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

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.