{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:55Z","timestamp":1772138095956,"version":"3.50.1"},"reference-count":35,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2019,8,9]],"date-time":"2019-08-09T00:00:00Z","timestamp":1565308800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100007225","name":"Ministry of Science and Technology","doi-asserted-by":"publisher","award":["106-2221-E-400-005-MY3"],"award-info":[{"award-number":["106-2221-E-400-005-MY3"]}],"id":[{"id":"10.13039\/100007225","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>DNA methylation plays an important role in regulating gene expression. DNA methylation is commonly analyzed using bisulfite sequencing (BS-seq)-based designs, such as whole-genome bisulfite sequencing (WGBS), reduced representation bisulfite sequencing (RRBS) and oxidative bisulfite sequencing (oxBS-seq). Furthermore, there has been growing interest in investigating the roles that genetic variants play in changing the methylation levels (i.e. methylation quantitative trait loci or meQTLs), how methylation regulates the imprinting of gene expression (i.e. allele-specific methylation or ASM) and the differentially methylated regions (DMRs) among different cell types. However, none of the current simulation tools can generate different BS-seq data types (e.g. WGBS, RRBS and oxBS-seq) while modeling meQTLs, ASM and DMRs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We developed profile-based whole-genome bisulfite sequencing data simulator (pWGBSSimla), a profile-based bisulfite sequencing data simulator, which simulates WGBS, RRBS and oxBS-seq data for different cell types based on real data. meQTLs and ASM are modeled based on the block structures of the methylation status at CpGs, whereas the simulation of DMRs is based on observations of methylation rates in real data. We demonstrated that pWGBSSimla adequately simulates data and allows performance comparisons among different methylation analysis methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>pWGBSSimla is available at https:\/\/omicssimla.sourceforge.io.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz635","type":"journal-article","created":{"date-parts":[[2019,8,8]],"date-time":"2019-08-08T07:35:30Z","timestamp":1565249730000},"page":"660-665","source":"Crossref","is-referenced-by-count":3,"title":["pWGBSSimla: a profile-based whole-genome bisulfite sequencing data simulator incorporating methylation QTLs, allele-specific methylations and differentially methylated regions"],"prefix":"10.1093","volume":"36","author":[{"given":"Ren-Hua","family":"Chung","sequence":"first","affiliation":[{"name":"Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes , Zhunan 350, Taiwan"}]},{"given":"Chen-Yu","family":"Kang","sequence":"additional","affiliation":[{"name":"Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes , Zhunan 350, Taiwan"}]}],"member":"286","published-online":{"date-parts":[[2019,8,9]]},"reference":[{"key":"2023013110025889400_btz635-B1","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1186\/s13059-016-0911-6","article-title":"A probabilistic generative model for quantification of DNA modifications enables analysis of demethylation pathways","volume":"17","author":"Aijo","year":"2016","journal-title":"Genome Biol"},{"key":"2023013110025889400_btz635-B2","doi-asserted-by":"crossref","first-page":"i511","DOI":"10.1093\/bioinformatics\/btw468","article-title":"LuxGLM: a probabilistic covariate model for quantification of DNA methylation modifications with complex experimental designs","volume":"32","author":"Aijo","year":"2016","journal-title":"Bioinformatics"},{"key":"2023013110025889400_btz635-B3","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1038\/nmeth.3115","article-title":"Comprehensive analysis of DNA methylation data with RnBeads","volume":"11","author":"Assenov","year":"2014","journal-title":"Nat. Methods"},{"key":"2023013110025889400_btz635-B4","doi-asserted-by":"crossref","first-page":"e1004663","DOI":"10.1371\/journal.pgen.1004663","article-title":"Methylation QTLs are associated with coordinated changes in transcription factor binding, histone modifications, and gene expression levels","volume":"10","author":"Banovich","year":"2014","journal-title":"PLoS Genet"},{"key":"2023013110025889400_btz635-B5","doi-asserted-by":"crossref","first-page":"1841","DOI":"10.1038\/nprot.2013.115","article-title":"Oxidative bisulfite sequencing of 5-methylcytosine and 5-hydroxymethylcytosine","volume":"8","author":"Booth","year":"2013","journal-title":"Nat. Protocols"},{"key":"2023013110025889400_btz635-B6","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.jhydrol.2007.06.035","article-title":"Efficient stochastic generation of multi-site synthetic precipitation data","volume":"345","author":"Brissette","year":"2007","journal-title":"J. Hydrol"},{"key":"2023013110025889400_btz635-B7","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1093\/bioinformatics\/btt674","article-title":"A