A novel algorithm for calling mRNA m<SUP>6</SUP>A peaks by modeling biological variances in MeRIP-seq data

Cui, Xiaodong, Meng, Jia ORCID: 0000-0003-3455-205X, Zhang, Shaowu, Chen, Yidong and Huang, Yufei (2016) A novel algorithm for calling mRNA m<SUP>6</SUP>A peaks by modeling biological variances in MeRIP-seq data. BIOINFORMATICS, 32 (12). pp. 378-385.

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Official URL: http://dx.doi.org/10.1093/bioinformatics/btw281

Abstract

<h4>Motivation</h4>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.<h4>Results</h4>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.<h4>Availability and implementation</h4>The package 'MeTPeak' is implemented in R and C ++, and additional details are available at https://github.com/compgenomics/MeTPeak<h4>Contact</h4>yufei.huang@utsa.edu or xdchoi@gmail.com<h4>Supplementary information</h4>Supplementary data are available at Bioinformatics online.

Item Type:	Article
Uncontrolled Keywords:	RNA, Messenger, Sequence Analysis, RNA, Base Sequence, Methylation, Algorithms, Models, Biological
Depositing User:	Symplectic Admin
Date Deposited:	06 Feb 2019 11:25
Last Modified:	17 Oct 2023 03:10
DOI:	10.1093/bioinformatics/btw281
Open Access URL:	https://doi.org/10.1093/bioinformatics/btw281
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3032308