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Regulomics I: Methods to read out regulatory functions.

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Presentation on theme: "Regulomics I: Methods to read out regulatory functions."— Presentation transcript:

1 Regulomics I: Methods to read out regulatory functions

2 Noonan and McCallion, Ann Rev Genomics Hum Genet 11:1 (2010) Identifying regulatory functions in genomes

3 forebrain gene A Brain TFs neural tube gene A Neural TFs limb Limb TFs gene A Expression of gene A Genes are not just protein coding sequences gene A

4 Lettice et al. Hum Mol Genet 12:1725 (2003) Sagai et al. Development 132:797 (2005) Regulatory mutations can cause profound phenotypes

5 Three essential questions Q1: Where are regulatory elements located in the genome? Q2: What regulatory functions do they encode? Q3: What genes do they control? We will use promoters and enhancers as our examples, but there are other regulatory functions

6 Q1: Mapping regulatory elements in genomes Chr5: 133,876,119 – 134,876,119 Genes Transcription Regulatory elements are not easily detected by sequence analysis Examine biochemical correlates of RE activity in cells/tissues: Chromatin Immunoprecipitation (ChIP-seq) DNase-seq and FAIRE Methylated DNA immunoprecipitation (MeDIP)

7 1. TF binding Biochemical indicators of regulatory function 2. Histone modification H3K27ac H3K4me3 3. Chromatin modifiers & coactivators p300MLL 4. DNA looping factors cohesin

8 Methods ChIP-seq Chromatin accessibility TFsHistone modsDNaseFAIRE From Furey (2012) Nat Rev Genet 13:840

9 Method I: ChIP-seq ChIP Input Peak call Signal Align reads to reference Use peaks of mapped reads to identify binding events PCR

10 ChIP-seq is an enrichment method Requires a statistical framework for determining the significance of enrichment ChIP-seq ‘peaks’ are regions of enriched read density relative to an input control Input = sonicated chromatin collected prior to immunoprecipitation ChIP Input Peak call Enrichment relative to control Calling peaks in ChIP-seq data

11 Wilbanks and Facciotti PLoS ONE 5:e11471 (2010) There are many ChIP-seq peak callers available

12 From Park (2009) Nat Rev Genet 10:669 Generating ChIP-seq peak profiles Artifacts: Repeats PCR duplicates

13 Assessing statistical significance # of reads at a site (S) Empirical FDR: Call peaks in input (using ChIP as control) FDR = ratio of # of peaks of given enrichment value called in input vs ChIP Assume read distribution follows a Poisson distribution Many sites in input data will have some reads by chance Some sites will have many reads From Pepke et al (2009) Nat Meth 6:S22

14 Assessing statistical significance # of reads at a site (S) From Park (2009) Nat Rev Genet 10:669 Sequencing depth matters:

15 ChIP-seq signal profiles vary depending on factor Transcription factors Pol II Histone mods From Park (2009) Nat Rev Genet 10:669

16 DNase IFAIRE Mapping chromatin accessibility From Furey (2012) Nat Rev Genet 13:840

17 Song et al., Genome Res 21:1757 (2011) DNase I hypersensitivity identifies regulatory elements… DNase I hypersensitive sites

18 …but needs to be combined with other data to determine what is actually bound – such as TF ChIP… DHS signal in GM12878 RNA PolII ChIP in GM12878

19 DHS sites in human ES cells: From Neph (2012) Nature 489:83 … or motif analysis

20 Q2: Making sense of regulatory functions Integrate multiple data sources TF function Histone modification Potential target genes Existing genome annotations Compare multiple biological states

21 Regulatory function is dependent on biological context forebrain gene A Brain TFs neural tube gene A Neural TFs limb Limb TFs gene A

22 Identifying tissue-specific regulatory function ChIP-seq signal Signal at 20,000 bound sites Limb Brain Sites strongly marked in Limb Sites strongly marked in Brain Clustering Sites strongly marked in both

23 LimbBrain Function? Assign enhancers to genes based on proximity (not ideal) GREAT: bejerano.stanford.edu/great/ Gene ontology annotation assigned to regulatory sequences Identifying tissue-specific regulatory function

24 Q2: Making sense of regulatory functions Integrate multiple data sources TF function Histone modification Potential target genes Existing genome annotations Compare multiple biological states

25 Example from PS1: CTCF and RAD21 (cohesin)

26 CTCF and cohesin co-occupy many sites Promoters Insulators Enhancers From Kagey et al (2010) Nature 467:430

27 CTCF: marks insulators and promoters RAD21 (cohesin): marks insulators, promoters and enhancers? Include histone modification data (Wednesday’s lecture) Promoter Enhancers?

28 Identifying bound motifs from ChIP-seq data CTCF ~20,000 binding sites identified by ChIP: From Furey (2012) Nat Rev Genet 13:840 MEME suite:

29 Enhancer-associated histone modification Caveat: Single TF binding events often do not indicate regulatory function Many TFs are present at high concentrations in the nucleus TF motifs are abundant in the genome Single TF binding events may be incidental

30 Q3: Identifying the target genes for regulatory elements forebrain gene A Brain TFs neural tube gene A Neural TFs limb Limb TFs gene A

31 Sequence: Hi-C ChIP for specific factors: ChIA-PET Sequence: 4C Chromosome Conformation Capture Sequence: 5C

32 3C evaluates specific interaction possibilities by qPCR Dekker et al Nat Rev Genet 14:390 (2013)

33 4C identifies genome-wide interactions for a single “bait” sequence

34 From Kieffer-Kwon et al. (2013) Cell 155:1507 ChIA-PET identifies interactions involving a particular factor

35 In principle, Hi-C captures all interactions, but is limited by sequencing depth Dekker et al Nat Rev Genet 14:390 (2013)

36 Hierarchical organization of the genome Dekker et al Nat Rev Genet 14:390 (2013) Gorkin et al Cell Stem Cell 14:762 (2014) Cohesin-mediated interactions

37 Summary Relevant overview papers on methodologies posted on class wiki Wednesday: Epigenetics and the histone code


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