Héctor Corrada Bravo CMSC858P Spring 2012 (many slides courtesy of Rafael Irizarry)
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Transcript of Héctor Corrada Bravo CMSC858P Spring 2012 (many slides courtesy of Rafael Irizarry)
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Introduction to epigenetics: chromatin modifications, DNA methylation and the CpG Island landscape (part 2)
Héctor Corrada BravoCMSC858P Spring 2012
(many slides courtesy of Rafael Irizarry)
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How do we measure DNA methylation?
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Microarray Data
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One question…
• Where do we measure?
• At least 7 arrays are needed to measure entire genome
• CpG are depleated
• Remaining CpGs cluster
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CpG Islands
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But variation seen outside
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McRBC
No Methylation
Cuts at AmCG or GmCG Input
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McRBC
Methylation
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McRBC after GEL
Methylation
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McRBC after GEL
Methylation
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Now unmethylated
No Methylation
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McRBC after Gel
No Methylation
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Gene Expression Normalization does not work well here
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We use control probes
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There are also waves
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Smoothing
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McRBC on tiling two channel array
We smooth
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Proportion of neighboring CpG also methylated/not methylated
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True signal (simulated)
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Observed data
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Observed data and true signal
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What is methylated (above 50%)?
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Naïve approach
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Many false positives (FP)
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Smooth
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No FP, but one false negative
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Smooth less? No FN, lots of FP
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We prefer this!
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CHARMDMR for three tissues (five replicates)
Irizarry et al, Nature Genetics 2009
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Some findings
[Irizarry et al., 2009, Nat. Genetics]
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Tissue easily distinguished
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Cancer DMR
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Many Regions like thisNote: hypo and hyper methylation
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Both hyper and hypo methylated
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Cancer and Tissue DMRs coincide
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DMR enriched in Shores
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Still affects expression
T-DMRs
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Still affects expression
C-DMRs
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USING SEQUENCING (BS-SEQ)
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TTCGATTACGA
AAGCTAATGCT
CH3
CH3
TTCGATTACGA
AAGCTAATGCT
CH3
CH3
Liver Brain
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TTCGATTACGA
AAGCTAATGCT
CH3
CH3
TTCGATTACGA
AAGCTAATGCT
CH3
CH3
TTCGATTACGA
AAGCTAATGCT
CH3
CH3
TTCGATTACGA
AAGCTAATGCT
CH3
TTCGATTACGA
AAGCTAATGCT
CH3
CH3TTCGATTACGA
AAGCTAATGCT
CH3
CH3
TTCGATTACGA
AAGCTAATGCT
CH3
CH3
85% Methylationchr3:44,031,616-44,031,626
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Bisulfite Treatment
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Bisulfite Treatment
GGGGAGCAGCATGGAGGAGCCTTCGGCTGACT
GGGGAGCAGTATGGAGGAGTTTTCGGTTGATT
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BS-seq
GTCGTAGTATTTGTCT GTCGTAGTATTTGTNN TGTCGTAGTATCTGTC TATGTCGTAGTATTTG TATATCGTAGTATTTT TATATCGTAGTATTTG NATATCGTAGTATNTG TTTTATATCGCAGTAT ATATTTTATGTCGTA ATATTTTATCTCGTA ATATTTTATGTCGTA GA-TATTTTATGTCGTGATCACAGGTCTATCACCCTATTAACCACTCACGGGAGCTCTCCATGCATTTGGTATTTTCGTCTGGGGGGTATGCACGCGATAGCATTGCGAGACGCTGGAGCCGGAGCACCCTATGTCGCAGTATCTGTCTTTGATTCCTGCCTCATCCTATTATTTATCGCACCTAC
GTTCAATATT
Coverage: 13Methylation Evidence: 13Methylation Percentage: 100%
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BS-seq
GTCGTAGTATTTGTCT GTCGTAGTATTTGTNN TGTCGTAGTATCTGTC TATGTCGTAGTATTTG TATATTGTAGTATTTT TATATCGTAGTATTTG NATATTGTAGTATNTG TTTTATATTGCAGTAT ATATTTTATGTCGTA ATATTTTATCTTGTA ATATTTTATGTCGTA GA-TATTTTATGTCGTGATCACAGGTCTATCACCCTATTAACCACTCACGGGAGCTCTCCATGCATTTGGTATTTTCGTCTGGGGGGTATGCACGCGATAGCATTGCGAGACGCTGGAGCCGGAGCACCCTATGTCGCAGTATCTGTCTTTGATTCCTGCCTCATCCTATTATTTATCGCACCTAC
GTTCAATATT
Coverage: 13Methylation Evidence: 9Methylation Percentage: 69%
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BS-seq
