Experience Sharing: Methylation Sequencing Analysis

Yi-Feng ChangInstitute of Biomedical Informatics, NYMU

Outline

DNA Methylation

Bisulfite Sequencing Technologies

BS-Seq Data Analysis

Tools for BS-Seq Data Analysis

A Case Study

2

Epigenetics Overview

3http://commonfund.nih.gov/epigenomics/figure.aspx

DNA Methylation: Functions and Diseases

4Singal, R. & Ginder, G.D. DNA Methylation. Blood Journal 93, 4059-4070 (1999).

Lee, K.W. & Pausova, Z. Cigarette smoking and DNA

methylation. Front Genet 4, 132 (2013).

Mikeska, T. & Craig, J.M. DNA methylation biomarkers: cancer

and beyond. Genes (Basel) 5, 821-64 (2014).

Grayson, D.R. & Guidotti, A. The dynamics of DNA methylation in schizophrenia and related

psychiatric disorders. Neuropsychopharmacology 38, 138-66 (2013).

Chen, C. et al. Correlation between DNA methylation and gene expression in the brains of

patients with bipolar disorder and schizophrenia. Bipolar Disord 16, 790-9 (2014).

De Jager, P.L. et al. Alzheimer's disease: early alterations in brain DNA methylation at ANK1,

BIN1, RHBDF2 and other loci. Nat Neurosci 17, 1156-63 (2014).

DNA Methylation Pathway

5

Moore, L.D., Le, T. & Fan, G. DNA methylation and its basic function. Neuropsychopharmacology 38, 23-38 (2013).

DNA Demethylation Pathway

6Moore, L.D., Le, T. & Fan, G. DNA methylation and its basic function. Neuropsychopharmacology 38, 23-38 (2013).

• 5mC: 5-Methylcytosine

• 5hmC: 5-hydroxymethylcytosine

• 5hmU: 5-hydroxymethyluracil

• 5fC: 5-formylcytosine

• 5caC: 5-carboxycytosine

• Tet: Ten-eleven translocation enzymes

• AID/ APOBEC: activation-induced cytidine

deaminase/apolipo- protein B mRNA-editing

enzyme complex

• TDG: Thymine DNA glycosylase

• SMUG1: Single-strand-selective

monofunctional uracil-DNA glycosylase 1

Bisulfite Sequencing Technologies

Timeline of Technologies for Studying DNA Methylation

8

MS-HRM

MeDIP-Seq

BS-Seq

MethylC-SeqTAB-Seq

MAB-Seq

Harrison, A. & Parle-McDermott, A. DNA methylation: a timeline of methods and applications. Front Genet 2, 74 (2011).

COBRA: Combined Bisulfite Restriction AnalysisAP-PCR: Methylation-Sensitive Arbitrarily Primed PCRAIMS: DNA methylation by amplification of intermethylated sitesRRBS: Reduced representation bisulfite sequencing

MS-HRM: Methylation-sensitive high resolution meltingMeDIP-Seq: Methylated DNA immunoprecipitation sequencingMethylC-Seq/BS-Seq: Bisulfite sequencingTAB-Seq: Tet-Assisted Bs-SeqMAB-Seq: M.SssI methylase-assisted BS-Seq

2015

The Steps to Determining the Methylation Status of Cytosine in a Known DNA Sequence by The Bisulfite Conversion Method

9Singal, R. & Ginder, G.D. DNA Methylation. Blood Journal 93, 4059-4070 (1999).

Techniques for Enrichment of Methylated or Target Regions Prior to BS- Seq

10

Lister, R. & Ecker, J.R. Finding the fifth base: genome-wide sequencing of cytosine methylation. Genome Res 19, 959-66 (2009).

Genomic DNA

Deep Sequencing

Techniques for Genome-Wide Sequencing of Cytosine Methylation Sites

11

Lister, R. & Ecker, J.R. Finding the fifth base: genome-wide sequencing of cytosine methylation. Genome Res 19, 959-66 (2009).

