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Chip-Seq) How Is Biological Complexity Achieved?

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Chip-Seq) How Is Biological Complexity Achieved? 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 Commonto almostall NGS pipelines bowtie ChIP-Seq specific 29 Introduction to Systems Biology Analysis pipeline Automated Humanintervention 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
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