GTCGTAGTATTTGTCT GTCGTAGTATTTGTNN TGTTGTAGTATCTGTC TATGTTGTAGTATTTG TATATTGTAGTATTTT TATATTGTAGTATTTG NATATTGTAGTATNTG TTTTATATTGCAGTAT ATATTTTATGTCGTA ATATTTTATCTTGTA ATATTTTATGTTGTA GA-TATTTTATGTCGTGATCACAGGTCTATCACCCTATTAACCACTCACGGGAGCTCTCCATGCATTTGGTATTTTCGTCTGGGGGGTATGCACGCGATAGCATTGCGAGACGCTGGAGCCGGAGCACCCTATGTCGCAGTATCTGTCTTTGATTCCTGCCTCATCCTATTATTTATCGCACCTAC
GTTCAATATT
Coverage: 13Methylation Evidence: 4Methylation Percentage: 31%
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BS-seq
• Alignment is much trickier:– Naïve strategy: do nothing, hope not many CpG in a
single read– Smarter strategy: “bisulfite convert” reference: turn all
Cs to Ts• Also needs to be done on reverse complement reference and
reads– Smartest strategy: be unbiased and try all combinations
of methylated/un-methylated CpGs in each read• Computationally expensive (see Hansen et al, 2011, for a
strategy)
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BS-seq
• There are similarities to SNP calling (we’ll see this in a couple of weeks)
• EXCEPT: we want to measure percentages– Use a binomial model to estimate p, percentage of
methylation– Allow for sequencing errors, coverage differences,
etc.
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Measuring DNA Methylation
• Estimating percentages• Use “local-likelihood”
method– Based on loess
(Plot courtesy of Kasper Hansen)
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BS-seq
Lister et al. 2009, Nature
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Gene Expression Regulation: DNA methylation in promoter regions
Lister et al. 2009, Nature
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DNA methylation patterns within genomic regions
Lister et al. 2009
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Putting it together
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What were we after?
• The epigenetic progenitor origin of human cancer• [Feinberg, et al., Nature Reviews Genetics, 2006]• Stochastic epigenetic variation as driving force of
disease• [Feinberg & Irizarry, PNAS, 2009]• Phenotypic variation, perhaps epigenetically mediated,
increases disease susceptibility• Increased epigenetic and gene expression variability of
specific genes/regions is a defining characteristic of cancer
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What did we do?
• Custom Illumina methylation microarray• Confirmed increased epigenetic variability in
specific regions across five cancer types
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What did we do?
• Custom Illumina methylation microarray• Confirmed increased epigenetic variability in
specific regions across five cancer types
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What did we do?• Custom Illumina methylation microarray
• Confirmed increased epigenetic variability in specific regions across five cancer
types
• Confirmed same sites are involved in tissue differentiation
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What did we do?• Custom Illumina methylation microarray
• Whole genome sequencing of bisulfite treated DNA– Found large blocks of hypo-methylation (sometimes Mbps long) in
colon cancer
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What did we do?• Custom Illumina methylation microarray
• Whole genome sequencing of bisulfite treated DNA– Found large blocks of hypo-methylation (sometimes Mbps long) in
colon cancer– These regions coincide with hyper-variable regions across cancer types
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What did we do?• Custom Illumina methylation microarray• Whole genome sequencing of bisulfite treated DNA• Gene Expression Analysis
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Gene Expression Data
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Gene Expression Data
When using multiple microarray experiments, proper normalization is key[McCall, et al., Biostatistics 2010]
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Normalization is key
• fRMA: a single-chip normalization procedure• GNUSE: a single-chip quality metric• Barcode: a single-chip common-scale
measurement
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What did we do?• Custom Illumina methylation microarray• Whole genome sequencing of bisulfite treated DNA• Gene Expression Analysis
– Genes with hyper-variable gene expression in colon cancer are enriched in hypo-methylation blocks
[Corrada Bravo, et al., under review]
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What are we doing next?• Custom Illumina methylation microarray• Whole genome sequencing of bisulfite treated DNA• Gene Expression Analysis
– Genes with hyper-variable gene expression in colon cancer are enriched in hypo-methylation blocks
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Bigger gene expression study
• 7,741 HGU133plus2 samples• 598 normal tissue samples, 4,886 tumor
samples• 176 different tissue types• 175 different GEO studies
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Bigger gene expression study
[Corrada Bravo, et al., under review]
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What are we doing next?• Custom Illumina methylation microarray• Whole genome sequencing of bisulfite treated DNA• Gene Expression Analysis
– Genes with hyper-variable gene expression in colon cancer are enriched in hypo-methylation blocks
– Tissue-specific genes have hyper-variable gene expression across cancer types
[Corrada Bravo, et al., under review]
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[Corrada Bravo, et al., under review]
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[Corrada Bravo, et al., under review]