Genomic DNA

Deep Sequencing

Approaches for Detecting Active DNA Demethylation at Single Base Resolution

12

TAB-Seq: Tet-Assisted Bs-Seq

Yu, M. et al. Tet-assisted bisulfite sequencing of 5-hydroxymethylcytosine. Nat Protoc 7, 2159-70 (2012).Yu, M. et al. Base-resolution analysis of 5-hydroxymethylcytosine in the mammalian genome. Cell 149, 1368-80 (2012).

MAB-Seq: M.SssI methylase-assisted BS-Seq

Wu, H., Wu, X., Shen, L. & Zhang, Y. Single-base resolution analysis of active DNA demethylation using methylase-assisted bisulfite sequencing. Nat Biotechnol 32, 1231-40 (2014).

Genomic Coverage of MeDIP-seq, MethylCap-seq, RRBS and Infinium

13Bock, C. et al. Quantitative comparison of genome-wide DNA methylation mapping technologies. Nat Biotechnol 28, 1106-14 (2010).

MeDIP-seq and MethylCap-seq provide broad coverage of the genome, whereas RRBS and Infinium are more restricted to CpG islands and promoter regions

Key Metrics of the Technology Comparison

14Beck, S. Taking the measure of the methylome. Nat Biotechnol 28, 1026-8 (2010).

BS-Seq Data Analysis

15

Effect and Problems of Bisulfite Treatment of DNA

16

Krueger, F., Kreck, B., Franke, A. & Andrews, S.R. DNA methylome analysis using short bisulfite sequencing data. Nat Methods 9, 145-51 (2012).

Mapping bisulfite reads to 4 possible bisulfite strands (OT/CTOT/OB/CTOB) is equivalent to mapping the bisulfite read and its reverse complementary read to both Top/Bottom strands of the original reference sequence.

How to Align BS Reads Against Reference Genome?

17Krueger, F. & Andrews, S.R. Bismark: A flexible aligner and methylation caller for Bisulfite-Seqapplications. Bioinformatics (2011).

Bock, C. Analysing and interpreting DNA methylation data. Nat Rev Genet 13, 705-19 (2012)

Y=C or T

TCGA TCGT ACGTATGA

Multiple hits

TTGT ATGT

Multiple hits

TCGA ATGA

Workflow for Analyzing BS-Seq data

18Krueger, F., Kreck, B., Franke, A. & Andrews, S.R. DNA methylome analysis using short bisulfite sequencing data. Nat Methods 9, 145-51 (2012).

http://omictools.com/bisulfite-seq/

Published Tools

19

B-SOLANA Bisulphite aligner for processing bisulphite-sequencing color space data http://code.google.com/p/bsolana

BatMeth Base and color space data http://code.google.com/p/batmeth

Bicycle Lister et al. 2009 workflow http://sing.ei.uvigo.es/bicycle/howitworks.html

BiQ Analyzer HTLocus-specific analysis and visualization of high-throughput bisulfite sequencing data

http://biq-analyzer-ht.bioinf.mpi-inf.mpg.de

BiSeq DMR for RRBS data R/Bioconductor package BiSeq

BISMA Support analysis of repetitive sequences http://biochem.jacobs-university.de/BDPC/BISMA

BismarkProbably the most widely used three-letter bisulphite aligner; supports both Bowtie (fast, gap-free alignment) and Bowtie 2.0 (sensitive, gapped alignment)

http://www.bioinformatics.babraham.ac.uk/projects/bismark

Bis-SNPVariant caller for inferring DNA methylation levels and genomic variants from BS-Seq reads that have been aligned by other tools

http://epigenome.usc.edu/publicationdata/bissnp2011

Bisulfighter Using Last for mapping, HMM for DMR detection http://epigenome.cbrc.jp/bisulfighter

BRAT Highly configurable and well-documented three-letter BS-Seq aligner http://compbio.cs.ucr.edu/brat

BS-SeekerBS-Seeker 2

Three-letter BS-Seq aligner based on Bowtiehttp://pellegrini.mcdb.ucla.edu/BS_Seeker/BS_Seeker.html

BSMAP Probably the most widely used wild-card BS-Seq aligner http://code.google.com/p/bsmap

