A Bayesian hierarchical model for analyzing methylated RNA immunoprecipitation sequencing data.

ABSTRACT:

Background

The recently emerged technology of methylated RNA immunoprecipitation sequencing (MeRIP-seq) sheds light on the study of RNA epigenetics. This new bioinformatics question calls for effective and robust peaking calling algorithms to detect mRNA methylation sites from MeRIP-seq data.

Methods

We propose a Bayesian hierarchical model to detect methylation sites from MeRIP-seq data. Our modeling approach includes several important characteristics. First, it models the zero-inflated and over-dispersed counts by deploying a zero-inflated negative binomial model. Second, it incorporates a hidden Markov model (HMM) to account for the spatial dependency of neighboring read enrichment. Third, our Bayesian inference allows the proposed model to borrow strength in parameter es

SUBMITTER: Zhang M 

PROVIDER: S-EPMC8026011 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature