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

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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.\nAVAILABILITY 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.", "claims": [{"id": "claim-data-access", "statement": "The public IgA nephropathy dataset used in the evaluation of pytximport is available via the European Nucleotide Archive with accession: PRJNA593015.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39565903", "locator": "sections[16] \"Data availability\" paragraph 2", "quote": "The public IgA nephropathy dataset used in the evaluation of pytximport is available via the European Nucleotide Archive with accession: PRJNA593015."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Across the tested configurations, pytximport had a median single-core runtime of 1.0–24.4 seconds, compared with 2.2–27.1 seconds for tximport.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39565903", "locator": "sections[10] \"3.2 Performance benchmark\" paragraph 2", "quote": "Depending on the configuration, the median runtime of pytximport ranged from 1.0 to 24.4 s. Similarly, tximport had a median time requirement between 2.2 and 27.1 s (Supplementary Table S2)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Across the tested configurations, pytximport had a median single-core runtime of 1.0–24.4 seconds, compared with 2.2–27.1 seconds for tximport.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:39565903"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_unresolved", "description": "The GEO series accession resolves and its sample accessions are listed. 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