TY - JOUR
T1 - MeTDiff
T2 - A Novel Differential RNA Methylation Analysis for MeRIP-Seq Data
AU - Cui, Xiaodong
AU - Zhang, Lin
AU - Meng, Jia
AU - Rao, Manjeet K.
AU - Chen, Yidong
AU - Huang, Yufei
N1 - Funding Information:
The authors acknowledge the funding support from National Institutes of Health (R01GM113245; NIH-NCIP30CA54174, and 5 U54 CA113001); US National Science Foundation (CCF-1246073); The William and Ella Medical Research Foundation grant, Thrive Well Foundation and The Max and Minnie Tomerlin Voelcker Fund. We thank the computational support from the UTSA Computational System Biology Core, funded by the National Institute on Minority Health and Health Disparities (G12MD007591) from the National Institutes of Health. Xiaodong Cui is the corresponding author.
Publisher Copyright:
© 2004-2012 IEEE.
PY - 2018/3/1
Y1 - 2018/3/1
N2 - 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: https://github.com/compgenomics/MeTDiff.
AB - 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: https://github.com/compgenomics/MeTDiff.
KW - MeTDiff
KW - N6-Methyladenosine (mA)
KW - beta-binomial modeling
KW - differential RNA methylation
UR - https://www.scopus.com/pages/publications/85044918393
U2 - 10.1109/TCBB.2015.2403355
DO - 10.1109/TCBB.2015.2403355
M3 - Article
C2 - 29610101
AN - SCOPUS:85044918393
SN - 1545-5963
VL - 15
SP - 526
EP - 534
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics
IS - 2
ER -