Bsmooth Mapping, quality control and DMR analysis pipeline http://rafalab.jhsph.edu/bsmooth

COHCAP Integration with gene expression data https://sourceforge.net/projects/cohcap/

CpG_MPs Methylation patterns of genomic regions http://202.97.205.78/CpG_MPs/

DMAP DMR for BS-Seq and RRBS datahttp://biochem.otago.ac.nz/research/databases-software/

DMR2+ DMR for array based data

DSS Bayesian hierarchical model to detect differentially methylated loci (DML) R/Bioconductor package DSS

Epidiff DMR detection http://bioinfo.hrbmu.edu.cn/epidiff

Bock, C. et al. Quantitative comparison of genome-wide DNA methylation mapping technologies. Nat Biotechnol 28, 1106-14 (2010).Krueger, F., Kreck, B., Franke, A. & Andrews, S.R. DNA methylome analysis using short bisulfite sequencing data. Nat Methods 9, 145-51 (2012).Tran, H., Porter, J., Sun, M.A., Xie, H. & Zhang, L. Objective and comprehensive evaluation of bisulfite short read mapping tools. Adv Bioinformatics 2014, 472045 (2014).http://omictools.com/bisulfite-seq/

Published Tools (cont.)

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GSNAP Wild-card BS-Seq aligner included in a widely used general-purpose alignment tool http://share.gene.com/gmap

GBSA Analysis pipeline for gene-centric or gene-independent focus http://ctrad-csi.nus.edu.sg/gbsa

FadE Mapping for Base and Color space http://code.google.com/p/fade

Kismeth Designed to be used with plants http://katahdin.mssm.edu/kismeth

LastRecent and well-validated wild-card BS aligner included in a general-purpose alignment tool

http://last.cbrc.jp

MethPipe Mapping, BS conversion rate, HMR, DMR pipeline http://smithlabresearch.org/software/methpipe

Methyl-MAPSMethyl-Analyzer

Base and color space data + post analysishttp://epigenomicspub.columbia.edu/methylanalyzer_data.html

MethylCoderThree-letter Bs-Seq aligner that can be used with either Bowtie (high speed) or GSNAP (high sensitivity)

https://github.com/brentp/methylcode

MethylExtract Detects variation http://bioinfo2.ugr.es/MethylExtract

MethylSig R package pipeline for BS-Seq and RRBS http://sartorlab.ccmb.med.umich.edu/software

MOABS DMR detection http://code.google.com/p/moabs

Pash Wild-card BS aligner included in a general-purpose alignment tool http://brl.bcm.tmc.edu/pash

RMAPRMAPBS

Wild-card BS aligner included in a general-purpose alignment toolhttp://www.cmb.usc.edu/people/andrewds/rmaphttp://smithlabresearch.org/software/methpipe

RRBSMAPVariant of BSMAP that is specialized on reduced-representation bisulphitesequencing (RRBS) data

http://rrbsmap.computational-epigenetics.org

SAAP-RRBS RRBS mappinghttp://ndc.mayo.edu/mayo/research/biostat/stand-alone-packages.cfm

segemehl Wild-card bisulphite aligner included in a general-purpose alignment tool http://www.bioinf.uni-leipzig.de/Software/segemehl

SOCS-B Robin-Karp hashin, color space data http://solidsoftwaretools.com/gf/project/socs

Bock, C. et al. Quantitative comparison of genome-wide DNA methylation mapping technologies. Nat Biotechnol 28, 1106-14 (2010).Krueger, F., Kreck, B., Franke, A. & Andrews, S.R. DNA methylome analysis using short bisulfite sequencing data. Nat Methods 9, 145-51 (2012).Tran, H., Porter, J., Sun, M.A., Xie, H. & Zhang, L. Objective and comprehensive evaluation of bisulfite short read mapping tools. Adv Bioinformatics 2014, 472045 (2014).http://omictools.com/bisulfite-seq/

How to Select BS-Seq Analysis Tools?

Actively update and good supports from authors or communities Aligner

BS-Seeker 2

Bismark

RMAPBS/RMAPBS-PE

Post-analysis tools

MethPipe

21

• Chatterjee, A., Stockwell, P.A., Rodger, E.J. & Morison, I.M. Comparison of alignment software for genome-wide bisulphite sequence data. Nucleic Acids Res 40, e79 (2012).

