Calling differentially methylated regions from whole genome bisulphite sequencing with DMRcate
Abstract Whole genome bisulphite sequencing (WGBS) permits the genome-wide study of single molecule methylation patterns. One of the key goals of mammalian cell-type identity studies, in both normal differentiation and disease, is to locate differential methylation patterns across the genome. We dis...
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2021
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Online Access: | http://dx.doi.org/10.1093/nar/gkab637 http://academic.oup.com/nar/article-pdf/49/19/e109/41071508/gkab637.pdf |
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croxfordunivpr:10.1093/nar/gkab637 2024-09-15T18:03:49+00:00 Calling differentially methylated regions from whole genome bisulphite sequencing with DMRcate Peters, Timothy J Buckley, Michael J Chen, Yunshun Smyth, Gordon K Goodnow, Christopher C Clark, Susan J National Health and Medical Research Council NHMRC Bill & Patricia Ritchie Foundation 2021 http://dx.doi.org/10.1093/nar/gkab637 http://academic.oup.com/nar/article-pdf/49/19/e109/41071508/gkab637.pdf en eng Oxford University Press (OUP) https://creativecommons.org/licenses/by/4.0/ Nucleic Acids Research volume 49, issue 19, page e109-e109 ISSN 0305-1048 1362-4962 journal-article 2021 croxfordunivpr https://doi.org/10.1093/nar/gkab637 2024-09-03T04:13:08Z Abstract Whole genome bisulphite sequencing (WGBS) permits the genome-wide study of single molecule methylation patterns. One of the key goals of mammalian cell-type identity studies, in both normal differentiation and disease, is to locate differential methylation patterns across the genome. We discuss the most desirable characteristics for DML (differentially methylated locus) and DMR (differentially methylated region) detection tools in a genome-wide context and choose a set of statistical methods that fully or partially satisfy these considerations to compare for benchmarking. Our data simulation strategy is both biologically informed—employing distribution parameters derived from large-scale consortium datasets—and thorough. We report DML detection ability with respect to coverage, group methylation difference, sample size, variability and covariate size, both marginally and jointly, and exhaustively with respect to parameter combination. We also benchmark these methods on FDR control and computational time. We use this result to backend and introduce an expanded version of DMRcate: an existing DMR detection tool for microarray data that we have extended to now call DMRs from WGBS data. We compare DMRcate to a set of alternative DMR callers using a similarly realistic simulation strategy. We find DMRcate and RADmeth are the best predictors of DMRs, and conclusively find DMRcate the fastest. Article in Journal/Newspaper DML Oxford University Press Nucleic Acids Research 49 19 e109 e109 |
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Oxford University Press |
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croxfordunivpr |
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English |
description |
Abstract Whole genome bisulphite sequencing (WGBS) permits the genome-wide study of single molecule methylation patterns. One of the key goals of mammalian cell-type identity studies, in both normal differentiation and disease, is to locate differential methylation patterns across the genome. We discuss the most desirable characteristics for DML (differentially methylated locus) and DMR (differentially methylated region) detection tools in a genome-wide context and choose a set of statistical methods that fully or partially satisfy these considerations to compare for benchmarking. Our data simulation strategy is both biologically informed—employing distribution parameters derived from large-scale consortium datasets—and thorough. We report DML detection ability with respect to coverage, group methylation difference, sample size, variability and covariate size, both marginally and jointly, and exhaustively with respect to parameter combination. We also benchmark these methods on FDR control and computational time. We use this result to backend and introduce an expanded version of DMRcate: an existing DMR detection tool for microarray data that we have extended to now call DMRs from WGBS data. We compare DMRcate to a set of alternative DMR callers using a similarly realistic simulation strategy. We find DMRcate and RADmeth are the best predictors of DMRs, and conclusively find DMRcate the fastest. |
author2 |
National Health and Medical Research Council NHMRC Bill & Patricia Ritchie Foundation |
format |
Article in Journal/Newspaper |
author |
Peters, Timothy J Buckley, Michael J Chen, Yunshun Smyth, Gordon K Goodnow, Christopher C Clark, Susan J |
spellingShingle |
Peters, Timothy J Buckley, Michael J Chen, Yunshun Smyth, Gordon K Goodnow, Christopher C Clark, Susan J Calling differentially methylated regions from whole genome bisulphite sequencing with DMRcate |
author_facet |
Peters, Timothy J Buckley, Michael J Chen, Yunshun Smyth, Gordon K Goodnow, Christopher C Clark, Susan J |
author_sort |
Peters, Timothy J |
title |
Calling differentially methylated regions from whole genome bisulphite sequencing with DMRcate |
title_short |
Calling differentially methylated regions from whole genome bisulphite sequencing with DMRcate |
title_full |
Calling differentially methylated regions from whole genome bisulphite sequencing with DMRcate |
title_fullStr |
Calling differentially methylated regions from whole genome bisulphite sequencing with DMRcate |
title_full_unstemmed |
Calling differentially methylated regions from whole genome bisulphite sequencing with DMRcate |
title_sort |
calling differentially methylated regions from whole genome bisulphite sequencing with dmrcate |
publisher |
Oxford University Press (OUP) |
publishDate |
2021 |
url |
http://dx.doi.org/10.1093/nar/gkab637 http://academic.oup.com/nar/article-pdf/49/19/e109/41071508/gkab637.pdf |
genre |
DML |
genre_facet |
DML |
op_source |
Nucleic Acids Research volume 49, issue 19, page e109-e109 ISSN 0305-1048 1362-4962 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1093/nar/gkab637 |
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Nucleic Acids Research |
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49 |
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19 |
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e109 |
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e109 |
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1810441282705686528 |