An HMM-based Exome Peak-finding package for RNA epigenome sequencing data

Summary form only given. Methylated RNA Immunoprecipatation combined with RNA sequencing (MeRIP-Seq), first developed in two recent studies, is revolutionizing the de novo study of RNA epigenome at a higher resolution. However, this new technology poses unique bioinformatics problems that call for novel and sophisticated statistical computational solutions. Here, we introduce HEP, a Hidden Markov Model (HMM)-based Exome Peak-finding algorithm for predicting transcriptome methylation sites in MeRIP- Seq data. In contrast to ExomPeak, our previously developed MeRIP-Seq analysis software package, HEP is a model-based approach, which enables rigorous statistical inference. To demonstrate the utility of HEP, it was evaluated both on a simulated data set and a real MeRIP-Seq data for m6A methylation. HEP demonstrates a higher sensitivity and specificity in both the simulation test and the real m6A data. In addition, the peaks were further confirmed by biological enrichment and sequence motifs.