Statistical methods for detecting differentially methylated regions based on MethylCap-seq data

Deepak Nag Ayyala, David E. Frankhouser, Javkhlan Ochir Ganbat, Guido Marcucci, Ralf Bundschuh, Pearlly Yan, Shili Lin

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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 highdimensional mean vector tests for detection of differentially methylated regions (DMRs) and compare and contrast such tests with other methods for detecting DMRs. Comprehensive simulation studies are conducted to highlight the performance of these tests under different settings. Based on our observation, we make recommendations on the optimal test to use. We illustrate the superiority of mean vector tests in detecting cancer-related canonical gene pathways, which are significantly enriched for acute myeloid leukemia and ovarian cancer.

Original languageEnglish (US)
Pages (from-to)926-937
Number of pages12
JournalBriefings in Bioinformatics
Issue number6
StatePublished - 2016
Externally publishedYes


  • Differentially methylated regions
  • High dimensionality
  • Mean vector test
  • MethylCap-seq

ASJC Scopus subject areas

  • Information Systems
  • Molecular Biology


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