6,766
Views
0
CrossRef citations to date
0
Altmetric
Review

Base Resolution Methylome Profiling: Considerations in Platform Selection, Data Preprocessing and Analysis

, , &
Pages 813-828 | Published online: 14 Sep 2015

References

  • Azuara D , Rodriguez-MorantaF , de OcaJet al. Novel methylation panel for the early detection of colorectal tumors in stool DNA . Clin. Colorectal Cancer9 ( 3 ), 168 – 176 ( 2010 ).
  • Hong L , AhujaN . DNA methylation biomarkers of stool and blood for early detection of colon cancer . Genet. Test. Mol. Biomarkers17 ( 5 ), 401 – 406 ( 2013 ).
  • Imperiale TF , RansohoffDF , ItzkowitzSHet al. Multitarget stool DNA testing for colorectal-cancer screening . N. Engl. J. Med.370 ( 14 ), 1287 – 1297 ( 2014 ).
  • Shiovitz S , BertagnolliMM , RenfroLAet al. CpG island methylator phenotype is associated with response to adjuvant irinotecan-based therapy for stage iii colon cancer . Gastroenterology147 ( 3 ), 637 – 645 ( 2014 ).
  • Fleischhacker M , DietrichD , LiebenbergV , FieldJK , SchmidtB . The role of DNA methylation as biomarkers in the clinical management of lung cancer . Expert Rev. Respir. Med.7 ( 4 ), 363 – 383 ( 2013 ).
  • Bock C , TomazouEM , BrinkmanABet al. Quantitative comparison of genome-wide DNA methylation mapping technologies . Nat. Biotechnol.28 ( 10 ), 1106 – 1114 ( 2010 ).
  • Ruike Y , ImanakaY , SatoF , ShimizuK , TsujimotoG . Genome-wide analysis of aberrant methylation in human breast cancer cells using methyl-DNA immunoprecipitation combined with high-throughput sequencing . BMC Genomics11 , 137 ( 2010 ).
  • Aberg KA , McclayJL , NerellaSet al. Mbd-seq as a cost-effective approach for methylome-wide association studies: demonstration in 1500 case–control samples . Epigenomics4 ( 6 ), 605 – 621 ( 2012 ).
  • Serre D , LeeBH , TingAH . Mbd-isolated genome sequencing provides a high-throughput and comprehensive survey of DNA methylation in the human genome . Nucleic Acids Res.38 ( 2 ), 391 – 399 ( 2010 ).
  • Harris RA , WangT , CoarfaCet al. Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications . Nat. Biotechnol.28 ( 10 ), 1097 – 1105 ( 2010 ).
  • Wang L , SunJ , WuHet al. Systematic assessment of reduced representation bisulfite sequencing to human blood samples: a promising method for large-sample-scale epigenomic studies . J. Biotechnol.157 ( 1 ), 1 – 6 ( 2012 ).
  • Dedeurwaerder S , DefranceM , CalonneE , DenisH , SotiriouC , FuksF . Evaluation of the infinium methylation 450k technology . Epigenomics3 ( 6 ), 771 – 784 ( 2011 ).
  • Sandoval J , HeynH , MoranSet al. Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome . Epigenetics6 ( 6 ), 692 – 702 ( 2011 ).
  • Sun Z , WuY , OrdogTet al. Aberrant signature methylome by DNMT1 hot spot mutation in hereditary sensory and autonomic neuropathy 1E . Epigenetics9 ( 8 ), 1184 – 1193 ( 2014 ).
  • Gu H , SmithZD , BockC , BoyleP , GnirkeA , MeissnerA . Preparation of reduced representation bisulfite sequencing libraries for genome-scale DNA methylation profiling . Nat. Protoc.6 ( 4 ), 468 – 481 ( 2011 ).
  • Agilent Technologies: SureSelect Methyl-Seq . www.genomics.agilent.com/article.jsp?pageId=3038&_requestid=542909 .
  • NimbleGen . www.nimblegen.com/products/seqcap/epi-system/cpgiant/ .
  • Walker DL , BhagwateAV , BahetiSet al. DNA methylation profiling: comparison of genome-wide sequencing methods and the Infinium Human Methylation 450 Bead Chip . Epigenomics doi:10.2217/EPI.15.64 ( 2015 ) ( Epub ahead of print ).
  • Bibikova M , BarnesB , TsanCet al. High density DNA methylation array with single CpG site resolution . Genomics98 ( 4 ), 288 – 295 ( 2011 ).
  • Wang D , YanL , HuQet al. Ima: an R package for high-throughput analysis of illumina’s 450k infinium methylation data . Bioinformatics28 ( 5 ), 729 – 730 ( 2012 ).
