An open RNA-Seq data analysis pipeline tutorial with an example of reprocessing data from a recent Zika virus study.

Area of Science:

• Bioinformatics
• Genomics
• Computational Biology

Background:

• RNA-sequencing (RNA-seq) is crucial for gene expression profiling but poses challenges for non-experts due to data size and computational demands.
• Existing RNA-seq analysis methods often lack standardization and reproducibility, hindering widespread adoption.

Purpose of the Study:

• To develop a reproducible, open-source RNA-sequencing analysis pipeline accessible to non-experts.
• To facilitate knowledge extraction from RNA-seq data, including interactive visualization and pathway enrichment analysis.
• To apply the pipeline to Zika virus (ZIKV) infection data and identify potential therapeutic interventions.

Main Methods:

• Development of a reproducible RNA-seq pipeline using an IPython notebook and Docker image.
• Integration of state-of-the-art bioinformatics tools for data analysis.
• Application of the pipeline to a Zika virus RNA-seq dataset from human neuronal progenitors.

Main Results:

• The pipeline successfully generated interactive principal component analysis (PCA) and hierarchical clustering (HC) plots.
• Enrichment analyses identified cell cycle genes downregulated by ZIKV and revealed overlaps with genes affecting brain morphology.
• Predicted small molecules that may mimic or reverse ZIKV-induced gene expression changes.

Conclusions:

• The developed pipeline democratizes RNA-seq analysis, making complex gene expression profiling accessible to researchers with limited bioinformatics expertise.
• The analysis provides insights into molecular mechanisms underlying ZIKV-induced microcephaly and suggests potential therapeutic strategies.
• The open-source nature of the pipeline and its availability on Docker Hub promote reproducibility and collaboration in RNA-seq research.