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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: May 30, 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

MBCdeg4: A modified clustering-based method for identifying differentially expressed genes from RNA-seq data.

Chiharu Ichikawa¹, Koji Kadota^1,2,3

¹Graduate School of Agricultural and Life Sciences, The University of Tokyo, Yayoi 1-1-1, Bunkyo-ku, Tokyo 113-8657, Japan.

|January 27, 2025

Summary

This summary is machine-generated.

The new MBCdeg4 method accurately identifies and classifies differentially expressed genes (DEGs) from RNA-seq data. It outperforms previous versions and conventional tools, making it a recommended choice for transcriptome analysis.

Keywords:

Gene clustering Gene expression MBCdeg4 Normalization R package

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

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Published on: June 24, 2021

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

RNA-sequencing (RNA-seq) is crucial for transcriptome measurement and identifying differentially expressed genes (DEGs).
Previous clustering-based DEG identification methods (MBCdeg1-3) were developed using the MBCluster.Seq R package.
Refinement of DEG identification methodologies is essential for accurate biological interpretation.

Purpose of the Study:

To introduce and evaluate MBCdeg4, an improved method for identifying and classifying DEGs from RNA-seq count data.
To compare the performance of MBCdeg4 against its predecessors (MBCdeg1-3) and conventional R packages (edgeR, DESeq2, TCC).
To establish MBCdeg4 as a superior tool for DEG analysis.

Main Methods:

Development of MBCdeg4, an enhanced version of the MBCdeg DEG identification method.
Utilization of the MBCluster.Seq R package with a novel normalization approach using DEGES (Differential Expression Gene Expression Statistics) derived normalization factors.
Comparative performance analysis using multiple simulation scenarios with RNA-seq count data against edgeR, DESeq2, TCC, and MBCdeg1-3.

Main Results:

MBCdeg4 demonstrated superior performance across various simulation scenarios for RNA-seq count data.
The DEGES normalization algorithm contributed to the enhanced accuracy of MBCdeg4.
MBCdeg4 consistently outperformed MBCdeg1-3, edgeR, DESeq2, and TCC in identifying and classifying DEGs.

Conclusions:

MBCdeg4 is a highly effective method for both the identification and classification of DEGs from RNA-seq data.
The DEGES normalization strategy significantly improves DEG analysis accuracy.
MBCdeg4 is recommended for its robust performance and is available as an R function for broader application.