ChIP seq peak calling. Statistical integration between ChIP seq and RNA seq
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1 Institute for Computational Biomedicine ChIP seq peak calling Statistical integration between ChIP seq and RNA seq Olivier Elemento, PhD
2 ChIP-seq to map where transcription factors bind DNA Transcription factor of interest Antibody Genome Analyzer II (Illumina)
3 Control: input DNA Genome Analyzer II (Illumina)
4 ACCAATAACCGAGGCTCATGCTAAGGCGTTAGCCACAGATGGAAGTCCGACGGCTTGATCCAGAATGGTGTGTGGATTGCCTTGGAACTGATTAGTGAATTC TGGTTATTGGCTCCGAGTACGATTCCGCAATCGGTGTCTACCTTCAGGCTGCCGAACTAGGTCTTACCACACACCTAACGGAACCTTGACTAATCACTTAAG Average length ~ 250bp
5 25-100bp ACCAATAACCGAGGCTCATGCTAAGGCGTTAGCCACAGATGGAAGTCCGACGGCTTGATCCAGAATGGTGTGTGGATTGCCTTGGAACTGATTAGTGAATTC TGGTTATTGGCTCCGAGTACGATTCCGCAATCGGTGTCTACCTTCAGGCTGCCGAACTAGGTCTTACCACACACCTAACGGAACCTTGACTAATCACTTAAG Average length ~ 250bp
6 25-100bp ACCAATAACCGAGGCTCATGCTAAGGCGTTAGCCACAGATGGAAGTCCGACGGCTTGATCCAGAATGGTGTGTGGATTGCCTTGGAACTGATTAGTGAATTC TGGTTATTGGCTCCGAGTACGATTCCGCAATCGGTGTCTACCTTCAGGCTGCCGAACTAGGTCTTACCACACACCTAACGGAACCTTGACTAATCACTTAAG Average length ~ 250bp
7 BCL6 ChIP-seq Lymphoma cell line (OCI-Ly1) 1 lane for ChIP, 1 for input DNA, 1 for QC 36nt long sequences 30 Million reads Aligned/mapped to hg18 with BWA
8 Read mapping with BWA Illumina Read AAAATACGCGTATTCTCCCAAAACAATATC CCCAAAACAAAAAAATACGCGTATTCTCCCAAAACAATATCTTACAAGATGTAAATATACCCAAGAT Reference Human Genome (hg18)
9 Read mapping with BWA Illumina Read AAAATACGCCTATTCTCCCAAAACAATATC CCCAAAACAAAAAAATACGCGTATTCTCCCAAAACAATATCTTACAAGATGTAAATATACCCAAGAT Reference Human Genome (hg18)
10 Read mapping with BWA Illumina Read AAAATACGCCTATTCTCCCATAACAATATC CCCAAAACAAAAAAATACGCGTATTCTCCCAAAACAATATCTTACAAGATGTAAATATACCCAAGAT Reference Human Genome (hg18)
11 Reads can map to multiple locations/chromosomes Illumina Read 1 Illumina Read 2 Reference Human Genome (hg18)
12 Reads map to one strand or the other Illumina Read 1 Illumina Read 2 hg18
13 011 AGGTCACAAAACAAGTCCTAACAAATTTAAGAGTAT U chr8.fa R DD 245 GTCAGAAAAATCCTTTTTATTATATAAACAATACAT U chr5.fa F DD 15G 20G 45 GTCATCAAACTCCAAGGATTCTGTTTTCAACATACT U chr18.fa R DD 1118 GAAAGTGATTAGCAGATTGTCATTTAATAATTGTCT U chr1.fa F DD 18G 28G 74 GATAAATTTTTTCCTACAATCTTAAATTATTACACA U chr3.fa R DD 10C 28 AAAAATTAAACAATTCTAAAAATATTTTTATCTTAA U chr2.fa R DD 18C 31G 4 GCACATGTCATACTCTTTCTAGCTCTCTTATTTTTC U chr8.fa R DD 015 AAATTAATGTAAAAAATAGGATACTGAATTGTGATA U chr10.fa F DD 30G 26 GTAGTTAACAATAATTTATTTTATACTTCAAAATTC U chrx.fa R DD 7A 702 GTCAGAATTAATTAATCAAAACACCAAATGTACTTC U chr12.fa F DD 003 ATTTTGACTTTATTATTTTTTCTTCAATGTTTTTAA NM GAAAGTACATCAAATACATATTATATACTTTACATA R AATCCATATACATTTCTTTTTAATCATTTCCTCTTT U chr11.fa F DD 20G 330 GTGAGTTTCTTAATCCTGAGTTCTAATTTTATTTCA R ACATTTTATAAATTTTTAATTTCATTTTAATTTATA NM GTTTTTAAAATCAACACTTTTATTATAGAAGTAGCA U chr12.fa R DD 828 GTACTGATGTAAACTTGGTAAAAACATTGACATAAA U chr14.fa F DD 583 GAAGAAAATGACTATGTCAAAATATTATCTCTCAAT U chr5.fa F DD 1653 GTTTTACTGATTTTCTTACTTACTAAACTACCTGTT U chr7.fa F DD 943 AATGATACGGCGACCACCGACAGGTTCAGAGTTCTA NM GAGAATTATTCAGAAGTCAAATCTGTGCTTAGTTTA U chr5.fa R DD 3G 7C 318 GTATGTATCATATATATTTATGTATCATATATATTT R GATTGCTCCATTATTTGTTAAAAACATAGTAAAATA NM ATGAGATCAGTACTTCAAAGAGATATCTGCACTCCC U chr12.fa R DD 178 