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Review
. 2024 Mar 1:23:1154-1168.
doi: 10.1016/j.csbj.2024.02.018. eCollection 2024 Dec.

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

Affiliations
Review

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

Diletta Rosati et al. Comput Struct Biotechnol J. .

Abstract

In recent years, the role of bioinformatics and computational biology together with omics techniques and transcriptomics has gained tremendous importance in biomedicine and healthcare, particularly for the identification of biomarkers for precision medicine and drug discovery. Differential gene expression (DGE) analysis is one of the most used techniques for RNA-sequencing (RNA-seq) data analysis. This tool, which is typically used in various RNA-seq data processing applications, allows the identification of differentially expressed genes across two or more sample sets. Functional enrichment analyses can then be performed to annotate and contextualize the resulting gene lists. These studies provide valuable information about disease-causing biological processes and can help in identifying molecular targets for novel therapies. This review focuses on differential gene expression (DGE) analysis pipelines and bioinformatic techniques commonly used to identify specific biomarkers and discuss the advantages and disadvantages of these techniques.

Keywords: Bioinformatic analyses; Biomarkers; Biomarkers discovery; Differential gene expression analysis; Pathway enrichment.

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Conflict of interest statement

The authors have no conflict of interest.

Figures

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Graphical abstract
Fig. 1
Fig. 1
MA plot. MA plot showing l2FC compared to mean expressions generated by Deseq2 R.
Fig. 2
Fig. 2
Volcano plot. Volcano plot generated by DESeq2 R.

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