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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Oct 18, 2025

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Differential RNA methylation using multivariate statistical methods.

Deepak Nag Ayyala¹, Jianan Lin², Zhengqing Ouyang³

¹Division of Biostatistics and Data Science, Department of Population Health Sciences, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA.

Briefings in Bioinformatics

|September 29, 2021

Summary

Mean vector testing (MVT) procedures offer improved detection of differential RNA methylation from m6A-seq data. This method controls error rates and enhances sensitivity, outperforming existing tools for analyzing methylation patterns.

Keywords:

RNA methylation differential analysis statistical methods

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Last Updated: Oct 18, 2025

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

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Published on: September 18, 2021

Antibody-Free Assay for RNA Methyltransferase Activity Analysis

Antibody-Free Assay for RNA Methyltransferase Activity Analysis

Published on: July 9, 2019

DNAzyme-dependent Analysis of rRNA 2’-O-Methylation

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Published on: September 16, 2019

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

N6-methyladenosine (m6A) is a prevalent RNA modification in eukaryotes.
MeRIP-seq (m6A-seq) is the standard method for measuring m6A signals.
Existing analysis tools have limitations in handling methylation signal sparsity and dependence.

Purpose of the Study:

To introduce Mean Vector Testing (MVT) procedures for gene-level differential RNA methylation analysis.
To address the limitations of existing computational tools for MeRIP-seq data.
To improve the accuracy and sensitivity of differential methylation detection.

Main Methods:

Application of Mean Vector Testing (MVT) procedures for analyzing MeRIP-seq data.
Utilizing a distribution-free test statistic within MVTs.
Comprehensive simulation studies comparing MVTs with existing tools.
Analysis of existing MeRIP-seq datasets to demonstrate MVT advantages.

Main Results:

MVTs effectively control the Type I error rate and achieve high power in detecting differential RNA methylation from m6A-seq data.
Analysis of two datasets revealed that identified genes with differential m6A patterns are functionally relevant to study conditions.
MVTs demonstrate superior performance compared to existing MeRIP-seq analysis tools.

Conclusions:

Mean Vector Testing (MVT) procedures provide a robust and sensitive method for differential RNA methylation analysis.
The proposed method enhances the reliability of MeRIP-seq data interpretation.
The DIMER software package implements these MVT procedures for broader accessibility.