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

被引：72

作者：

Cui, Xiaodong ^{[1
]}

Zhang, Lin ^{[2
]}

Meng, Jia ^{[3
]}

Rao, Manjeet K. ^{[4
]}

Chen, Yidong ^{[4
]}

Huang, Yufei ^{[1
]}

机构：

[1] Univ Texas San Antonio, Dept Elect Engn, San Antonio, TX 78249 USA

[2] China Univ Min & Technol, Dept Informat Engn, Xuzhou 221116, Jiangsu, Peoples R China

[3] Xian Jiaotong Liverpool Univ, Dept Biol Sci, Suzhou 215123, Jiangsu, Peoples R China

[4] Greehey Childrens Canc Res Inst, San Antonio, TX 78229 USA

来源：

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS | 2018年 / 15卷 / 02期

基金：

美国国家科学基金会; 美国国家卫生研究院;

关键词：

N6-Methyladenosine (m(6)A); beta-binomial modeling; differential RNA methylation; MeTDiff; DNA METHYLATION; NUCLEAR-RNA; CHIP; N-6-METHYLADENOSINE; ENRICHMENT; M(6)A-SEQ; DISCOVERY; REVEALS; CELLS; FTO;

D O I：

10.1109/TCBB.2015.2403355

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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

引用

页码：526 / 534

页数：9

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