GTTAGTCCCAATATTCCATTAATCCCAATAAATATA U chr6.fa F DD 15G 19G 972 GAGATAATAATAGCAGTTATGGCATCGAGATAATTT U chr2.fa R DD 341 GTAGAGGGCACACATCACAAACAAGTTTCTGAGAAT R GAATATCCACTTGCAGACTTTACAAACAAATTTTTT R GGCAGATGAAACTTCTATACACTATATTTTAGCCAG U chr13.fa F DD 371 GAAAGAAAAACTATTGAAAAAATAGTTACTTTCCAA U chr1.fa R DD 614 GTGTAGATGATATCGAGGGCATTAGAAGTAAATAGC U chr5.fa F DD 808 GAGAGGAAATAATAAAGATAAAAGTAGAAAAAGTGA U chr1.fa F DD 815 GATAATTATGTTGTTGTAATTATTGTTTGTTTTTTT U chr15.fa R DD 260 GTTGACAATCCAGCTGTCATAGAAACTGACTATTTT U chr12.fa R DD 916 AAAAATTCTCCCAAAACAACAAGATGTAAATATACC U chr3.fa R DD 308 GTTCTTACACTGATATGAAGAAATACCTGAGACTGG U chr2.fa R DD 17 GAGAAACACACATATTTTTGTAAGTGCCATCACATC U chr7.fa R DD 18C 348 GTATTATCTAACACACAAGATGATGTTTGTTTTTAT NM GAGTGTAGAAAATTTTCTGCCCTAAAATATTTGTTA U chr6.fa F DD 13G 729 GTATCCTAAAGTGTATCTTATGTTTTTTCATCTTCT U chr12.fa R DD 9C 64 AATAAAACAAATTCCAATGGCTTAGATTCTACTTAA U chr10.fa R DD 15C 20C 059 AAATGGTCATACTTCCCAAAGCGATCTACAGATTCA U chr3.fa R DD 19C 061 ACATTTCCACATTTCTGTGGAAGCCTCACAATCATT R ATTAATCAACAGCAACATTAATCAACTGAATCAACA U chr2.fa R DD 47 GAATAAATAATCAAAACATATAATACATTTTTTTAT U chr5.fa F DD 32G 731 ATATACACATATATATACATATATATATACACATAT R GAGAAGGAAATGTGTTTTCTAAGTTTCTTTATCTTC U chr4.fa F DD 32G
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17 Peak detection Calculate read count at each position (bp) in genome Determine regions (peaks) where read count is greater than expected
18 genome Read count Expected read count T A T T A A T T A T C C C C A T A T A T G A T A T genome Expected read count = total number of reads * extended fragment length / chr length
19 The Poisson distribution Is the observed read count at a given genomic position greater than expected? Frequency P(X x) =1 x 1 0 x = observed read count λ = expected read count λ x e λ x! Read count
20 Is the observed read count at a given genomic position greater than expected? P(X x) =1 x 1 0 x = 10 reads (observed) λ = 0.5 reads (expected) λ x e λ x! genome P(X>=10) = 1.7 x log10 P(X>=10) = The Poisson -log10 P(X>=10) = 9.77
21 Read count Expected read count Expected read count = total number of reads * extended frag len / chr len P c (X x) =1 x 1 0 λ c x e λ c x! Log(p) Threshold peak
22 Non-mappable fraction of the genome We enumerated all 30-mers, counted # occurrences, calculated non-unique fraction of genome chr / (=12%) chr / chr / chr / chr / chr / chr / chr / chr / chr / chr / chr / chr / chrx / chr / chr / chr / chr / chrm 4628/ chr / chr / chr / chr / chr / chry / (=74%)
23 Peak detection Correct for input DNA by looking for peaks in input DNA Merge peaks separated by less than 100bp Output all peaks with length >= 100bp Process 23M reads in <7mins
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25 Jenny Giannopoulou ChIPseeqer
26 ChIP seq in lymphoma cells (LY1 cell line) Yanwen Jiang
27 RNA seq in LY1 cells RPKM RPKM = # reads per kilobase per million reads
28 Modeling the influence of a TF s binding on a promoter h k x TF= i,promoter= j = h k e d k / d 0 Peak height Weight
29 Transcription factors / histone modifications Transcripts GENE BCL6 MTA3 BCOR K4ME1 K4ME3 K79ME2 K79ME3 K79AC... DNAm LY1-RNAseq NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ How do you identify transcription factors and histone modifications that frequently work together?
