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

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 15; no. 2; pp. 526 - 534
• Main Authors: Cui, Xiaodong, Zhang, Lin, Meng, Jia, Rao, Manjeet K., Chen, Yidong, Huang, Yufei
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2018
• Subjects: Adenosine - analogs & derivatives; Adenosine - chemistry; Adenosine - genetics; Adenosine - metabolism; Algorithms; beta-binomial modeling; Bioinformatics; Cell Line, Tumor; Computational Biology; Computational modeling; Databases, Genetic; differential RNA methylation; Humans; IEEE transactions; IP networks; MeTDiff; Methylation; N6-Methyladenosine (m<sup xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">6 A); RNA - analysis; RNA - chemistry; Robustness; Sequence Analysis, RNA - methods; Sequential analysis; Software
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2015.2403355

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.