4.7 Article

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

Publisher

IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2015.2403355

Keywords

N6-Methyladenosine (m(6)A); beta-binomial modeling; differential RNA methylation; MeTDiff

Funding

  1. National Institutes of Health [R01GM113245, NIH-NCIP30CA54174, 5 U54 CA113001]
  2. US National Science Foundation [CCF-1246073]
  3. William and Ella Medical Research Foundation grant
  4. Thrive Well Foundation
  5. Max and Minnie Tomerlin Voelcker Fund
  6. National Institute on Minority Health and Health Disparities from the National Institutes of Health [G12MD007591]

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N6-Methyladenosine (m(6)A) 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: https://github.com/compgenomics/MeTDiff

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