Canadian Bioinforma1cs Workshops

Transcription

1 5/12/16 Canadian Bioinforma1cs Workshops Module #: Title of Module 2 1

2 Module 3 Introduc1on to WGBS and analysis Guillaume Bourque Learning Objec/ves of Module Know the different technologies used to measure DNA methyla1on Understand their strengths and weaknesses Be familiar with the bisulfite-sequencing (BS-seq) data analysis workflow Understand the underlying principles and challenges Be able to extract methyla1on levels from BS-seq data Know how to visualize BS-seq data Be able to iden1fy differen1ally methylated regions (DMRs) 1

3 What is DNA methyla/on? Most common form of DNA methyla1on is 5- methylcytosine (5mC) Affects 70 to 80% of CpGs in the human genome High levels of 5-methylcytosine (5mC) in CpG-rich promoters is strongly associated with repression In CpG poor regions, the rela1onship between DNA methyla1on and transcrip1on is more complex Bock, Nat Rev Genet, 2012 What is DNA methyla/on? Day and Sweatt, Nat Neuro,

4 Why study methyla/on? Methyla1on is the only epigene1c mark with demonstrated mito1c inheritance DNA methyla1on has been show in to be important for: Genomic imprin1ng Transposable element silencing Stem cell differen1a1on Embryonic development Inflamma1on Bock, Nat Rev Genet, 2012 Abnormal methyla/on in Cancer Rodríguez-Paredes & Esteller Nat Med,

5 Assays for measuring DNA methyla/on Bisulphite microarrays: bisulphite muta1ons are mapped using genotyping microarrays that cover a selec1on of Cs Enrichment-based methods: methylated (alterna1vely unmethylated) DNA fragments are enriched and then measured using NGS Whole-genome bisulphite sequencing (WGBS): bisulphite muta1ons are mapped using NGS across de wholegenome Bisulphite treatment wikipedia.org 4

6 Bisulphite microarrays DNA prepara1on Bisulfite conversion Hybridiza1on onto microarray (e.g. Illumina 450K) Data normaliza1on and analysis Methyla/on profile with microarray Bock et al., Nat Biotech,

7 Processing bisulphite microarray data Bock, Nat Rev Genet, 2012 Enrichment-based: MeDIP-seq Sonifica1on of DNA Library prepara1on Denatura1on Enrichment with an1body for 5-methylcytosine Library amplifica1on High-throughput sequencing 6

8 Enrichment-based: MethylCap Sonifica1on of DNA Enrichment with methyl-binding domain protein Washing and elu1on Library prepara1on and amplifica1on High-throughput sequencing Enrichment-based: RRBS Diges1on with Mspl Library prepara1on Gel-based size selec1on Bisulfite treatment Library amplifica1on High-throughput sequencing 7

9 Enrichment-based methyla/on profiles Bock et al., Nat Biotech, 2010 Processing enrichment-based data Bock, Nat Rev Genet,

10 Differences in coverage Bock et al., Nat Biotech, 2010 WGBS Genomic DNA Isola1on Bisulfite treatment Library prepara1on High-throughput sequencing ($$$) 9

11 Enrichment-based: MCC-Seq Genomic DNA Isola1on Library prepara1on Bisulfite treatment Capture of targeted DNA-fragments High-throughput sequencing Allum et al. Nat Comm, 2015 Tools for processing WGBS (and other BS-seq) Bock, Nat Rev Genet,

12 Microarray/enrichment-based/WGBS All provide accurate DNA methyla1on measurements Microarrays typically have lower-costs and provide accurate measurements across a large number of CpGs targe1ng important regions (promoters, CpG islands) Enrichment-based methods (e.g. MeDIP-seq, MethylCap) have rela1vely low-resolu1on and can be a challenge to analyze/ normalize Bisulfite-based methods (e.g. RRBS, MCC-Seq and WGBS) provide absolute DNA measurements at a based-pair resolu1on WGBS is very expensive Workflow for analyzing BS-data Processing of bisulfite-sequencing data: Quality control and pre-processing Bisulfite sequence alignment Quan1fica1on of absolute DNA methyla1on Data visualiza1on and sta1s1cal analysis Visual inspec1on in a genome browser of selected regions Visualiza1on of global distribu1on of methyla1on values Clustering of samples based on similarity Downstream analysis Iden1fica1on of Differen1ally Methylated Regions (DMRs) Global analysis of DMRs 11

13 Workflow for analyzing BS-data Processing of bisulfite-sequencing data: Quality control and pre-processing Bisulfite sequence alignment Quan1fica1on of absolute DNA methyla1on Data visualiza1on and sta1s1cal analysis Visual inspec1on in a genome browser of selected regions Visualiza1on of global distribu1on of methyla1on values Clustering of samples based on similarity Downstream analysis Iden1fica1on of Differen1ally Methylated Regions (DMRs) Global analysis of DMRs Importance of quality control Before you start an analysis, it s very important to look at your raw data! Are all of your samples sequenced using the same protocol and instruments? Are there any technical issues affec1ng some of the samples? This is especially important if you plan to compare different samples or different condi1ons 12

14 Running FastQC on read 1 Very good! QC: Base Composi/on WGBS RRBS Felix Krueger, Babraham Bioinformatics 13

15 Read trimming Simple trimming: The method scans from the 5 end of each read. As soon as it detects a posi1on with quality scores below a preset threshold, it discards this posi1on and the remaining posi1ons at the 3 end of the read. Dynamic trimming (window based): The method searches for the longest stretch of posi1ons (window) in each read such that the quality scores of each posi1on in the window exceed a preset threshold. Moi trimming (running sum): The method starts from the 3 end of each read, subtracts a preset cutoff quality score from the quality score at each posi1on and adds the remainder to a cumula1ve score at the posi1on. The 3 por1on of the read star1ng from the posi1on with the minimum cumula1ve score is trimmed. Removing poor quality basecalls before trimming after trimming Phred score Felix Krueger, Babraham Bioinformatics 14

16 Complex/diverse library: Sequence duplica/on Duplicated library: percent methylation deduplication percent methylation Felix Krueger, Babraham Bioinformatics 31 Quality metrics Read quality Presence of adapter sequencers Duplicate rates Conversion rate 15

17 Workflow for analyzing BS-data Processing of bisulfite-sequencing data: Quality control and pre-processing Bisulfite sequence alignment Quan1fica1on of absolute DNA methyla1on Data visualiza1on and sta1s1cal analysis Visual inspec1on in a genome browser of selected regions Visualiza1on of global distribu1on of methyla1on values Clustering of samples based on similarity Downstream analysis Iden1fica1on of Differen1ally Methylated Regions (DMRs) Global analysis of DMRs Bisulfite treatment (again) Xi and Li, BMC Bioinformatics,

18 3 main strategies for processing WGBS reads Wild-card alignment Three-leier alignment Reference-free processing Wild-card aligners Replace Cs in the genomic DNA sequence by the wildcard leier Y, which matches both Cs and Ts in the read sequence Or they modify the alignment scoring matrix in such a way that mismatches between Cs in the genomic DNA sequence and Ts in the read sequence are not penalized. Solware: BSMAP, GSNAP, Last/bisulfighter, Pash, RMAP, RRBSMAP and segemehl Bock, Nat Rev Genet,

19 Three-base aligner Simplify bisulphite alignment by conver1ng all Cs into Ts in the reads and for both strands of the genomic DNA sequence Solware: Bismark, BRAT, BS-Seeker and MethylCoder Bisulphite sequence alignment Bock, Nat Rev Genet,

20 Strengths and weaknesses Three-leier aligners have lower coverage in highly methylated regions because they purge the remaining Cs from the bisulphitesequencing reads and thereby decrease their sequence complexity and they become ambiguous. Wild-card aligners typically have higher genomic coverage but at the cost of introducing some bias towards increased DNA methyla1on levels because the extra Cs in a methylated sequencing read can raise the sequence complexity These problems are more prevalent in repe11ve regions of the genome and are reduced with longer reads Bismark 19

21 3 main strategies for processing WGBS reads Wild-card alignment Three-leier alignment Reference-free processing Reference-based variant detec/on Michael Stromberg 20

