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dc.contributor.authorSaito, Yutaka
dc.contributor.authorTsuji, Junko
dc.contributor.authorMituyama, Toutai
dc.date2022-08-11T08:09:43.000
dc.date.accessioned2022-08-23T16:40:58Z
dc.date.available2022-08-23T16:40:58Z
dc.date.issued2014-04-01
dc.date.submitted2015-09-09
dc.identifier.citationNucleic Acids Res. 2014 Apr;42(6):e45. doi: 10.1093/nar/gkt1373. Epub 2014 Jan 13. <a href="http://dx.doi.org/10.1093/nar/gkt1373">Link to article on publisher's site</a>.
dc.identifier.issn0305-1048 (Linking)
dc.identifier.doi10.1093/nar/gkt1373
dc.identifier.pmid24423865
dc.identifier.urihttp://hdl.handle.net/20.500.14038/39786
dc.description.abstractAnalysis of bisulfite sequencing data usually requires two tasks: to call methylated cytosines (mCs) in a sample, and to detect differentially methylated regions (DMRs) between paired samples. Although numerous tools have been proposed for mC calling, methods for DMR detection have been largely limited. Here, we present Bisulfighter, a new software package for detecting mCs and DMRs from bisulfite sequencing data. Bisulfighter combines the LAST alignment tool for mC calling, and a novel framework for DMR detection based on hidden Markov models (HMMs). Unlike previous attempts that depend on empirical parameters, Bisulfighter can use the expectation-maximization algorithm for HMMs to adjust parameters for each data set. We conduct extensive experiments in which accuracy of mC calling and DMR detection is evaluated on simulated data with various mC contexts, read qualities, sequencing depths and DMR lengths, as well as on real data from a wide range of biological processes. We demonstrate that Bisulfighter consistently achieves better accuracy than other published tools, providing greater sensitivity for mCs with fewer false positives, more precise estimates of mC levels, more exact locations of DMRs and better agreement of DMRs with gene expression and DNase I hypersensitivity. The source code is available at http://epigenome.cbrc.jp/bisulfighter.
dc.language.isoen_US
dc.relation<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=24423865&dopt=Abstract">Link to Article in PubMed</a>
dc.rightsCopyright The Author(s) 2014. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com.
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/
dc.subjectCytosine
dc.subject*DNA Methylation
dc.subjectSequence Alignment
dc.subjectSequence Analysis, DNA
dc.subject*Software
dc.subjectSulfites
dc.subjectBioinformatics
dc.subjectComputational Biology
dc.titleBisulfighter: accurate detection of methylated cytosines and differentially methylated regions
dc.typeJournal Article
dc.source.journaltitleNucleic acids research
dc.source.volume42
dc.source.issue6
dc.identifier.legacyfulltexthttps://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=3587&amp;context=oapubs&amp;unstamped=1
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/oapubs/2583
dc.identifier.contextkey7573162
refterms.dateFOA2022-08-23T16:40:58Z
html.description.abstract<p>Analysis of bisulfite sequencing data usually requires two tasks: to call methylated cytosines (mCs) in a sample, and to detect differentially methylated regions (DMRs) between paired samples. Although numerous tools have been proposed for mC calling, methods for DMR detection have been largely limited. Here, we present Bisulfighter, a new software package for detecting mCs and DMRs from bisulfite sequencing data. Bisulfighter combines the LAST alignment tool for mC calling, and a novel framework for DMR detection based on hidden Markov models (HMMs). Unlike previous attempts that depend on empirical parameters, Bisulfighter can use the expectation-maximization algorithm for HMMs to adjust parameters for each data set. We conduct extensive experiments in which accuracy of mC calling and DMR detection is evaluated on simulated data with various mC contexts, read qualities, sequencing depths and DMR lengths, as well as on real data from a wide range of biological processes. We demonstrate that Bisulfighter consistently achieves better accuracy than other published tools, providing greater sensitivity for mCs with fewer false positives, more precise estimates of mC levels, more exact locations of DMRs and better agreement of DMRs with gene expression and DNase I hypersensitivity. The source code is available at http://epigenome.cbrc.jp/bisulfighter.</p>
dc.identifier.submissionpathoapubs/2583
dc.contributor.departmentProgram in Bioinformatics and Integrative Biology
dc.source.pagese45


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Copyright The Author(s) 2014. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com.
Except where otherwise noted, this item's license is described as Copyright The Author(s) 2014. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com.