TY - JOUR
T1 - A novel algorithm for calling mRNA m6A peaks by modeling biological variances in MeRIP-seq data
AU - Cui, Xiaodong
AU - Meng, Jia
AU - Zhang, Shaowu
AU - Chen, Yidong
AU - Huang, Yufei
N1 - Publisher Copyright:
© 2016 The Author 2016. Published by Oxford University Press.
PY - 2016/6/15
Y1 - 2016/6/15
N2 - Motivation: N6-methyl-adenosine (m6A) 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 m6A. 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. Results: We propose a novel, graphical model-based peak calling method, MeTPeak, for transcriptome-wide detection of m6A sites from MeRIP-seq data. MeTPeak explicitly models read count of an m6A 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 m6A, further validating the improved performance of MeTPeak.
AB - Motivation: N6-methyl-adenosine (m6A) 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 m6A. 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. Results: We propose a novel, graphical model-based peak calling method, MeTPeak, for transcriptome-wide detection of m6A sites from MeRIP-seq data. MeTPeak explicitly models read count of an m6A 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 m6A, further validating the improved performance of MeTPeak.
UR - http://www.scopus.com/inward/record.url?scp=84976467345&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btw281
DO - 10.1093/bioinformatics/btw281
M3 - Article
C2 - 27307641
AN - SCOPUS:84976467345
SN - 1367-4803
VL - 32
SP - i378-i385
JO - Bioinformatics
JF - Bioinformatics
IS - 12
ER -