27705 NM_008922 ENSMUSG00000026134 Prim2 MACS_peak_11 chr1 34542882 34543084 31.27 Intergenic -12292 NM_009355 Mm.10725 NM_009355 ENSMUSG00000026125 Prss39 MACS_peak_12 chr1 36568483 36568781 31.27 promoter-TSS (NM_001039551) -89 NM_001039551 Mm.256323 NM_001039551 ENSMUSG00000001138 Cnnm3 MACS_peak_13 chr1 36573340 36573846 30.25 intron (NM_053186, intron 1 of 7) 4872 NM_053186 Mm.256323 NM_001039551 ENSMUSG00000001138 Cnnm3 MACS_peak_14 chr1 37042748 37042953 31.27 Intergenic -46478 NM_018872 Mm.186943 NM_018872 ENSMUSG00000026116 Tmem131 MACS_peak_15 chr1 37509245 37509550 31.27 exon (NM_173870, exon 10 of 15) 22380 NM_026123 Mm.27404 NM_026123 ENSMUSG00000026111 Unc50 MACS_peak_16 chr1 39106807 39107265 30.68 Intergenic 62377 NM_026850 Mm.28121 NM_026850 ENSMUSG00000026078 Pdcl3 MACS_peak_17 chr1 42743330 42743817 30.41 intron (NR_027826, intron 1 of 3) 8097 NR_027826 Mm.45203 NR_027826 2610017I09Rik MACS_peak_18 chr1 44608959 44609420 30.65 intron (NM_028450, intron 1 of 10) 673 NM_028450 Mm.133132 NM_028450 ENSMUSG00000056870 Gulp1 MACS_peak_19 chr1 58024231 58024659 30.97 exon (NM_001159864, exon 2 of 7) 1923 NR_027630 Mm.339711 NM_001159864 ENSMUSG00000054770 Kctd18 MACS_peak_20 chr1 58307233 58307506 31.27 intron (NM_023631, intron 21 of 34) -27801 NM_001008419 Mm.414292 NM_001008419 ENSMUSG00000079554 Aox3l1 MACS_peak_21 chr1 58561375 58561704 31.27 intron (NM_008765, intron 1 of 17) 224 NM_008765 Mm.3411 NM_001025378 ENSMUSG00000026037 Orc2 MACS_peak_22 chr1 59052716 59053033 31.27 TTS (NM_172656) 22459 NM_172656 Mm.286006 NM_172656 ENSMUSG00000026027 Stradb MACS_peak_23 chr1 59452617 59452951 31.27 Intergenic -86207 NM_008057 Mm.297906 NM_008057 ENSMUSG00000041075 Fzd7 MACS_peak_24 chr1 59466694 59466915 31.27 Intergenic -72187 NM_008057 Mm.297906 NM_008057 ENSMUSG00000041075 Fzd7 MACS_peak_25 chr1 62813907 62814436 30.06 intron (NM_010939, intron 10 of 15) 64280 NM_001077405 Mm.266341 NM_001077403 ENSMUSG00000025969 Nrp2 MACS_peak_26 chr1 63223398 63223643 31.27 promoter-TSS (NM_001160038) 115 NM_018796 Mm.2718 NM_018796 ENSMUSG00000025967 Eef1b2 MACS_peak_27 chr1 66739647 66739891 31.27 intron (NM_175510, intron 62 of 64) -7698 NM_025683 Mm.240912 NM_025683 ENSMUSG00000026005 Rpe MACS_peak_28 chr1 68506260 68506647 31.27 intron (NM_010154, intron 3 of 26) 648180 NM_010154 Mm.442420 NM_010154 ENSMUSG00000062209 Erbb4 MACS_peak_29 chr1 72886622 72887150 30.06 intron (NM_008342, intron 1 of 3) 15809 NM_008342 Mm.141936 NM_008342 ENSMUSG00000039323 Igfbp2 MACS_peak_30 chr1 74634689 74635038 31.27 promoter-TSS (NM_029888) -72 NM_025784 Mm.358700 NM_025784 ENSMUSG00000026172 Bcs1l MACS_peak_31 chr1 75484212 75484606 31.27 intron (NM_178884, intron 16 of 19) 8302 NM_177056 Mm.44401 NM_177056 ENSMUSG00000051703 Tmem198 MACS_peak_32 chr1 77379445 77379687 31.27 exon (NM_007936, exon 13 of 18) 132097 NM_007936 Mm.400747 NM_007936 ENSMUSG00000026235 Epha4 MACS_peak_33 