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

Abstract

Summary: Transcript quantification tools efficiently map bulk RNA sequencing (RNA-seq) reads to reference transcriptomes. However, their output consists of transcript count estimates that are subject to multiple biases and cannot be readily used with existing differential gene expression analysis tools in Python.Here we present pytximport, a Python implementation of the tximport R package that supports a variety of input formats, different modes of bias correction, inferential replicates, gene-level summarization of transcript counts, transcript-level exports, transcript-to-gene mapping generation, and optional filtering of transcripts by biotype. pytximport is part of the scverse ecosystem of open-source Python software packages for omics analyses and includes both a Python as well as a command-line interface.With pytximport, we propose a bulk RNA-seq analysis workflow based on Bioconda and scverse ecosystem packages, ensuring reproducible analyses through Snakemake rules. We apply this pipeline to a publicly available RNA-seq dataset, demonstrating how pytximport enables the creation of Python-centric workflows capable of providing insights into transcriptomic alterations.

Availability and implementation: pytximport is licensed under the GNU General Public License version 3. The source code is available at https://github.com/complextissue/pytximport and via Zenodo with DOI: 10.5281/zenodo.13907917. A related Snakemake workflow is available through GitHub at https://github.com/complextissue/snakemake-bulk-rna-seq-workflow and Zenodo with DOI: 10.5281/zenodo.12713811. Documentation and a vignette for new users are available at: https://pytximport.readthedocs.io.

Figure 1.

Overview of the pytximport package and its associated RNA-seq workflow. (a) pytximport package. pytximport is available for use as a Python library or from the command line. It can be configured to either output AnnData objects for integration with other scverse ecosystem software or xarray datasets. Common applications for pytximport include gene count estimation from transcript quantification files, isoform-usage bias correction, filtering of transcript-level data, and creation of transcript-to-gene mappings. (b) Pythonic RNA-seq analysis workflow. We propose a reproducible RNA-seq analysis workflow based on command-line software available through Bioconda (left line: Snakemake, fastp, Salmon) and scverse ecosystem Python packages (right line: pytximport, PyDESeq2, decoupleR). (c) Comparison with tximport. Counts from pytximport match counts from tximport exactly across different quantification modes and input files from different transcript quantification tools. RSEM-g: RSEM gene-level input; RSEM-t: RSEM transcript-level input. The Python logo is in the public domain and was provided through Bioicons. The AnnData logo is licensed under the BSD 3-Clause License. The xarray logo is provided under the Apache License Version 2.0. The Snakemake and PyDESeq2 logos are licensed under the MIT License. The Salmon and decoupleR logos are licensed under the GNU General Public License Version 3.