22 Reference-free variant detec/on Moncunill et al. Nature Biotech 2014 Reference-free methyla/on analysis Klughammer et al. Cell Reports

23 Workflow for analyzing BS-data Processing of bisulfite-sequencing data: Quality control and pre-processing Bisulfite sequence alignment Quan1fica1on of absolute DNA methyla1on Data visualiza1on and sta1s1cal analysis Visual inspec1on in a genome browser of selected regions Visualiza1on of global distribu1on of methyla1on values Clustering of samples based on similarity Downstream analysis Iden1fica1on of Differen1ally Methylated Regions (DMRs) Global analysis of DMRs Goal: measure absolute methyla/on levels Johnson et al. Curr. Prot. Mol. Bio

24 Impact of SNPs Liu et al. Genome Biol, 2012 BS-seq data SNP/methyla/on caller Bis-SNP MethylExtract BS-SNPer Etc. 23

25 Bis-SNP Bis-SNP accuracy Liu et al. Genome Biol,

26 Workflow for analyzing BS-data Processing of bisulfite-sequencing data: Quality control and pre-processing Bisulfite sequence alignment Quan1fica1on of absolute DNA methyla1on Data visualiza1on and sta1s1cal analysis Visual inspec1on in a genome browser of selected regions Visualiza1on of global distribu1on of methyla1on values Clustering of samples based on similarity Downstream analysis Iden1fica1on of Differen1ally Methylated Regions (DMRs) Global analysis of DMRs Visualizing BS-seq data in IGV Reads are colored by DNA strand. For paired reads, this is the same as coloring by first-of-pair strand. Gray for forward reads or forward read first pairs (F1 or F1R2) Sage for reverse reads or reverse read first pairs (R1 or R1F2) The chosen mode is highlighted in reads with a red or blue nucleo1de corresponding to the posi1on of the cytosine in the reference genome. For forward reads, a red C denotes a nonconverted cytosine, implying methyl or other protec1on, while a blue T denotes a bisulfite converted cytosine. For reverse reads, a red G denotes a nonconverted cytosine, implying methyl or other protec1on, while a blue A denotes a bisulfite converted cytosine. In zoomed-out views, colored nucleo1des are represented by colored lines. 25

27 Visualizing BS-seq data in IGV (2) Visualizing BS-seq data in IGV (2) 26

28 Workflow for analyzing BS-data Processing of bisulfite-sequencing data: Quality control and pre-processing Bisulfite sequence alignment Quan1fica1on of absolute DNA methyla1on Data visualiza1on and sta1s1cal analysis Visual inspec1on in a genome browser of selected regions Visualiza1on of global distribu1on of methyla1on values Clustering of samples based on similarity Downstream analysis Iden1fica1on of Differen1ally Methylated Regions (DMRs) Global analysis of DMRs Methyla/on values methylkit (Akalin et al. Genome Biol 2012) 27

29 Pairwise correla/ons methylkit (Akalin et al. Genome Biol 2012) Sample clustering methylkit (Akalin et al. Genome Biol 2012) 28

30 Workflow for analyzing BS-data Processing of bisulfite-sequencing data: Quality control and pre-processing Bisulfite sequence alignment Quan1fica1on of absolute DNA methyla1on Data visualiza1on and sta1s1cal analysis Visual inspec1on in a genome browser of selected regions Visualiza1on of global distribu1on of methyla1on values Clustering of samples based on similarity Downstream analysis Iden1fica1on of Differen1ally Methylated Regions (DMRs) Global analysis of DMRs Differen/ally methylated regions 29

31 Need for biological replicates Hansen et al. Genome Biol 2012 Advantages of smoothing BSmooth (Hansen et al. Genome Biol 2012) 30

32 DMRs example Lee et al. Nat. Commun Coverage requirements Ziller et al. Nat Methods

33 Conclusions BS-seq analysis is not easy Need to choose the appropriate DNA methyla1on technology Need to check quality and watch for biases Need to perform a mul1ple-step analysis workflow That s what we ll do in the lab! Available WGBS datasets 32