Differential RNA methylation analysis for MeRIP-seq data under general experimental design
Year: 2022
Type: article
Abstract: MOTIVATION: RNA epigenetics is an emerging field to study the post-transcriptional gene regulation. The dynamics of RNA epigenetic modification have been reported to associate with many human diseases. Recently developed high-throughput technology named Methylated RNA Immunoprecipitation Sequencing (MeRIP-seq) enables the transcriptome-wide profiling of N6-methyladenosine (m6A) modification and comparison of RNA epigenetic modifications. There are a few computational methods for the comparison of mRNA modifications under different conditions but they all suffer from serious limitations. RESULTS: In this work, we develop a novel statistical method to detect differentially methylated mRNA regions from MeRIP-seq data. We model the sequence count data by a hierarchical negative binomial model that accounts for various sources of variations and derive parameter estimation and statistical testing procedures for flexible statistical inferences under general experimental designs. Extensive benchmark evaluations in simulation and real data analyses demonstrate that our method is more accurate, robust and flexible compared to existing methods. AVAILABILITY AND IMPLEMENTATION: Our method TRESS is implemented as an R/Bioconductor package and is available at https://bioconductor.org/packages/devel/TRESS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Source: Bioinformatics
Authors: Zhenxing Guo, Andrew M Shafik, Peng Jin, Hao Wu
Institution: Emory University
Language: English
Topic: RNA modifications and cancer
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W4294667050