RnaXtract, a tool for extracting gene expression, variants, and cell-type composition from bulk RNA sequencing
Abstract RNA sequencing (RNA-seq) is a widely used method in transcriptomics research, offering insights into gene expression, variant discovery, and, when deconvoluted, the cellular composition of complex tissues. However, existing RNA-seq pipelines frequently emphasize gene expression analysis and...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-16875-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract RNA sequencing (RNA-seq) is a widely used method in transcriptomics research, offering insights into gene expression, variant discovery, and, when deconvoluted, the cellular composition of complex tissues. However, existing RNA-seq pipelines frequently emphasize gene expression analysis and often lack cell deconvolution and variant calling. To address these limitations, we present RnaXtract, a comprehensive and user-friendly pipeline designed to maximize extraction of valuable information from bulk RNA-seq data. RnaXtract automates an entire workflow, encompassing quality control, gene expression quantification, variant calling, and the cell-type deconvolution. Built on the Snakemake framework, RnaXtract ensures robust reproducibility, efficient resource management, and flexibility to adapt to diverse research needs. The pipeline integrates state-of-the-art tools, from quality control to the new updates on variant calling and cell-type deconvolution tools such as EcoTyper and CIBERSORTx, enabling researchers to extract biological insights with precision. By providing an end-to-end solution for bulk RNA-seq, RnaXtract addresses critical gaps in existing workflows, empowering researchers to explore gene expression, genetic variation, and cellular heterogeneity within a single cohesive framework. |
|---|---|
| ISSN: | 2045-2322 |