Differential RNA methylation analysis for MeRIP-seq data under general experimental design.

Area of Science:

• RNA epigenetics and post-transcriptional gene regulation.
• The role of RNA modifications in human diseases.

Background:

• RNA epigenetic modifications, particularly N6-methyladenosine (m6A), are crucial for gene regulation and disease association.
• High-throughput Methylated RNA Immunoprecipitation Sequencing (MeRIP-seq) allows transcriptome-wide profiling of m6A.
• Existing computational methods for comparing mRNA modifications have significant limitations.

Purpose of the Study:

• To develop a novel statistical method for detecting differentially methylated mRNA regions using MeRIP-seq data.
• To provide a robust and flexible computational tool for RNA epigenetic modification analysis.

Main Methods:

• Development of a hierarchical negative binomial model to analyze MeRIP-seq count data.
• Incorporation of parameter estimation and statistical testing for flexible inferences.
• Modeling various sources of variation in the data.

Main Results:

• The novel method, TRESS, demonstrates superior accuracy and robustness compared to existing approaches.
• Benchmark evaluations using simulations and real data confirm TRESS's performance.
• TRESS offers greater flexibility for statistical inferences in various experimental designs.

Conclusions:

• TRESS provides an accurate and robust computational solution for analyzing MeRIP-seq data.
• The developed method enhances the ability to compare RNA epigenetic modifications across different conditions.
• TRESS is available as an R/Bioconductor package, facilitating its use in the research community.