A novel algorithm for calling mRNA m6A peaks by modeling biological variances in MeRIP-seq data.
N(6)-methyl-adenosine (m(6)A) is the most prevalent mRNA methylation but precise prediction of its mRNA location is important for understanding its function. A recent sequencing technology, known as Methylated RNA Immunoprecipitation Sequencing technology (MeRIP-seq), has been developed for transcriptome-wide profiling of m(6)A. We previously developed a peak calling algorithm called exomePeak. However, exomePeak over-simplifies data characteristics and ignores the reads' variances among replicates or reads dependency across a site region. To further improve the performance, new model is needed to address these important issues of MeRIP-seq data.



We propose a novel, graphical model-based peak calling method, MeTPeak, for transcriptome-wide detection of m(6)A sites from MeRIP-seq data. MeTPeak explicitly models read count of an m(6)A site and introduces a hierarchical layer of Beta variables to capture the variances and a Hidden Markov model to characterize the reads dependency across a site. In addition, we developed a constrained Newton's method and designed a log-barrier function to compute analytically intractable, positively constrained Beta parameters. We applied our algorithm to simulated and real biological datasets and demonstrated significant improvement in detection performance and robustness over exomePeak. Prediction results on publicly available MeRIP-seq datasets are also validated and shown to be able to recapitulate the known patterns of m(6)A, further validating the improved performance of MeTPeak.



The package 'MeTPeak' is implemented in R and C ++, and additional details are available at https://github.com/compgenomics/MeTPeak



yufei.huang@utsa.edu or xdchoi@gmail.com



Supplementary data are available at Bioinformatics online.
Department of Electrical and Computer Engineering, University of Texas at San Antonio, TX 78249, USA.
Xiaodong
X
Cui
Department of Biological Science, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China.
Jia
J
Meng
College of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
Shaowu
S
Zhang
Greehey Children's Cancer Research Institute Department of Epidemiology and Biostatistics, University of Texas Health Science Center at San Antonio, TX 78229, USA.
Yidong
Y
Chen
Department of Electrical and Computer Engineering, University of Texas at San Antonio, TX 78249, USA Department of Epidemiology and Biostatistics, University of Texas Health Science Center at San Antonio, TX 78229, USA.
Yufei
Y
Huang
D000465:Algorithms; D001483:Base Sequence; D008745:Methylation; D008954:Models, Biological; D012333:RNA, Messenger; D017423:Sequence Analysis, RNA
D012333:RNA, Messenger