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Updated: Sep 10, 2025

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

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

Sophiane G Bouirdene^1,2,3, Simon Gotty^2,3, Mickaël Leclercq^2,3

¹Doctoral Program in Molecular Medicine, Faculté de Médecine, Université Laval, Québec, Québec, G1V 0A6, Canada.

Scientific Reports

|August 24, 2025

Summary

RnaXtract is a new pipeline for RNA sequencing (RNA-seq) data analysis. It automates gene expression, variant calling, and cell deconvolution, providing a comprehensive framework for transcriptomics research.

Keywords:

Bulk RNA-seq Cell deconvolution Computational biology Gene expression RNA sequencing analysis Snakemake pipeline Transcriptomics Variant calling

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

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Published on: August 4, 2016

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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.