Gene count estimation with pytximport enables reproducible analysis of bulk RNA sequencing data in Python

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Bulk RNA sequencing (RNA-seq) generates transcript count estimates prone to biases.
• Existing analysis tools in Python struggle to directly utilize these raw counts for differential gene expression analysis.

Purpose of the Study:

• Introduce pytximport, a Python implementation of the R tximport package.
• Enable seamless integration of RNA-seq quantification data into Python-based omics workflows.
• Facilitate reproducible and bias-corrected transcriptomic data analysis.

Main Methods:

• Developed pytximport as a Python package and command-line interface.
• Implemented support for various input formats, bias correction methods, and inferential replicates.
• Integrated pytximport into a reproducible bulk RNA-seq workflow using Bioconda, scverse, and Snakemake.

Main Results:

• pytximport successfully processes and summarizes transcript counts from RNA-seq data.
• The tool allows for gene-level summarization and transcript-level exports.
• Demonstrated the utility of pytximport in a Python-centric workflow for analyzing transcriptomic alterations.

Conclusions:

• pytximport enhances Python-based bulk RNA-seq analysis by providing bias-corrected transcript counts.
• The integration with the scverse ecosystem and Snakemake promotes reproducible omics research.
• Enables deeper insights into transcriptomic changes using Python tools.