chr1 78414324 78414823 30.31 3' UTR (NM_001004173, exon 5 of 5) 70848 NM_011811 Mm.447792 NM_011811 ENSMUSG00000026245 Farsb MACS_peak_34 chr1 88154636 88155004 31.27 intron (NM_030184, intron 22 of 24) -44976 NM_145222 Mm.86467 NM_145222 ENSMUSG00000079445 B3gnt7 MACS_peak_35 chr1 88423602 88423928 31.27 intron (NM_008972, intron 1 of 4) 454 NM_008972 Mm.19187 NM_008972 ENSMUSG00000026238 Ptma MACS_peak_36 chr1 89053527 89053877 31.27 Intergenic -2100 NM_021306 Mm.140765 NM_021306 ENSMUSG00000026247 Ecel1 MACS_peak_37 chr1 90137423 90137857 30.91 intron (NM_001177364, intron 18 of 42) 14045 NM_001177364 Mm.386915 NM_001177364 ENSMUSG00000079429 Heatr7b1 MACS_peak_38 chr1 90351357 90351698 31.27 Intergenic 47933 NM_029269 Mm.173058 NM_029269 ENSMUSG00000026295 Spp2 MACS_peak_39 chr1 91649825 91650081 31.27 intron (NM_001037136, intron 11 of 16) -69486 NR_015540 Mm.444291 NR_015540 4933400F21Rik MACS_peak_40 chr1 91828061 91828504 30.83 promoter-TSS (NM_010262) -531 NM_010262 Mm.204730 NM_010262 ENSMUSG00000034486 Gbx2 MACS_peak_41 chr1 92110841 92111293 30.74 exon (NM_007722, exon 2 of 2) 10489 NM_007722 Mm.6522 NM_007722 ENSMUSG00000044337 Cxcr7 MACS_peak_42 chr1 92860478 92860815 31.27 intron (NM_008998, intron 2 of 5) 3598 NM_001159725 Mm.279780 NM_001159725 ENSMUSG00000026304 Rab17 MACS_peak_43 chr1 93308179 93308547 31.27 3' UTR (NM_019479, exon 4 of 4) 1436 NM_019479 Mm.280029 NM_019479 ENSMUSG00000067071 Hes6 MACS_peak_44 chr1 93797586 93798066 30.48 Intergenic 99772 NM_007855 Mm.9474 NM_007855 ENSMUSG00000007805 Twist2 MACS_peak_45 chr1 94077009 94077215 31.27 Intergenic -32142 NM_207225 Mm.318567 NM_207225 ENSMUSG00000026313 Hdac4 MACS_peak_46 chr1 94108420 94108757 31.27 Intergenic -63618 NM_207225 Mm.318567 NM_207225 ENSMUSG00000026313 Hdac4 MACS_peak_47 chr1 94633707 94634167 30.66 Intergenic -88519 NM_153114 Mm.219545 NM_153114 ENSMUSG00000044055 Otos MACS_peak_48 chr1 94651419 94651841 31.04 Intergenic -76633 NM_016696 Mm.297976 NM_016696 ENSMUSG00000034220 Gpc1 MACS_peak_49 chr1 95327476 95327874 31.27 exon (NM_133808, exon 7 of 28) 47710 NM_133808 Mm.30012 NM_133808 ENSMUSG00000034088 Hdlbp MACS_peak_50 chr1 95616670 95616902 31.27 intron (NM_025920, intron 3 of 5) 34515 NM_016778 Mm.3295 NM_016778 ENSMUSG00000026278 Bok
38 Introduction to Systems Biology Heatmaps: visualizing distributions of peaks
TF2
-3000bp Peak 1 +3000bp TF2
-3000bp Peak 2 +3000bp
-3000bp Peak n-1 +3000bp TF2
-3000bp Peak n +3000bp
39 Introduction to Systems Biology Heatmaps: visualizing distributions of peaks
Nanog vs Sox2 Nanog vs Oct4
6000bp 6000bp 40 Introduction to Systems Biology Heatmaps: visualizing distributions of peaks
cMyc vs Oct4 cMyc vs Sox2
41 Introduction to Systems Biology Histograms of “tag directories”
Nanog vs Sox2 Nanog vs Oct4
42 Introduction to Systems Biology Histograms of “tag directories”
cMyc vs Oct4 cMyc vs Sox2
43 Introduction to Systems Biology