• Bismark• BSMAP• RMAPBS

• Tran, H., Porter, J., Sun, M.A., Xie, H. & Zhang, L. Objective and comprehensive evaluation of bisulfite short read mapping tools. Adv Bioinformatics 2014, 472045 (2014).

• BSMAP• BS-Seeker• Bismark• BRAT-BW• BiSS

• Kunde-Ramamoorthy, G. et al. Comparison and quantitative verification of mapping algorithms for whole-genome bisulfite sequencing. Nucleic Acids Res 42, e43 (2014)

• Bismark• BSMAP • Pash

Public BS-Seq Resources from MethBase and NCBI GEO

22

http://smithlabresearch.org/software/methbase/

Glycine max (Soy beans)Schistocerca gregaria (Locust)Rattus norvegicus (Rat)Danio rerio (Zebra fish)Drosophila melanogaster (Fruit fly)Oryza sativa (Rice)Macaca mulatta (Rhesus monkey)Mus musculus domesticus (Western Europen house mouse)Xenopus (Silurana) tropicalis (Frog)Cynoglossus semilaevis (Tongue sole, bony fish)Bombyx mori (Silkworm)Harpegnathos saltator (Jerdon's jumping ant)Camponotus floridanus (Florida carpenter ant)

A Case Study

BS-Seq of Female and Male Mouse Thalamus

23

Analysis Pipeline

24Allele-specific Methylated Regions

amrfinder allelicmeth

Differential Methylation Region

dmr

Large Hypo/Hyper-Methylation Domainspmd

Hypo/Hyper-Methylation Regionshmr hyperhmr pmr

Methylation Callingmethcounts + error correction

Bisulfite Conversion Ratebsrate

Remove Duplicate Readsduplicate-remover

Mappingrmapbs rmapbs-pe

Quality Trimmingfastq_masker

Cross-species Comparison of Methylomes

liftOver

Calculating Methylation Ratio for Regions

bigWigAverageOverBed roimethstat Bwtools

Generate Methylation BED file

Bedtools bedGraphToBigWig

fastx toolkit: http://hannonlab.cshl.edu/fastx_toolkit/

MethPipe: http://smithlabresearch.org/software/methpipe/

Bedtools: https://github.com/arq5x/bedtools2

Programs from UCSC Genome Browser: http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64

bwtool: https://github.com/CRG-Barcelona/bwtool/wiki

Whole Gnome BS-Seq of Male/Female Mouse Thalamus

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Sample namelibrary

size (bp)Total base

(Mbases)/ laneTotal Base

(Mb)/ sample# Reads

(PE read)Total reads

% of >= Q30 Bases (PF)

Mean Quality Score (PF)

seqcoverage

Male Thalamus 369 25,53790,889

255,372,054908,891,140

92.33 36.2 33.7Male Thalamus 337 28,165 281,645,674 92.42 36.12Male Thalamus 312 37,187 371,873,412 90.62 35.56

Female Thalamus 386 29,96584,106

299,653,436841,061,998

90.81 35.68 31.2Female Thalamus 362 31,305 313,048,784 91.53 35.82Female Thalamus 317 22,836 228,359,778 76.36 31.28

Sequencing Output

Sample Mapped ReadsAfter Remove

DuplicatesSequencing depth

(CpG), When > 0BS Conversion

Rate

Male Thalamus 770,958,854 655,643,506 26.5951 0.980197

Female Thalamus 698,661,676 630,472,842 27.1065 0.980664

Mapping Results

How to Correct Error Rates in BS-Seq Data?

• Error Rate: non-conversion rate + sequencing error (0.01% )• Higher non-conversion rate will produce more methylated CpG sites

• Using statistical test (binomial distribution test with FDR < 1%)• Lister, R. et al. Human DNA methylomes at base resolution show widespread epigenomic differences.

