A novel algorithm for calling mRNA m6A peaks by modeling biological variances in MeRIP-seq data

Xiaodong Cui, Jia Meng, Shaowu Zhang, Yidong Chen, Yufei Huang

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)i378-i385
JournalBioinformatics
Volume32
Issue number12
DOIs
StatePublished - Jun 15 2016

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Fingerprint Dive into the research topics of 'A novel algorithm for calling mRNA m<sup>6</sup>A peaks by modeling biological variances in MeRIP-seq data'. Together they form a unique fingerprint.

Cite this