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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: Feb 23, 2026

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

QNB: differential RNA methylation analysis for count-based small-sample sequencing data with a quad-negative binomial

Lian Liu¹, Shao-Wu Zhang², Yufei Huang³

¹Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China.

BMC Bioinformatics

|September 2, 2017

Summary

This summary is machine-generated.

QNB is a new statistical method for analyzing RNA methylation differences using small-sample sequencing data. It improves accuracy, especially for low-expression genes, outperforming existing methods in RNA epigenetics studies.

Keywords:

Differential methylation analysis Negative binomial distribution RNA methylation Small-sample size m6A

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Last Updated: Feb 23, 2026

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Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Published on: February 24, 2015

Area of Science:

Molecular Biology
Genomics
Bioinformatics

Background:

RNA epigenetics, including RNA methylation, is a rapidly advancing field crucial for biological processes.
High-throughput sequencing (e.g., MeRIP-Seq) provides transcriptome-wide RNA methylation data as counts.
Small sample sizes and low gene expression pose challenges for accurate differential RNA methylation analysis.

Purpose of the Study:

To develop a robust statistical approach for differential RNA methylation analysis with small-sample, count-based sequencing data.
To address limitations of existing methods in handling low-expression genes and utilizing control samples effectively.

Main Methods:

Introduced QNB, a novel statistical approach utilizing 4 independent negative binomial distributions.
Linked distribution variances and means via local regressions for improved modeling.
Developed a robust gene expression estimator by integrating input and IP sample information.

Main Results:

QNB demonstrated superior performance compared to existing algorithms on simulated and real MeRIP-Seq data.
The method effectively handles small sample sizes and improves testing for lowly expressed genes.
QNB's approach to incorporating control samples enhances analytical power.

Conclusions:

QNB offers improved accuracy and robustness for differential RNA methylation analysis.
The QNB model is adaptable for various RNA modification sequencing datasets, including m1A-Seq and RIP-Seq.
This approach advances the study of epitranscriptomic dynamics.