biostudies-literatureAyyala DNNIGMS NIH HHS926-937https://www.ebi.ac.uk/biostudies/studies/S-EPMC5142008biostudies-literatureUnknown17(6)DNA methylation is a well-established epigenetic mark, whose pattern throughout the genome, especially in the promoter or CpG islands, may be modified in a cell at a disease stage. Recently developed probabilistic approaches allow distributing methylation signals at nucleotide resolution from MethylCap-seq data. Standard statistical methods for detecting differential methylation suffer from 'curse of dimensionality' and sparsity in signals, resulting in high false-positive rates. Strong correlation of signals between CG sites also yields spurious results. In this article, we review applicability of high-dimensional mean vector tests for detection of differentially methylated regions (DMRs) and compare and contrast such tests with other methods for detecting DMRs. Comprehensive simulation sBriefings in bioinformaticsStatistical methods for detecting differentially methylated regions based on MethylCap-seq data.PMC5142008T32 GM068412Ayyala DNFrankhouser DEBundschuh RLin SGanbat JOYan PMarcucci GfalseStatistical methods for detecting differentially methylated regions based on MethylCap-seq data.DNA methylation is a well-established epigenetic mark, whose pattern throughout the genome, especially in the promoter or CpG islands, may be modified in a cell at a disease stage. Recently developed probabilistic approaches allow distributing methylation signals at nucleotide resolution from MethylCap-seq data. Standard statistical methods for detecting differential methylation suffer from 'curse of dimensionality' and sparsity in signals, resulting in high false-positive rates. Strong correlation of signals between CG sites also yields spurious results. In this article, we review applicability of high-dimensional mean vector tests for detection of differentially methylated regions (DMRs) and compare and contrast such tests with other methods for detecting DMRs. Comprehensive simulation s2016-01-01T00:00:00Z2016 Nov2025-04-21T20:19:52.467Z2019-03-27T02:30:57ZS-EPMC51420082645409510.1093/bib/bbv089