Integrated analysis of sequencing data

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1 Integrated analysis of sequencing data How to combine *-seq data M. Defrance, M. Thomas-Chollier, C. Herrmann, D. Puthier, J. van Helden *ChIP-seq, RNA-seq, MeDIP-seq,

2 Transcription factor binding ChIP-seq Expression quantification RNA-seq DNA methylation MeDIP-seq Integrated analysis of genome-wide DNA methylation and gene expression profiles in molecular subtypes of breast cancer Je-Keun Rhee 1, Kwangsoo Kim 2, Heejoon Chae 3, Jared Evans 4, Pearlly Yan 5, Byoung-Tak Zhang 1,6, Joe Gray 7, Paul Spellman 7, Tim H.-M. Huang 8, Kenneth P. Nephew 9,1 and Sun Kim 1,2,6, * Integrated Analysis? Integrated analysis of DNA methylation and gene expression reveals specific signaling pathways associated with platinum resistance in ovarian cancer Meng Li 1, Curt Balch 1,2,3, John S Montgomery 1, Mikyoung Jeong 4, Jae Hoon Chung 4, Pearlly Yan 5, Tim HM Huang 5, Sun Kim* 6,7 and Kenneth P Nephew* 1,2,3,8 Combined ChIP-Seq and transcriptome analysis identifies AP-1/JunD as a primary regulator of oxidative stress and IL-1β synthesis in macrophages Richard P Hull 1, Prashant K Srivastava 1, Zelpha D Souza 1, Santosh S Atanur 1, Fatima Mechta-Grigoriou 2, Laurence Game 1, Enrico Petretto 1, H Terence Cook 3, Timothy J Aitman 1 and Jacques Behmoaras 3*

3 Transcription factor binding ChIP-seq OPTION 1 OPTION 2 1 CONDITION CONDITION 1 CONDITION 2 Enriched regions Differentially enriched regions

4 μ-array Commercial / custom Coding, non coding! Expression quantification RNA-seq RNA-seq Coding, non coding Alternative transcripts! CONDITION 1 CONDITION 2 Differentially expressed genes Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks Cole Trapnell 1,2, Adam Roberts 3, Loyal Goff 1,2,4, Geo Pertea 5,6, Daehwan Kim 5,7, David R Kelley 1,2, Harold Pimentel 3, Steven L Salzberg 5,6, John L Rinn 1,2 & Lior Pachter 3,8,9

5 Differentially expressed genes? 2 condition x 1 sample

6 Differentially expressed genes? Genes: C2/C log 1 (P value) Significant No Yes log 2 (fold change) Figure 8 CummeRbund volcano 2 condition plots x reveal n samples genes, transcripts, TSS groups or CDS groups that differ significantly between the pairs of conditions C1 and C2.

7 Enrichment-based methods Methylated (alternatively, unmethylated) DNA fragments are enriched in a DNA library. The library composition is quantified by next-generation sequencing! DNA methylation-seq Bisulphite sequencing DNA treatment with bisulphite specifically introduces mutations at unmethylated Cs. These mutations are mapped by next- generation sequencing Enrichment-based methods Methylated (alternatively, unmethylated) DNA fragments are enriched in a DNA library. The library composition is quantified by nextgeneration sequencing OPTION 1 OPTION 2 1 CONDITION CONDITION 1 CONDITION 2 Methylation level (CpG, region) Differentially methylated regions