classification approach for DNA methylation profiling with bisulfite next-generation sequencing data","volume":"30","author":"Cheng","year":"2014","journal-title":"Bioinformatics"},{"key":"2023013110025889400_btz635-B8","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1002\/gepi.21850","article-title":"SeqSIMLA2: simulating correlated quantitative traits accounting for shared environmental effects in user-specified pedigree structure","volume":"39","author":"Chung","year":"2015","journal-title":"Genet. Epidemiol"},{"key":"2023013110025889400_btz635-B9","doi-asserted-by":"crossref","first-page":"215.","DOI":"10.1186\/1471-2105-15-215","article-title":"Using beta-binomial regression for high-precision differential methylation analysis in multifactor whole-genome bisulfite sequencing experiments","volume":"15","author":"Dolzhenko","year":"2014","journal-title":"BMC Bioinformatics"},{"key":"2023013110025889400_btz635-B10","doi-asserted-by":"crossref","first-page":"e69.","DOI":"10.1093\/nar\/gku154","article-title":"A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data","volume":"42","author":"Feng","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2023013110025889400_btz635-B11","doi-asserted-by":"crossref","first-page":"e100.","DOI":"10.1093\/nar\/gks275","article-title":"A mostly traditional approach improves alignment of bisulfite-converted DNA","volume":"40","author":"Frith","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2023013110025889400_btz635-B12","doi-asserted-by":"crossref","first-page":"R83.","DOI":"10.1186\/gb-2012-13-10-r83","article-title":"BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions","volume":"13","author":"Hansen","year":"2012","journal-title":"Genome Biol"},{"key":"2023013110025889400_btz635-B13","doi-asserted-by":"crossref","first-page":"13567","DOI":"10.1038\/s41598-018-31886-5","article-title":"Identification of differentially methylated region (DMR) networks associated with progression of nonalcoholic fatty liver disease","volume":"8","author":"Hotta","year":"2018","journal-title":"Sci. Rep"},{"key":"2023013110025889400_btz635-B14","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1093\/biostatistics\/kxr013","article-title":"Significance analysis and statistical dissection of variably methylated regions","volume":"13","author":"Jaffe","year":"2012","journal-title":"Biostatistics"},{"key":"2023013110025889400_btz635-B15","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1101\/gr.196394.115","article-title":"metilene: fast and sensitive calling of differentially methylated regions from bisulfite sequencing data","volume":"26","author":"Juhling","year":"2016","journal-title":"Genome Res"},{"key":"2023013110025889400_btz635-B16","doi-asserted-by":"crossref","first-page":"e1005650.","DOI":"10.1371\/journal.pgen.1005650","article-title":"A flexible, efficient binomial mixed model for identifying differential DNA methylation in bisulfite sequencing data","volume":"11","author":"Lea","year":"2015","journal-title":"PLoS Genet"},{"key":"2023013110025889400_btz635-B17","doi-asserted-by":"crossref","first-page":"1730","DOI":"10.1101\/gr.211854.116","article-title":"Whole-genome analysis of the methylome and hydroxymethylome in normal and malignant lung and liver","volume":"26","author":"Li","year":"2016","journal-title":"Genome Res"},{"key":"2023013110025889400_btz635-B18","doi-asserted-by":"crossref","first-page":"578","DOI":"10.4161\/epi.5.7.12960","article-title":"Allele-specific methylation in the human genome: implications for genetic studies of complex disease","volume":"5","author":"Meaburn","year":"2010","journal-title":"Epigenetics"},{"key":"2023013110025889400_btz635-B19","doi-asserted-by":"crossref","first-page":"766","DOI":"10.1038\/nature07107","article-title":"Genome-scale DNA methylation maps of pluripotent and differentiated cells","volume":"454","author":"Meissner","year":"2008","journal-title":"Nature"},{"key":"2023013110025889400_btz635-B20","doi-asserted-by":"crossref","first-page":"10.","DOI":"10.1186\/1756-8935-6-10","article-title":"5-hydroxymethylcytosine and its potential roles in development and cancer","volume":"6","author":"Pfeifer","year":"2013","journal-title":"Epigenet. Chromatin"},{"key":"2023013110025889400_btz635-B21","doi-asserted-by":"crossref","first-page":"2645","DOI":"10.1093\/bioinformatics\/btt459","article-title":"MLML: consistent simultaneous estimates of DNA methylation and hydroxymethylation","volume":"29","author":"Qu","year":"2013","journal-title":"Bioinformatics"},{"key":"2023013110025889400_btz635-B22","doi-asserted-by":"crossref","first-page":"2371","DOI":"10.1093\/bioinformatics\/btv114","article-title":"WGBSSuite: simulating whole-genome bisulphite sequencing data and benchmarking differential DNA methylation analysis