Journal
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Volume 15, Issue 2, Pages 526-534Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2015.2403355
Keywords
N6-Methyladenosine (m(6)A); beta-binomial modeling; differential RNA methylation; MeTDiff
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Funding
- National Institutes of Health [R01GM113245, NIH-NCIP30CA54174, 5 U54 CA113001]
- US National Science Foundation [CCF-1246073]
- William and Ella Medical Research Foundation grant
- Thrive Well Foundation
- Max and Minnie Tomerlin Voelcker Fund
- 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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