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Updated: Aug 17, 2025

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Temporal progress of gene expression analysis with RNA-Seq data: A review on the relationship between computational

Juliana Costa-Silva¹, Douglas S Domingues², David Menotti¹

¹Department of Informatics - Federal University of Paraná, Rua Coronel Francisco Heráclito dos Santos, 100, 81531-990 Curitiba, Paraná, Brazil.

Computational and Structural Biotechnology Journal

|December 14, 2022

Summary

This summary is machine-generated.

This review details computational methods for analyzing differential gene expression (DEG) from RNA sequencing (RNA-seq) data. It provides an organized overview of DEG analysis pipelines, tools, and challenges for researchers.

Keywords:

Bioinformatics Differential expression analysis Gene expression RNA-Seq

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Last Updated: Aug 17, 2025

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

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

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Published on: September 18, 2021

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

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Published on: June 8, 2020

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Differential gene expression (DEG) analysis of RNA sequencing (RNA-seq) data is a standard in many research fields.
Computational analysis involves diverse data types, file formats, and numerous tools, often integrated into pipelines.

Purpose of the Study:

To provide a comprehensive review of computational methods for DEG analysis in RNA-seq data.
To offer an organized overview of DEG analysis pipeline steps, objectives, and available methods.
To serve as a tutorial for new researchers and an update for established users.

Main Methods:

Review of existing computational methods for DEG analysis.
Presentation of a timeline for DEG computational methods.
Illustration of tool relationships via an interaction network.

Main Results:

An organized overview of DEG analysis pipeline steps and their objectives.
Discussion of principal methods, their properties, and inter-tool relationships.
Identification of challenges and gaps in current DEG analysis.

Conclusions:

The review offers a structured guide to DEG analysis pipelines and computational tools.
It aids researchers in understanding and updating their RNA-seq data analysis strategies.
The paper addresses current limitations and future directions in DEG analysis.