tools","volume":"31","author":"Rackham","year":"2015","journal-title":"Bioinformatics"},{"key":"2023013110025889400_btz635-B23","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1534\/genetics.116.195008","article-title":"A Bayesian approach for analysis of whole-genome bisulfite sequencing data identifies disease-associated changes in DNA methylation","volume":"205","author":"Rackham","year":"2017","journal-title":"Genetics"},{"key":"2023013110025889400_btz635-B24","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1038\/s41525-017-0007-6","article-title":"Base resolution maps reveal the importance of 5-hydroxymethylcytosine in a human glioblastoma","volume":"2","author":"Raiber","year":"2017","journal-title":"NPJ Genom. Med"},{"key":"2023013110025889400_btz635-B25","doi-asserted-by":"crossref","first-page":"e97349.","DOI":"10.1371\/journal.pone.0097349","article-title":"A note on exact differences between beta distributions in genomic (Methylation) studies","volume":"9","author":"Raineri","year":"2014","journal-title":"PLoS One"},{"key":"2023013110025889400_btz635-B26","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1101\/gr.104695.109","article-title":"Allele-specific methylation is prevalent and is contributed by CpG-SNPs in the human genome","volume":"20","author":"Shoemaker","year":"2010","journal-title":"Genome Res"},{"key":"2023013110025889400_btz635-B27","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1186\/1471-2164-15-145","article-title":"Methylation quantitative trait loci (meQTLs) are consistently detected across ancestry, developmental stage, and tissue type","volume":"15","author":"Smith","year":"2014","journal-title":"BMC Genomics"},{"key":"2023013110025889400_btz635-B28","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1186\/s12864-017-4091-x","article-title":"5-hydroxymethylcytosine is highly dynamic across human fetal brain development","volume":"18","author":"Spiers","year":"2017","journal-title":"BMC Genomics"},{"key":"2023013110025889400_btz635-B29","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1007\/978-1-4939-7774-1_17","article-title":"Approaches for the analysis and interpretation of whole genome bisulfite sequencing data","volume":"1767","author":"Stuart","year":"2018","journal-title":"Methods Mol. Biol"},{"key":"2023013110025889400_btz635-B30","doi-asserted-by":"crossref","first-page":"e4","DOI":"10.1093\/nar\/gks829","article-title":"CpG_MPs: identification of CpG methylation patterns of genomic regions from high-throughput bisulfite sequencing data","volume":"41","author":"Su","year":"2013","journal-title":"Nucleic Acids Res"},{"key":"2023013110025889400_btz635-B31","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1101\/gr.147942.112","article-title":"Dynamic DNA methylation across diverse human cell lines and tissues","volume":"23","author":"Varley","year":"2013","journal-title":"Genome Res"},{"key":"2023013110025889400_btz635-B32","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1038\/ng1990","article-title":"Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome","volume":"39","author":"Weber","year":"2007","journal-title":"Nat. Genet"},{"key":"2023013110025889400_btz635-B33","first-page":"e141","article-title":"Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates","volume":"43","author":"Wu","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2023013110025889400_btz635-B34","doi-asserted-by":"crossref","first-page":"3667","DOI":"10.1093\/bioinformatics\/btw527","article-title":"oxBS-MLE: an efficient method to estimate 5-methylcytosine and 5-hydroxymethylcytosine in paired bisulfite and oxidative bisulfite treated DNA","volume":"32","author":"Xu","year":"2016","journal-title":"Bioinformatics"},{"key":"2023013110025889400_btz635-B35","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1038\/nature12433","article-title":"Charting a dynamic DNA methylation landscape of the human genome","volume":"500","author":"Ziller","year":"2013","journal-title":"Nature"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btz635\/29548917\/btz635.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/36\/3\/660\/48982620\/bioinformatics_36_3_660.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/36\/3\/660\/48982620\/bioinformatics_36_3_660.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,18]],"date-time":"2023-09-18T18:56:28Z","timestamp":1695063388000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/36\/3\/660\/5545541"}},"subtitle":[],"editor":[{"given":"Bonnie","family":"Berger","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2019,8,9]]},"references-count":35,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,2,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btz635","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/390633","asserted-by":"object"}]},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2020,2,1]]},"published":{"date-parts":[[2019,8,9]]}}}