  • Touleimat N , TostJ . Complete pipeline for infinium® human methylation 450k beadchip data processing using subset quantile normalization for accurate DNA methylation estimation . Epigenomics4 ( 3 ), 325 – 341 ( 2012 ).
  • Maksimovic J , GordonL , OshlackA . Swan: subset-quantile within array normalization for Illumina Infinium HumanMethylation450 Beadchips . Genome Biol.13 ( 6 ), R44 ( 2012 ).
  • Aryee MJ , JaffeAE , Corrada-BravoHet al. Minfi: a flexible and comprehensive bioconductor package for the analysis of Infinium DNA methylation microarrays . Bioinformatics30 ( 10 ), 1363 – 1369 ( 2014 ).
  • Teschendorff AE , MarabitaF , LechnerMet al. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data . Bioinformatics29 ( 2 ), 189 – 196 ( 2013 ).
  • Davis S DP , BilkeS , TricheTJr , BootwallaM . Methylumi: handle Illumina methylation data . R package version 2.12.0 ( 2014 ).
  • Du P , KibbeWA , LinSM . Lumi: a pipeline for processing Illumina microarray . Bioinformatics24 ( 13 ), 1547 – 1548 ( 2008 ).
  • Pidsley R , CcYW , VoltaM , LunnonK , MillJ , SchalkwykLC . A data-driven approach to preprocessing Illumina 450k Methylation array data . BMC Genomics14 , 293 ( 2013 ).
  • Marabita F , AlmgrenM , LindholmMEet al. An evaluation of analysis pipelines for DNA methylation profiling using the Illumina HumanMethylation450 beadchip platform . Epigenetics8 ( 3 ), 333 – 346 ( 2013 ).
  • Sun Z , ChaiHS , WuYet al. Batch effect correction for genome-wide methylation data with Illumina Infinium platform . BMC Med. Genomics4 , 84 ( 2011 ).
  • Wu MC , JoubertBR , KuanPFet al. A systematic assessment of normalization approaches for the Infinium 450k Methylation platform . Epigenetics9 ( 2 ), 318 – 329 ( 2014 ).
  • Dedeurwaerder S , DefranceM , BizetM , CalonneE , BontempiG , FuksF . A comprehensive overview of Infinium HumanMethylation450 data processing . Brief. Bioinform.15 ( 6 ), 929 – 941 ( 2014 ).
  • Johnson WE , LiC , RabinovicA . Adjusting batch effects in microarray expression data using empirical bayes methods . Biostatistics8 ( 1 ), 118 – 127 ( 2007 ).
  • Huss M . Introduction into the analysis of high-throughput-sequencing based epigenome data . Brief. Bioinform.11 ( 5 ), 512 – 523 ( 2010 ).
  • Reinders J , PaszkowskiJ . Bisulfite methylation profiling of large genomes . Epigenomics2 ( 2 ), 209 – 220 ( 2010 ).
  • Krueger F , KreckB , FrankeA , AndrewsSR . DNA methylome analysis using short bisulfite sequencing data . Nat. Methods9 ( 2 ), 145 – 151 ( 2012 ).
  • Krueger F , AndrewsSR . Bismark: a flexible aligner and methylation caller for bisulfite-seq applications . Bioinformatics27 ( 11 ), 1571 – 1572 ( 2011 ).
  • Guo W , FizievP , YanWet al. BS-Seeker2: a versatile aligning pipeline for bisulfite sequencing data . BMC Genomics14 , 774 ( 2013 ).
  • Harris EY , PontsN , Le RochKG , LonardiS . Brat-bw: efficient and accurate mapping of bisulfite-treated reads . Bioinformatics28 ( 13 ), 1795 – 1796 ( 2012 ).
  • Kielbasa SM , WanR , SatoK , HortonP , FrithMC . Adaptive seeds tame genomic sequence comparison . Genome Res.21 ( 3 ), 487 – 493 ( 2011 ).
  • Pedersen B , HsiehTF , IbarraC , FischerRL . Methylcoder: software pipeline for bisulfite-treated sequences . Bioinformatics27 ( 17 ), 2435 – 2436 ( 2011 ).
  • Xi Y , LiW . BSMAP: whole-genome bisulfite sequence mapping program . BMC Bioinformatics10 , 232 ( 2009 ).
  • Xi Y , BockC , MullerF , SunD , MeissnerA , LiW . RRBSMAP: a fast, accurate and user-friendly alignment tool for reduced representation bisulfite sequencing . Bioinformatics28 ( 3 ), 430 – 432 ( 2012 ).
  • Babraham Bioinformatics: Trim Galore! www.bioinformatics.babraham.ac.uk/projects/trim_galore/ .
  • Mayo Clinic: SAAP-RRBS . http://bioinformaticstools.mayo.edu/research/saap-rrbs/ .
  • Lin X , SunD , RodriguezBet al. BSeQC: quality control of bisulfite sequencing experiments . Bioinformatics29 ( 24 ), 3227 – 3229 ( 2013 ).
