{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T11:06:10Z","timestamp":1763809570742},"reference-count":21,"publisher":"Oxford University Press (OUP)","issue":"17","license":[{"start":{"date-parts":[[2016,11,10]],"date-time":"2016-11-10T00:00:00Z","timestamp":1478736000000},"content-version":"vor","delay-in-days":181,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation : One of the main goals of large scale methylation studies is to detect differentially methylated loci. One way is to approach this problem sitewise, i.e. to find differentially methylated positions (DMPs). However, it has been shown that methylation is regulated in longer genomic regions. So it is more desirable to identify differentially methylated regions (DMRs) instead of DMPs. The new high coverage arrays, like Illuminas 450k platform, make it possible at a reasonable cost. Few tools exist for DMR identification from this type of data, but there is no standard approach.<\/jats:p>\n               <jats:p>Results : We propose a novel method for DMR identification that detects the region boundaries according to the minimum description length (MDL) principle, essentially solving the problem of model selection. The significance of the regions is established using linear mixed models. Using both simulated and large publicly available methylation datasets, we compare seqlm performance to alternative approaches. We demonstrate that it is both more sensitive and specific than competing methods. This is achieved with minimal parameter tuning and, surprisingly, quickest running time of all the tried methods. Finally, we show that the regional differential methylation patterns identified on sparse array data are confirmed by higher resolution sequencing approaches.<\/jats:p>\n               <jats:p>Availability and Implementation : The methods have been implemented in R package seqlm that is available through Github: https:\/\/github.com\/raivokolde\/seqlm<\/jats:p>\n               <jats:p>Contact: \u00a0rkolde@gmail.com<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btw304","type":"journal-article","created":{"date-parts":[[2016,5,14]],"date-time":"2016-05-14T02:25:40Z","timestamp":1463192740000},"page":"2604-2610","source":"Crossref","is-referenced-by-count":35,"title":["seqlm: an MDL based method for identifying differentially methylated regions in high density methylation array data"],"prefix":"10.1093","volume":"32","author":[{"given":"Raivo","family":"Kolde","sequence":"first","affiliation":[{"name":"1 Institute of Computer Science, University of Tartu, 50409 Tartu, Estonia"},{"name":"2 Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA 02114, USA"}]},{"given":"Kaspar","family":"M\u00e4rtens","sequence":"additional","affiliation":[{"name":"1 Institute of Computer Science, University of Tartu, 50409 Tartu, Estonia"}]},{"given":"Kaie","family":"Lokk","sequence":"additional","affiliation":[{"name":"3 Institute of Molecular and Cell Biology, University of Tartu, 51010 Tartu, Estonia"}]},{"given":"Sven","family":"Laur","sequence":"additional","affiliation":[{"name":"1 Institute of Computer Science, University of Tartu, 50409 Tartu, Estonia"}]},{"given":"Jaak","family":"Vilo","sequence":"additional","affiliation":[{"name":"1 Institute of Computer Science, University of Tartu, 50409 Tartu, Estonia"}]}],"member":"286","published-online":{"date-parts":[[2016,5,13]]},"reference":[{"key":"2023020112593420200_btw304-B1","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1093\/bioinformatics\/btu049","article-title":"Minfi: a flexible and comprehensive bioconductor package for the analysis of infinium DNA methylation microarrays","volume":"30","author":"Aryee","year":"2014","journal-title":"Bioinformatics"},{"key":"2023020112593420200_btw304-B2","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1038\/nrc3130","article-title":"A decade of exploring the cancer epigenomebiological and translational implications","volume":"11","author":"Baylin","year":"2011","journal-title":"Nat. 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