RnaXtract, a tool for extracting gene expression, variants, and cell-type composition from bulk RNA sequencing

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• RNA sequencing (RNA-seq) is crucial for transcriptomics, offering insights into gene expression, variant discovery, and cellular composition.
• Existing RNA-seq pipelines often focus on gene expression, neglecting cell deconvolution and variant calling capabilities.
• There is a need for integrated workflows that maximize information extraction from bulk RNA-seq data.

Purpose of the Study:

• To introduce RnaXtract, a comprehensive and user-friendly pipeline for bulk RNA-seq data analysis.
• To address limitations in existing pipelines by integrating gene expression quantification, variant calling, and cell-type deconvolution.
• To provide researchers with a robust, reproducible, and flexible end-to-end solution for transcriptomics research.

Main Methods:

• RnaXtract is built on the Snakemake framework for reproducibility and efficient resource management.
• The pipeline automates quality control, gene expression quantification, variant calling, and cell-type deconvolution.
• It integrates state-of-the-art tools, including EcoTyper and CIBERSORTx for cell deconvolution, and updated variant calling tools.

Main Results:

• RnaXtract provides a cohesive framework for extracting multiple layers of biological information from bulk RNA-seq data.
• The pipeline successfully integrates gene expression analysis, variant discovery, and cell-type deconvolution.
• It empowers researchers to gain precise biological insights into gene expression, genetic variation, and cellular heterogeneity.

Conclusions:

• RnaXtract addresses critical gaps in current RNA-seq workflows by offering an integrated, end-to-end solution.
• The pipeline enhances the utility of bulk RNA-seq data, enabling deeper exploration of biological systems.
• RnaXtract facilitates comprehensive analysis of transcriptomics data, supporting diverse research needs in genomics and molecular biology.