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Updated: Jan 1, 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

Identifying suitable tools for variant detection and differential gene expression using RNA-seq data.

S Akila Parvathy Dharshini¹, Y-H Taguchi², M Michael Gromiha³

¹Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamilnadu, India.

|December 22, 2019

Summary

This summary is machine-generated.

This study evaluated RNA-sequencing tools for neurodegenerative disease research on the hg38 genome. The GATK and STAR aligner combination identified more variants, while Salmon excelled in gene quantification for hg38.

Keywords:

Brain tissue Differential gene expression Multi-mapped reads Variant calling hg38

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Area of Science:

Genomics
Bioinformatics
Neuroscience

Background:

Neurodegenerative diseases are increasing globally, necessitating advanced research tools.
RNA-sequencing (RNA-seq) is crucial for understanding gene expression, variants, and pathways in these disorders.
The performance of RNA-seq tools on the hg38 genome assembly requires systematic evaluation.

Purpose of the Study:

To systematically analyze and compare spliced aligners, transcript assemblers, and variant callers for RNA-seq data on hg19 and hg38 genome assemblies.
To identify optimal tool combinations for accurate variant calling and gene quantification on the hg38 genome.
To evaluate identified variants against expression Quantitative Trait Loci (eQTL) and Genome-Wide Association Study (GWAS) data.

Main Methods:

Systematic analysis of spliced aligners (STAR, SAMtools), transcript assemblers, and variant calling tools.
Comparison of variant calling pipelines using GATK and STAR versus SAMtools.
Evaluation of gene quantification methods, including Salmon, on hg38 and hg19 assemblies using hippocampus brain tissue.
Comparison of identified variants with eQTL and GWAS databases, and differentially expressed genes (DG) with microarray data.

Main Results:

The GATK and STAR protocol identified more GWAS/eQTL variants compared to SAMtools.
The hg38 assembly revealed a higher number of non-coding variants than hg19 due to improved annotation.
Salmon-based transcriptomic quantification on hg38 yielded more differentially expressed genes than other genome-based methods.
hg38 showed increased multi-mapping reads compared to hg19, potentially impacting gene quantification accuracy.

Conclusions:

The GATK and STAR combination is recommended for variant calling on hg38 for neurodegenerative disease research.
Salmon offers superior gene quantification performance on the hg38 assembly.
The increased multi-mapping reads in hg38 highlight the need for advanced algorithms to improve alignment and quantification accuracy.