  • Wilhelm-Benartzi CS , KoestlerDC , KaragasMRet al. Review of processing and analysis methods for DNA methylation array data . Br. J. Cancer109 ( 6 ), 1394 – 1402 ( 2013 ).
  • Babraham Bioinformatics: FastQC . www.bioinformatics.babraham.ac.uk/projects/fastqc/ .
  • Sun Z , BahetiS , MiddhaSet al. SAAP-RRBS: streamlined analysis and annotation pipeline for reduced representation bisulfite sequencing . Bioinformatics28 ( 16 ), 2180 – 2181 ( 2012 ).
  • Sun S , NoviskiA , YuX . MethyQA: a pipeline for bisulfite-treated methylation sequencing quality assessment . BMC Bioinformatics14 , 259 ( 2013 ).
  • Wang X , LairdPW , HinoueT , GroshenS , SiegmundKD . Non-specific filtering of beta-distributed data . BMC Bioinformatics15 ( 1 ), 199 ( 2014 ).
  • Du P , ZhangX , HuangCCet al. Comparison of beta-value and m-value methods for quantifying methylation levels by microarray analysis . BMC Bioinformatics11 , 587 ( 2010 ).
  • Zhuang J , WidschwendterM , TeschendorffAE . A comparison of feature selection and classification methods in DNA methylation studies using the illumina infinium platform . BMC Bioinformatics13 , 59 ( 2012 ).
  • Smyth GK . Linear models and empirical bayes methods for assessing differential expression in microarray experiments . Stat. Appl. Genet. Mol. Biol.3 , Article 3 ( 2004 ).
  • Barfield RT , KilaruV , SmithAK , ConneelyKN . CpGassoc: an R function for analysis of DNA methylation microarray data . Bioinformatics28 ( 9 ), 1280 – 1281 ( 2012 ).
  • Warden CD , LeeH , TompkinsJDet al. COHCAP: an integrative genomic pipeline for single-nucleotide resolution DNA methylation analysis . Nucleic Acids Res.41 ( 11 ), e117 ( 2013 ).
  • Saadati M , BennerA . Statistical challenges of high-dimensional methylation data . Stat. Med.33 ( 30 ), 5347 – 5357 ( 2014 ).
  • Dolzhenko E , SmithAD . Using beta-binomial regression for high-precision differential methylation analysis in multifactor whole-genome bisulfite sequencing experiments . BMC Bioinformatics15 ( 1 ), 215 ( 2014 ).
  • Lister R , PelizzolaM , DowenRHet al. Human DNA methylomes at base resolution show widespread epigenomic differences . Nature462 ( 7271 ), 315 – 322 ( 2009 ).
  • Li Y , ZhuJ , TianGet al. The DNA methylome of human peripheral blood mononuclear cells . PLoS Biol.8 ( 11 ), e1000533 ( 2010 ).
  • Challen GA , SunD , JeongMet al. Dnmt3a is essential for hematopoietic stem cell differentiation . Nat. Genet.44 ( 1 ), 23 – 31 ( 2012 ).
  • Akalin A , KormakssonM , LiSet al. Methylkit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles . Genome Biol.13 ( 10 ), R87 ( 2012 ).
  • Li S , Garrett-BakelmanFE , AkalinAet al. An optimized algorithm for detecting and annotating regional differential methylation . BMC Bioinformatics14 ( Suppl. 5 ), S10 ( 2013 ).
  • Xu H , PodolskyRH , RyuDet al. A method to detect differentially methylated loci with next-generation sequencing . Genet. Epidemiol.37 ( 4 ), 377 – 382 ( 2013 ).
  • Park Y , FigueroaME , RozekLS , SartorMA . MethylSig: a whole-genome DNA methylation analysis pipeline . Bioinformatics30 ( 17 ), 2414 – 2422 ( 2014 ).
  • Sun D , XiY , RodriguezBet al. MOABS: model based analysis of bisulfite sequencing data . Genome Biol.15 ( 2 ), R38 ( 2014 ).
  • Hansen KD , LangmeadB , IrizarryRA . BSmooth: from whole-genome bisulfite sequencing reads to differentially methylated regions . Genome Biol.13 ( 10 ), R83 ( 2012 ).
  • Hebestreit K , DugasM , KleinHU . Detection of significantly differentially methylated regions in targeted bisulfite sequencing data . Bioinformatics29 ( 13 ), 1647 – 1653 ( 2013 ).
  • Jaffe AE , MurakamiP , LeeHet al. Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies . Int. J. Epidemiol.41 ( 1 ), 200 – 209 ( 2012 ).
  • Saito Y , TsujiJ , MituyamaT . Bisulfighter: accurate detection of methylated cytosines and differentially methylated regions . Nucleic Acids Res.42 ( 6 ), e45 ( 2014 ).