Nature 462, 315-22 (2009)

• http://sing.ei.uvigo.es/bicycle

• Filtering CpG sites with read depth

• A CpG site is unmethylated if its read depth < 5

26

Sex Sample BS Conversion Rate Error Rate

Male Thalamus 0.980197 0.019903

Female Thalamus 0.980664 0.019436

How to Compare Two or More BS-Seq Data?

27

chrom position strand context probabilityunmeth read

sample Ameth readsample A

unmeth readSample B

meth readsample B

meth. ratiosample A

meth. ratiosample B meth. diff.

p-value(two-sided)

p-value(left-sided)

p-value(right-sided)

chr1 3004854 + CpG 5.303E-03 8 18 15 7 0.3077 0.6818 -0.3741 1.943E-02 1.040E-02 9.982E-01

chr1 3005927 + CpG 1.087E-02 6 4 12 0 0.6000 1.0000 -0.4000 2.871E-02 2.871E-02 1.000E+00chr1 3006295 + CpG 5.954E-05 0 8 9 1 0.0000 0.9000 -0.9000 4.114E-04 2.057E-04 1.000E+00chr1 3006364 + CpG 9.966E-01 4 2 0 9 0.6667 0.0000 0.6667 1.099E-02 1.000E+00 1.099E-02chr1 3009412 + CpG 7.792E-05 23 33 42 13 0.4107 0.7636 -0.3529 2.250E-04 1.473E-04 1.000E+00

chr1 3011067 + CpG 9.922E-01 47 1 40 8 0.9792 0.8333 0.1458 3.053E-02 9.987E-01 1.526E-02chr1 3011800 + CpG 4.762E-03 0 3 5 0 0.0000 1.0000 -1.0000 1.786E-02 1.786E-02 1.000E+00chr1 3016267 + CpG 9.979E-01 12 4 0 5 0.7500 0.0000 0.7500 6.192E-03 1.000E+00 6.192E-03chr1 3021463 + CpG 1.260E-02 56 7 109 3 0.8889 0.9732 -0.0843 3.668E-02 2.677E-02 9.953E-01

chr1 3021653 + CpG 9.855E-01 87 0 102 6 1.0000 0.9444 0.0556 3.423E-02 1.000E+00 2.708E-02

1. Using Fisher's exact test to determine whether a CpG site in two BS-Seq data is differentially methylatedLister, R. et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462, 315-22 (2009).

chrom position sample1 sample2 sample3 sample4 sample5 sample6 sample7 sample8 sample9 sample10 sample11 sample12 Chi Sq

chr1 3264648 0.916 0.781 0.913 0.748 0.891 0.551 0.894 0.698 0.885 0.713 0.860 0.719 163.886

chr1 3265559 0.836 0.517 0.808 0.524 0.848 0.427 0.851 0.508 0.862 0.505 0.850 0.524 326.237

chr1 3604995 0.070 0.051 0.000 0.000 0.068 0.076 0.065 0.000 0.101 0.000 0.000 0.000 102.970

chr1 4242297 0.000 0.000 0.000 0.000 0.000 0.026 0.000 0.000 0.000 0.000 0.000 0.000 196.797

chr1 4322330 0.432 0.097 0.352 0.112 0.254 0.643 0.392 0.103 0.439 0.154 0.325 0.139 129.562

chr1 4431875 0.000 0.000 0.000 0.000 0.075 0.000 0.000 0.000 0.143 0.000 0.000 0.000 142.589

chr1 4597796 0.784 0.343 0.493 0.349 0.583 0.830 0.827 0.306 0.860 0.382 0.798 0.258 119.062

chr1 4916463 0.146 0.143 0.272 0.270 0.719 0.782 0.195 0.161 0.429 0.305 0.208 0.298 107.710

chr1 4916643 0.272 0.438 0.157 0.288 0.711 0.737 0.183 0.197 0.208 0.214 0.257 0.266 129.678

chr1 5011040 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.333 0.162 0.000 0.150 107.753

2. Using Chi square to find sample-specific methylated CpG siteHon, G.C. et al. Epigenetic memory at embryonic enhancers identified in DNA methylation maps from adult mouse tissues. Nat Genet 45, 1198-206 (2013).

