Differential RNA methylation using multivariate statistical methods

Deepak Nag Ayyala, Jianan Lin, Zhengqing Ouyang

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article numberbbab309
JournalBriefings in Bioinformatics
Volume23
Issue number1
DOIs
StatePublished - Jan 1 2022
Externally publishedYes

Keywords

  • RNA methylation
  • differential analysis
  • statistical methods

ASJC Scopus subject areas

  • Information Systems
  • Molecular Biology

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