8 DNA methylation mapping technologies Figure 2 Comparison of DNA methylation maps obtained with four different methods. The screenshot shows genome browser tracks for MeDIP-seq (first two tracks, in green), MethylCapseq (three tracks in blue, gray and red), RRBS (stacked light blue tracks) and Infinium (single black track with percentage values) across the HOXA cluster in a human ES cell line (HUES6). Each track represents data from a single sequencing lane (MeDIP-seq, MethylCap-seq, RRBS) or microarray hybridization (Infinium). MeDIP-seq and MethylCap-seq data are visually similar to ChIP-seq data, with peaks in regions that show high density of the target molecule (5-methylcytosine) and troughs in regions with low density of methylated cytosines. The heights of the peaks RRBS represents the number of reads in each genomic interval, for each track normalized to the same genome-wide read count. RRBS gives rise to clusters of CpGs with absolute DNA methylation measurements, separated by regions that are not covered due to the reduced-representation property of the RRBS protocol. Each data point corresponds to the methylation level at a single CpG, and dark blue points indicate higher methylation levels than light blue points. Infinium data is represented in a similar way to the RRBS data, and the methylation levels at single CpGs are shown as percentage values. For reference, the CpG density is indicated by stacked points (black) at the bottom of the diagram, and CpG islands (red) as well as known genes (blue) are listed as described previously 55,56. MethylCap-seq MeDIP-seq WG bisulfite-seq μ-array (infinium) MeDIP (lane 1) MeDIP (lane 2) MethylCap (high methylation fraction) MethylCap (medium methylation fraction) MethylCap (low methylation fraction) RRBS (lane 1) RRBS (lane 2) Infinium Human- Methylation27 CpG Islands CpG Dinucleotides Scale chr7: kb MeDIP read a counts for HUES6.MeDIP U S - sequencing lane 1 4% 2% 7% 9% MeDIP read a counts for HUES6.MeDIP U S - sequencing lane 2 MethylCap read counts t for HUES6.MethylCap C p - sequencing lane 1 (high methylation fraction) MethylCap read counts for HUES6.MethylCap p - sequencing lane 2 (medium methylation fraction) MethylCap read counts s for o HUES6.MethylCap C - sequencing lane 3 (low methylation fraction) RRBS CpG methylation for HUES6.RRBS - sequencing lane 1 RRBS CpG methylation for HUES6.RRBS - sequencing lane 2 Infinium niu CpG methylation y t for HUES6.Infinium 59% % 8% % 77% 42% 45% 8% 8% % 83% 33% 11% 12% 1 % 33% 3 71% 6% 87% 34% 51% Bona fide CpG islands, l n, predicted r to exhibit x b a combined epigenetic score above.67 Perfect P f Matches to Short Sequence (CG) 4% 4% RefSeq Genes HOXA1 HOXA1 HOXA3 HOXA3 HOXA2 HOXA3 HOXA4 RefSeq S q Genes HOXA5 HOXA7 HOXA6 HOXA1 HOXA9 HOXA1 HOXA11AS HOXA11 HOXA13

9 eature selection. For RRBS, the DNA methylation level was determined as the percentage of methylated DNA methylation mapping technologies: Genomic coverage MeDIP-seq MethylCap-seq RRBS Infinium CpG Islands (length 7 bp) 44,44 regions genome-wide No coverage No coverage No coverage No coverage Promoter regions (2 kb centered on TSS) 23,69 regions genome-wide No coverage No coverage No coverage No coverage Whole genome (1 kb sliding window) No coverage No coverage ,858,143 regions genome-wide No coverage No coverage a b c DNA methylation level (MeDIP-seq) Pearson's r = DNA methylation level (Infinium) Comparative quantification DNA methylation level (MethylCap) Pearson's r = DNA methylation level (Infinium) DNA methylation level (RRBS) Pearson's r = DNA methylation level (Infinium)

10 Differentially Methylated Regions (DMRs)? CONDITION 1 CONDITION 2

11 Identifying differentially methylated regions (DMRs) CONDITION 1 CONDITION 2 5mC-seq profiles Compute differential mc OPTION 1: fragment the genome (promoters, genes, ) OPTION 2: sliding window approach DMRs

12 Differentially methylated region, CpG or promoter? a Genomic DNA sequence CG CG CG CG CG CG CG CG CG CG b Single-CpG analysis CG1 CG2 CG3 CG4 CG5 CG6 CG7 CG8 CG9 CG1 c Genome-wide tiling analysis Tiling region 1 Tiling region 3 Tiling region 5 Tiling region 7 Tiling region 2 Tiling region 4 Tiling region 6 Tiling region 8 d Annotated genome analysis Enhancer Promoter region First exon fragmentation level

13 Differentially methylated regions Normalized Read count chr12: BR 1 BR chr16: BR 1 BR chr12: BR 1 BR chr19: BR 1 BR5 6 Region Log ratio Fold change Pvalue RefSeq.ID chr12: E-3 chr16: E-3 chr12: E-3 NM_6312 chr19: E-3 NM_14921 chr5: E-3 chr14: E-3 chr4: E-3 chr6: E-3 NM_1341 chr3: E-3 NM_ chr11: E-3 NM_32427 chr3: E-3 NM_32251 chr11: E-3 NM_7368 chr11: E-3 chr13: E-3 NM_ chr17: E-3 NM_4252 chr16: E-3 NM_13957

14 Combining RNA-seq and ChIP-seq Transcription factor binding ChIP-seq + Expression quantification RNA-seq OPTION 1: focus on genes, on/off state OPTION 2: quantitative analysis / correlation

15 OPTION 1: focus on genes, on/off state ChIP-seq Peaks Annotated peaks Genes (enriched at promoter, ) ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data Lihua J Zhu 1,2*, Claude Gazin 3, Nathan D Lawson 1,2, Hervé Pagès 4, Simon M Lin 5, David S Lapointe 6, Michael R Green 1,2

