MeTDiff: A Novel Differential RNA Methylation Analysis for MeRIP-Seq Data

Xiaodong Cui, Lin Zhang, Jia Meng, Manjeet K. Rao, Yidong Chen, Yufei Huang

Research output: Contribution to journalArticlepeer-review

53 Scopus citations


N6-Methyladenosine (m6A) transcriptome methylation is an exciting new research area that just captures the attention of research community. We present in this paper, MeTDiff, a novel computational tool for predicting differential m6A methylation sites from Methylated RNA immunoprecipitation sequencing (MeRIP-Seq) data. Compared with the existing algorithm exomePeak, the advantages of MeTDiff are that it explicitly models the reads variation in data and also devices a more power likelihood ratio test for differential methylation site prediction. Comprehensive evaluation of MeTDiff's performance using both simulated and real datasets showed that MeTDiff is much more robust and achieved much higher sensitivity and specificity over exomePeak. The R package 'MeTDiff' and additional details are available at:

Original languageEnglish (US)
Pages (from-to)526-534
Number of pages9
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number2
StatePublished - Mar 1 2018


  • MeTDiff
  • N6-Methyladenosine (mA)
  • beta-binomial modeling
  • differential RNA methylation

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics


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