Methylation Status of Gene Structure

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Methylation Female Male % of diff. meth CpG Female vs Male

0% 143809 141905 100% 6533

5% 13157 14411 95% 178

10% 14189 15190 90% 333

15% 15268 16186 85% 290

20% 19380 19468 80% 537

25% 26789 26047 75% 1437

30% 38455 36165 70% 8037

35% 38599 38593 65% 232

40% 51006 51116 60% 1817

45% 53893 56669 55% 134

50% 112061 105476 50% 126906

55% 99034 98250 45% 1295

60% 134921 132829 40% 7599

65% 196008 188988 35% 99828

70% 267239 257493 30% 4314

75% 406567 391491 25% 69793

80% 606620 588995 20% 71484

85% 835161 835741 15% 32022

90% 982156 1023248 10% 11372

95% 431721 472753 5% 548

100% 304113 290657 0% 4272664

Divide mouse chromosomes into 200bp bins: ~4,8,000,000/13,628,844 have CpG sites

CGI, protein_coding

nCGI, protein_coding

0.2

0.4

0.6

0.8

0.60

0.65

0.70

0.75

0.80

0 20 40Gene Structure (-10K, Gene Body: 1 ~ 100%, +10K)

Cp

G M

eth

yla

tio

n R

atio

(x1

00

%)

sample

thalamus-f

thalamus-m

-10K TSS 40% 80% 100% 10K

-10K +10KTSS TxEnd50bins 50bins 50bins

Gene body

CGI, protein_coding

nCGI, protein_coding

0.950

0.955

0.960

0.965

0.970

0.950

0.955

0.960

0.965

0.970

0 20 40Gene Structure (-10K, Gene Body: 1 ~ 100%, +10K)

Sim

iarity

of C

pG

Meth

. S

tatu

s (

x100%

)

samplethalamus-f vsthalamus-m

-10K TSS 40% 80% 100% 10K

Differential Methylated Region between Female and Male Murine Thalamus Genomes

Differential Methylated Region (DMR)1. Merge two adjacent differential methylated

CpG sites with distance ≤ 200 bp

2. Keep merged regions with > 3 CpG sites and > 70% of CpG sites are differential methylated

3. Merge two regions with distance ≤ 500 bp

29

ChromLower meth.

in femaleLower meth.

in male Mosaic DMR

chr1 290 130 20chr2 276 127 28chr3 157 74 9chr4 236 112 23chr5 228 151 22chr6 137 82 12chr7 226 94 23chr8 189 105 21chr9 175 106 13

chr10 169 88 15chr11 265 143 22chr12 149 88 14chr13 138 75 12chr14 108 63 11chr15 140 87 15chr16 73 51 8chr17 151 79 15chr18 99 53 8chr19 102 64 12chrX 939 89 4

0

500

1000

1500

2000

2500

3000

3500

4000

4500

# of Gene Structure Overlapped with DMR

DMR may span across multiple gene structures

Acknowledgement

陽明大學生化暨分子生物研究所徐明達教授 Aim for the Top University Plan from the Ministry of Education, Taiwan

NSC102-2319-B010-001 高通量基因體分析核心設施(III)

NSC101-2319-B010-003-B4 高通量基因體分析核心設施(II)-核心設施服務計畫C1-1

NSC100-2319-B010-001 高通量基因體分析核心設施(I)

NSC99-3112-B010-020 高通量基因體分析核心設施(III)

NSC98-3112-B010-011 高通量基因體分析核心設施(II)

Mouse Brain Tissues 陽明大學生命科學系暨基因體研究所蔡亭芬教授

台北醫學大學轉譯醫學博士學位學程陳怡帆助理教授

Computing Resource National Research Program for Biopharmaceuticals (NRPB, NSC 1022325-B-492-001)

National Center for High-performance Computing (NCHC) of National Applied Research Laboratories (NARLabs)

30

Experience Sharing: Methylation Sequencing Analysis

DNA Methylation

Bisulfite Sequencing Technologies

BS-Seq Data Analysis

Tools for BS-Seq Data Analysis

A case study

Questions?

This slide is available in http://www.slideshare.net/YiFengChang

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