regulomics i: methods to read out regulatory functions
TRANSCRIPT
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Regulomics I:Methods to read out regulatory functions
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Noonan and McCallion, Ann Rev Genomics Hum Genet 11:1 (2010)
Identifying regulatory functions in genomes
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forebraingene A
Brain TFs
neural tubegene A
Neural TFs
limb
Limb TFs
gene A
Expression ofgene A
Genes are not just protein coding sequences
gene A
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Lettice et al. Hum Mol Genet 12:1725 (2003) Sagai et al. Development 132:797 (2005)
Regulatory mutations can causeprofound phenotypes
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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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1. TF binding
Biochemical indicators of regulatory function
2. Histonemodification • H3K27ac • H3K4me3
3. Chromatinmodifiers &coactivators
p300 MLL
4. DNA loopingfactors cohesin
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MethodsChIP-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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ChIP-seq is an enrichment methodRequires a statistical framework for determining the significance of enrichment
ChIP-seq ‘peaks’ are regions of enriched read density relative to an input controlInput = sonicated chromatin collected prior to immunoprecipitation
ChIP
Input
Peak call Enrichment relative to control
Calling peaks in ChIP-seq data
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Wilbanks and Facciotti PLoS ONE 5:e11471 (2010)
There are many ChIP-seq peak callers available
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From Park (2009) Nat Rev Genet 10:669
Generating ChIP-seq peak profiles
Artifacts:
• Repeats• PCR duplicates
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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
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Assessing statistical significance
# of reads at a site (S)
From Park (2009) Nat Rev Genet 10:669
Sequencing depth matters:
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ChIP-seq signal profiles vary depending on factor
Transcriptionfactors
Pol II
Histonemods
From Park (2009) Nat Rev Genet 10:669
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DNase I FAIRE
Mapping chromatin accessibility
From Furey (2012) Nat Rev Genet 13:840
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Song et al., Genome Res 21:1757 (2011)
DNase I hypersensitivity identifiesregulatory elements…
DNase I hypersensitive sites
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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 ChIPin GM12878
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DHS sites in human ES cells:
From Neph (2012) Nature 489:83
… or motif analysis
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Q2: Making sense of regulatory functions
Integrate multiple data sources• TF function• Histone modification• Potential target genes• Existing genome annotations
Compare multiple biological states
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Regulatory function is dependent on biologicalcontext
forebraingene A
Brain TFs
neural tubegene A
Neural TFs
limb
Limb TFs
gene A
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Identifying tissue-specific regulatory function
ChIP-seq signal
Sign
al a
t 20,
000
boun
d si
tes
LimbLimb Brain
Sites strongly marked in Limb
Sites strongly marked in Brain
Clustering
Sites strongly marked
in both
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Limb Brain
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
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Q2: Making sense of regulatory functions
Integrate multiple data sources• TF function• Histone modification• Potential target genes• Existing genome annotations
Compare multiple biological states
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Example from PS1: CTCF and RAD21 (cohesin)
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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 promotersRAD21 (cohesin): marks insulators, promoters and enhancers?Include histone modification data (Wednesday’s lecture)
Promoter Enhancers?
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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:http://meme.nbcr.net/meme/
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Enhancer-associatedhistone modification
Caveat:Single TF binding events often do not indicate regulatory function
• Many TFs are present at high concentrationsin the nucleus
• TF motifs are abundant in the genome
• Single TF binding events may be incidental
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Q3: Identifying the target genes forregulatory elements
forebraingene A
Brain TFs
neural tubegene A
Neural TFs
limb
Limb TFs
gene A
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Sequence: Hi-C
ChIP for specific factors:ChIA-PET
Sequence: 4C
Chromosome Conformation Capture
Sequence: 5C
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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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From Kieffer-Kwon et al. (2013) Cell 155:1507
ChIA-PET identifies interactions involving a particular factor
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In principle, Hi-C captures all interactions, but islimited by sequencing depth
Dekker et al Nat Rev Genet 14:390 (2013)
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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
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Summary
• Relevant overview papers on methodologies posted on class wiki
• Wednesday: Epigenetics and the histone code