16 OPTION 1: focus on genes, on/off state RNA-seq Differential analysis Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks Cole Trapnell 1,2, Adam Roberts 3, Loyal Goff 1,2,4, Geo Pertea 5,6, Daehwan Kim 5,7, David R Kelley 1,2, Harold Pimentel 3, Steven L Salzberg 5,6, John L Rinn 1,2 & Lior Pachter 3,8,9 Genes up / down regulated

17 OPTION 1: focus on genes, on/off state Genes up / down regulated Genes (enriched at promoter, ) Differentially expressed genes targeted by TF

18 OPTION 2: quantitative analysis ChIP-seq Annotated peaks RNA-seq Expression differences

19 OPTION 2: quantitative analysis RNA-seq expressed genes ChIP-seq Differential enrichment

20 Histone marks and TF binding A B E2F3 Significant peaks TSS within 1 kb of peaks 4879 E2F3 Hells H3K27me Hells C H3K27me3 Scale (Chr9): 1 kb E2F3 Tags Hells RefSeq Mll1

21 Co-localization a b 4. H2B S112 GlcNAc 1,468 OGT TET2 1,172 2,744 (5,723) (8,392) 2, ,729 1,255 Relative tag density TET2 OGT c TET2 H2B S112 GlcNAc OGT H2B S112 GlcNAc (6,61) Acer2 4, 3, 2, 1, 1, 2, 3, 4, Relative distance to TSS (bp) 1 kb 1 1 kb 1 1 kb Zmym6 Tulp4

22 A 2967 B Normalized read count Tet2 99% O-GlcNAc ChIP-Seq RNA Pol II Overlap Tet2 O-GlcNAc Tet2 Others All H3K4me3 O-GlcNAc 85% H3K4me Normalized read count RNA-seq ChIP-seq Mouse bone marrow: ChIP-Seq Tet2, O-GlcNAc, H3K4me3 Peak density ratio RNA-Seq Tet2 Others All H3K4me3 O-GlcNAc Relation to CpG island CGI Shores RNA Pol II 7378 Overall distribution Distant Peak density ratio Genomic location CTet2 knock-out: WB O-GlcNAc Wt Promoter Tet2 Ko WB O-GlcNAc WB HDAC1 Gene body Intergenic Promoter CpG density HCP 63.% ICP 34.3% LCP 2.7% D Tet2 knock-out: ChIP-Seq H3K4me3 log(peak P-value) Wt Tet2 Ko

23 b Chr17: 34,9, CpG-rich promoters (n = 13,856) CpG-poor promoters (n = 9,965) CpG island Tet1 hip-seq) 4 Tet1 peaks Tet1 peak enrichment log1 (peak P value) (kb) b Chr17: 34,9, c 35,15, 2 Tet1 peaks Tet1 (exp1) 5 Tet1 (exp2) Tet1 (exp2) 5 Kdm2a H3K4me3 CpG island 2 Tet1 (exp1) Distance to TSS (kb) Kdm2a IgG.5 d t genomic regions with high-density CpG wide occupancy of Tet1 at all annotated gene CpG-rich genes; red, CpG-poor genes). The was determined by ChIP-seq analysis. Average log1 (peak P values) in 2-bp bins is shown ring 5 kb up- and downstream of TSSs. Log1 (ChIP-seq peak P value) Low CpG island occupancy (ChIP-seq) 35,15, H3K4me3.5 5 IgG.5 Tet1 High Kdm2a H3K4me3 Genes with CpG-rich promoters a Genes with CpG-poor promoters RESEARCH LETTER s (n = 13,856) ers (n = 9,965) +2.5 Histone marks and TF binding 5 +5 Distance to TSS (kb) are also indicated. c, Heatmap representation of genomic regions with highdensity CpG sites (CpG islands), binding profiles of Tet1, Kdm2a4 and H3K4me313 in ES cells at all annotated mouse genes promoters (5 kb flanking TSSs of Refseq genes). The heatmap is rank-ordered from genes with CpG islands of longest length to no CpG islands within 5-kb genomic regions flanking TSSs. The presence of CpG islands is shown in colour (blue, present;

24 Combining Histone marks and TF binding and expression Gene expression Con KD Tet1 KD CpG island Tet1 H3K27me3 H3K4me3 H3K4me1 H3K36me3 Krt8 Sox17 Tet1-repressed targets (n = 677) Pdgfra Hoxa1 Gata6 Eomes Lhx2 Cdx2 Tet1-activated targets (n = 39) Distance to TSS (kb) Esrrb Tcl1 Nanog Prmt8

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