30 Principal Component Analysis H3K4AC H3K4ME3
31 Principal Component Analysis H3K4AC H3K4ME3
32 Principal Component Analysis H3K4AC H3K4ME3
33 Promoter PC1 PC2 PC3 PC4 PC5 PC6 PC7... RNAseq-RPKM NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ Model: (~25,000 unique RefSeq promoters) PCs are orthogonal! RPKM i = β 0 + m j =1 β j PC ij
34 Model fitting using ordinary least squares Find ˆ β j that minimize n i=1 (RPKM i ˆ β 0 + m ˆ β j PC ij ) 2 j=1
35 Model assessment R 2 = 1 n ( y ˆ i y i ) 2 / (y i y ) 2 i =1 n i =1 (y = RPKM)
36 7 TF and 8 histone modifications predict 65% of variance in gene expression levels R 2 =0.65, Spearman=0.804
37 Assessing individual coefficients RPKM i = ˆ β 0 + m j=1 ˆ β j PC ij Calculate t-statistic t = ˆ β /se( ˆ β ) j j j Calculate p-value using t-distribution with n-p degrees of freedom P(X t j )
38 attach(m) fit <- lm(log(rpkm+1) ~ PC1 + PC2 + PC3 + PC4 + PC5 + PC6 + PC7 + PC8 + PC9 ) print(summary(fit)) Coefficients: Estimate Std. Error t value Pr(> t ) (Intercept) < 2e-16 *** PC < 2e-16 *** PC < 2e-16 *** PC < 2e-16 *** PC PC < 2e-16 *** PC e-08 *** PC e-06 *** PC e-10 *** PC < 2e-16 *** --- Signif. codes: 0 *** ** 0.01 * Residual standard error: on degrees of freedom (3543 observations deleted due to missingness) Multiple R-squared: , Adjusted R-squared: F-statistic: on 9 and DF, p-value: < 2.2e-16
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40 BCL6 In silico knockdown 1. Set all BCL6 values to Project new binding data into original PCs 3. Predict RPKMs using original fitted model 4. Compare RPKMs to RPKMs predicted by original binding data and original model
41 Transcription factors / histone modifications Transcripts GENE BCL6 MTA3 BCOR K4ME1 K4ME3 K79ME2 K79ME3 K79AC... DNAm LY1-RNAseq NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ RPKM i = β 0 + m j =1 β j PC ij
42 Simulated BCL6 knockdown predicts (correctly) that BCL6 is an obligate repressor No genes are significantly downregulated! Top 250 up regulated genes are enriched with genes with expression higher in NB compared to LY1 (p<0.005)
43 Simulated PU.1 knockdown (activator) rediscovers important PU.1 targets STRING analysis of top 40 down regulated genes + PU.1
44 Do CNVs contribute to the model? RPKM i = β 0 + m j =1 β j PC j + β m+1 CNV i Gene copy number We generated Affymetrix 6.0 SNP array data in LY1 Yanwen Jiang, Huimin Geng
45 Ongoing work Using non negative matrix factorization instead of PCA Integration of DNA looping Integration of post transcriptional regulation (can we improve over 65%?) Experimental validation (knockdowns)
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47 Can the binding patterns predict what will happen upon sirna knockdown? log(sibcl6/nt ) i = β 0 + m j=1 β j PC ij
48 Ly1 expression sibcl6/nt 24h
49 BCL6 In silico knockdown 1. Set all BCL6 values to Project new binding data into original PCs 3. Predict RPKMs using original fitted model 4. Compare RPKMs to RPKMs predicted by original binding data and original model
50 GENE BCL6 MTA3 BCOR K4ME1 K4ME3 K79ME2 K79ME3 K79AC... DNAm LY1-RNAseq NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ (~25,000 unique RefSeq promoters)
51 BCL6 In silico knockdown 1. Set all BCL6 values to Project new binding data into original PCs 3. Predict RPKMs using original fitted model 4. Compare RPKMs to RPKMs predicted by original binding data and original model
Ins?tute for Computa?onal Biomedicine. ChIP- seq. Olivier Elemento, PhD TA: Jenny Giannopoulou, PhD
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