DiCoExpress: a tool to process multifactorial RNAseq experiments from quality controls to co-expression analysis...

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• RNA sequencing (RNAseq) is the standard for transcriptome analysis.
• Numerous statistical methods and bioinformatics tools have been developed for RNAseq.
• Recent studies benchmark these tools to identify optimal analysis approaches.

Purpose of the Study:

• To introduce DiCoExpress, an R script-based tool for comprehensive RNAseq analysis.
• To integrate statistically validated methods for quality control, differential expression, and co-expression analysis.
• To facilitate the biological interpretation of RNAseq data.

Main Methods:

• DiCoExpress utilizes existing R packages: FactoMineR, edgeR, and coseq.
• It performs quality control, differential expression analysis using generalized linear models with automated contrast writing, and co-expression analysis.
• Enrichment analysis of differentially expressed genes and co-expression clusters is automated.

Main Results:

• DiCoExpress was successfully used to analyze a Brassica napus RNAseq dataset.
• The analysis demonstrated the tool's capability to handle complex experimental designs with multiple factors and replicates.
• The tool effectively identified differentially expressed genes and co-expression clusters, facilitating annotation enrichment.

Conclusions:

• DiCoExpress is a versatile R script tool for end-to-end RNAseq analysis.
• It streamlines the process from quality control to biological interpretation.
• The tool emphasizes statistical modeling and experimental design for robust gene expression analysis.