Calling differentially methylated regions from whole genome bisulphite sequencing with DMRcate
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...
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Online Access: | http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8565305/ http://www.ncbi.nlm.nih.gov/pubmed/34320181 https://doi.org/10.1093/nar/gkab637 |
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ftpubmed:oai:pubmedcentral.nih.gov:8565305 2023-05-15T16:01:40+02:00 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 2021-07-28 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8565305/ http://www.ncbi.nlm.nih.gov/pubmed/34320181 https://doi.org/10.1093/nar/gkab637 en eng Oxford University Press http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8565305/ http://www.ncbi.nlm.nih.gov/pubmed/34320181 http://dx.doi.org/10.1093/nar/gkab637 © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. CC-BY Nucleic Acids Res Methods Online Text 2021 ftpubmed https://doi.org/10.1093/nar/gkab637 2021-11-07T01:59:24Z 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. Text DML PubMed Central (PMC) Nucleic Acids Research 49 19 e109 e109 |
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Methods Online 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 |
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Methods Online |
description |
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. |
format |
Text |
author |
Peters, Timothy J Buckley, Michael J Chen, Yunshun Smyth, Gordon K Goodnow, Christopher C Clark, Susan J |
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 |
publishDate |
2021 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8565305/ http://www.ncbi.nlm.nih.gov/pubmed/34320181 https://doi.org/10.1093/nar/gkab637 |
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DML |
genre_facet |
DML |
op_source |
Nucleic Acids Res |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8565305/ http://www.ncbi.nlm.nih.gov/pubmed/34320181 http://dx.doi.org/10.1093/nar/gkab637 |
op_rights |
© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
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CC-BY |
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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