Title

Differential RNA methylation using multivariate statistical methods.

Document Type

Article

Publication Date

9-29-2021

Publication Title

Briefings in bioinformatics

Keywords

JGM

JAX Source

Brief Bioinform 2021 Sep 29: bbab309

ISSN

1477-4054

PMID

34586372

DOI

https://doi.org/10.1093/bib/bbab309

Abstract

MOTIVATION: m6A methylation is a highly prevalent post-transcriptional modification in eukaryotes. MeRIP-seq or m6A-seq, which comprises immunoprecipitation of methylation fragments , is the most common method for measuring methylation signals. Existing computational tools for analyzing MeRIP-seq data sets and identifying differentially methylated genes/regions are not most optimal. They either ignore the sparsity or dependence structure of the methylation signals within a gene/region. Modeling the methylation signals using univariate distributions could also lead to high type I error rates and low sensitivity. In this paper, we propose using mean vector testing (MVT) procedures for testing differential methylation of RNA at the gene level. MVTs use a distribution-free test statistic with proven ability to control type I error even for extremely small sample sizes. We performed a comprehensive simulation study comparing the MVTs to existing MeRIP-seq data analysis tools. Comparative analysis of existing MeRIP-seq data sets is presented to illustrate the advantage of using MVTs.

RESULTS: Mean vector testing procedures are observed to control type I error rate and achieve high power for detecting differential RNA methylation using m6A-seq data. Results from two data sets indicate that the genes detected identified as having different m6A methylation patterns have high functional relevance to the study conditions.

AVAILABILITY: The dimer software package for differential RNA methylation analysis is freely available at https://github.com/ouyang-lab/DIMER.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Briefings in Bioinformatics online.

Share

COinS