  • Song Q , DecatoB , HongEEet al. A reference methylome database and analysis pipeline to facilitate integrative and comparative epigenomics . PLoS ONE8 ( 12 ), e81148 ( 2013 ).
  • Pedersen BS , SchwartzDA , YangIV , KechrisKJ . Comb-p: software for combining, analyzing, grouping and correcting spatially correlated p-values . Bioinformatics28 ( 22 ), 2986 – 2988 ( 2012 ).
  • Feber A , GuilhamonP , LechnerMet al. Using high-density DNA methylation arrays to profile copy number alterations . Genome Biol.15 ( 2 ), R30 ( 2014 ).
  • Barturen G , RuedaA , OliverJL , HackenbergM . MethylExtract: high-quality methylation maps and SNV calling from whole-genome bisulfite sequencing data . F1000Res2 , 217 ( 2013 ).
  • Liu Y , SiegmundKD , LairdPW , BermanBP . Bis-snp: combined DNA methylation and SNP calling for bisulfite-seq data . Genome Biol.13 ( 7 ), R61 ( 2012 ).
  • Fang F , HodgesE , MolaroA , DeanM , HannonGJ , SmithAD . Genomic landscape of human allele-specific DNA methylation . Proc. Natl Acad. Sci. USA109 ( 19 ), 7332 – 7337 ( 2012 ).
  • Mosen-Ansorena D , TelleriaN , VeganzonesS , De La OrdenV , MaestroML , AransayAM . SeqCNA: an R package for DNA copy number analysis in cancer using high-throughput sequencing . BMC Genomics15 , 178 ( 2014 ).
  • Chan KC , JiangP , ChanCWet al. Noninvasive detection of cancer-associated genome-wide hypomethylation and copy number aberrations by plasma DNA bisulfite sequencing . Proc. Natl Acad. Sci. USA110 ( 47 ), 18761 – 18768 ( 2013 ).
  • Guo JU , SuY , ShinJHet al. Distribution, recognition and regulation of non-CpG methylation in the adult mammalian brain . Nat. Neurosci.17 ( 2 ), 215 – 222 ( 2014 ).
  • Pinney SE . Mammalian non-CpG methylation: stem cells and beyond . Biology (Basel)3 ( 4 ), 739 – 751 ( 2014 ).
  • Price ME , CottonAM , LamLLet al. Additional annotation enhances potential for biologically-relevant analysis of the Illumina Infinium HumanMethylation450 beadchip array . Epigenetics Chromatin6 ( 1 ), 4 ( 2013 ).
  • Wang J , TangJ , LaiM , ZhangH . 5-hydroxymethylcytosine and disease . Mutat. Res. Rev. Mutat. Res.762 , 167 – 175 ( 2014 ).
  • Yu M , HonGC , SzulwachKEet al. Tet-assisted bisulfite sequencing of 5-hydroxymethylcytosine . Nat. Protoc.7 ( 12 ), 2159 – 2170 ( 2012 ).
  • Booth MJ , OstTW , BeraldiDet al. Oxidative bisulfite sequencing of 5-methylcytosine and 5-hydroxymethylcytosine . Nat. Protoc.8 ( 10 ), 1841 – 1851 ( 2013 ).
  • Harper KN , PetersBA , GambleMV . Batch effects and pathway analysis: two potential perils in cancer studies involving DNA methylation array analysis . Cancer Epidemiol. Biomarkers Prev.22 ( 6 ), 1052 – 1060 ( 2013 ).
  • Buhule OD , MinsterRL , HawleyNLet al. Stratified randomization controls better for batch effects in 450k methylation analysis: a cautionary tale . Front. Genet.5 , 354 ( 2014 ).
  • Hovestadt V , RemkeM , KoolMet al. Robust molecular subgrouping and copy-number profiling of medulloblastoma from small amounts of archival tumour material using high-density DNA methylation arrays . Acta Neuropathol.125 ( 6 ), 913 – 916 ( 2013 ).
  • Moran S , VizosoM , Martinez-CardusAet al. Validation of DNA methylation profiling in formalin-fixed paraffin-embedded samples using the Infinium HumanMethylation450 microarray . Epigenetics9 ( 6 ), 829 – 833 ( 2014 ).
  • Dumenil TD , WocknerLF , BettingtonMet al. Genome-wide DNA methylation analysis of formalin-fixed paraffin embedded colorectal cancer tissue . Genes Chromosomes Cancer53 ( 7 ), 537 – 548 ( 2014 ).
  • Wang J , XiaY , LiLet al. Double restriction-enzyme digestion improves the coverage and accuracy of genome-wide CpG methylation profiling by reduced representation bisulfite sequencing . BMC Genomics14 , 11 ( 2013 ).