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Methods to read out regulatory functions
Regulomics I: Methods to read out regulatory functions
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Identifying regulatory functions in genomes
Merge into general discussion of regulatory space - regulomics Noonan and McCallion, Ann Rev Genomics Hum Genet 11:1 (2010)
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Genes are not just protein coding sequences
Expression of gene A gene A limb Limb TFs gene A forebrain gene A Brain TFs Tissue specific TF neural tube gene A Neural TFs
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Regulatory mutations can cause
profound phenotypes Lettice et al. Hum Mol Genet 12:1725 (2003) Sagai et al. Development 132:797 (2005)
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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
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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)
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Biochemical indicators of regulatory function
1. TF binding 2. Histone modification H3K27ac H3K4me3 3. Chromatin modifiers & coactivators p300 MLL 4. DNA looping factors cohesin
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Methods ChIP-seq Chromatin accessibility TFs Histone mods DNase FAIRE
From Furey (2012) Nat Rev Genet 13:840
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Method I: ChIP-seq ChIP Input Peak call Signal
Align reads to reference Use peaks of mapped reads to identify binding events PCR
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Calling peaks in ChIP-seq data
Input Peak call Enrichment relative to control Highlight the challenges for both ChIP and RNA-seq in both protocols 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
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There are many ChIP-seq peak callers available
Wilbanks and Facciotti PLoS ONE 5:e11471 (2010)
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Generating ChIP-seq peak profiles
Artifacts: Repeats PCR duplicates From Park (2009) Nat Rev Genet 10:669
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Assessing statistical significance
Assume read distribution follows a Poisson distribution Many sites in input data will have some reads by chance Some sites will have many reads Poisson assumption + seq depth # 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 From Pepke et al (2009) Nat Meth 6:S22
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Assessing statistical significance
Sequencing depth matters: Poisson assumption + seq depth # of reads at a site (S) From Park (2009) Nat Rev Genet 10:669
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ChIP-seq signal profiles vary depending on factor
Transcription factors Pol II Histone mods From Park (2009) Nat Rev Genet 10:669
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Mapping chromatin accessibility
DNase I FAIRE From Furey (2012) Nat Rev Genet 13:840
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DNase I hypersensitivity identifies
regulatory elements… DNase I hypersensitive sites Case studies: TFs Which? Oct4? CTCF? Song et al., Genome Res 21:1757 (2011)
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…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
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… or motif analysis DHS sites in human ES cells:
From Neph (2012) Nature 489:83
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Q2: Making sense of regulatory functions
Compare multiple biological states Integrate multiple data sources TF function Histone modification Potential target genes Existing genome annotations
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Regulatory function is dependent on biological
context forebrain gene A Brain TFs neural tube Neural TFs limb Limb TFs
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Identifying tissue-specific regulatory function
Limb Brain Limb Sites strongly marked in Limb Sites strongly marked in both ChIP-seq signal Signal at 20,000 bound sites Clustering signal Sites strongly marked in Brain
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Identifying tissue-specific regulatory function
Limb Brain Function? Assign enhancers to genes based on proximity (not ideal) GREAT: bejerano.stanford.edu/great/ Gene ontology annotation assigned to regulatory sequences
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Q2: Making sense of regulatory functions
Compare multiple biological states Integrate multiple data sources TF function Histone modification Potential target genes Existing genome annotations
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Example from PS1: CTCF and RAD21 (cohesin)
Annotate (GREAT)
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CTCF and cohesin co-occupy many sites
Promoters Insulators Enhancers From Kagey et al (2010) Nature 467:430
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CTCF: marks insulators and promoters
Enhancers? Annotate (GREAT) CTCF: marks insulators and promoters RAD21 (cohesin): marks insulators, promoters and enhancers? Include histone modification data (Wednesday’s lecture)
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Identifying bound motifs from ChIP-seq data
CTCF ~20,000 binding sites identified by ChIP: GREAT MEME suite: From Furey (2012) Nat Rev Genet 13:840
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Single TF binding events often do not indicate regulatory function
Caveat: Single TF binding events often do not indicate regulatory function Enhancer-associated histone modification Many TFs are present at high concentrations in the nucleus TF motifs are abundant in the genome Single TF binding events may be incidental Combinations of marks/TF binding events
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Q3: Identifying the target genes for
regulatory elements forebrain gene A Brain TFs neural tube Neural TFs limb Limb TFs
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Chromosome Conformation Capture ChIP for specific factors: ChIA-PET
Sequence: 5C Sequence: Hi-C Sequence: 4C
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3C evaluates specific interaction possibilities by qPCR
Dekker et al Nat Rev Genet 14:390 (2013)
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4C identifies genome-wide interactions for a single
“bait” sequence
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ChIA-PET identifies interactions involving a particular factor
From Kieffer-Kwon et al. (2013) Cell 155:1507
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In principle, Hi-C captures all interactions, but is
limited by sequencing depth Dekker et al Nat Rev Genet 14:390 (2013)
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Hierarchical organization of the genome
Cohesin-mediated interactions Dekker et al Nat Rev Genet 14:390 (2013) Gorkin et al Cell Stem Cell 14:762 (2014)
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Summary Relevant overview papers on methodologies posted on class wiki
Wednesday: Epigenetics and the histone code
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