HEPeak: an HMM-based exome peak-finding package for RNA epigenome sequencing data

Area of Science:

• RNA epigenomics
• Bioinformatics
• Computational biology

Background:

• Methylated RNA Immunoprecipitation combined with RNA sequencing (MeRIP-seq) offers high-resolution study of RNA epigenomics.
• MeRIP-seq generates complex bioinformatics challenges requiring advanced statistical solutions for methyltranscriptome analysis.

Purpose of the Study:

• To develop a novel computational algorithm for accurate identification and characterization of transcriptome-wide RNA methylation sites.
• To improve upon existing peak-calling methods for MeRIP-seq data analysis.

Main Methods:

• Development of HEPeak, a Hidden Markov Model (HMM)-based algorithm for Exome Peak-finding.
• HEPeak models correlations within m6A peak regions, enabling rigorous statistical inference.
• Evaluation using simulated and real MeRIP-seq datasets from human and mouse cells.

Main Results:

• HEPeak demonstrated higher sensitivity and specificity compared to the previous exomePeak algorithm on simulated data.
• The algorithm successfully recapitulated known m6A distributions in transcripts.
• Novel m6A sites were identified in long non-coding RNAs using HEPeak.

Conclusions:

• HEPeak is a novel HMM-based peak-calling algorithm designed for MeRIP-seq data.
• The algorithm provides a statistically rigorous approach to methyltranscriptome analysis.
• HEPeak is publicly available in R for broader research application.