The recount3 Python package for programmatic access to uniformly processed RNA-seq data
Abstract
The recount3 online resource provides tens of thousands of uniformly processed RNA-seq samples across human and mouse from major sequencing repositories like the Sequence Read Archive. While access to these datasets has traditionally been centered in the R/Bioconductor ecosystem, the growing prominence of Python in bioinformatics and machine learning necessitates native, efficient tooling for Python users. Therefore, we present the recount3 Python package with robust application programming interface (API) and command-line interface (CLI) for discovering, downloading, and materializing recount3 resources. The software orchestrates uniform resource locator (URL) resolution, persistent on-disk caching, and the automatic parsing of data into analysis-ready data structures, including Pandas DataFrames and BiocPy RangedSummarizedExperiment objects. The recount3 Python package drastically lowers the barrier to entry for large-scale utilization of RNA-seq data in Python-based computational pipelines, bridging the gap between massive public transcriptomic data and modern machine learning ecosystems.
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BibTeX
@article{alsalihi2026recount3,
title = {{The recount3 Python package for programmatic access to uniformly processed RNA-seq data}},
author = {Alexander Alsalihi and Robert M. Flight and Hunter N. B. Moseley},
journal = {BMC Genomics},
year = {2026},
month = nov,
doi = {10.1186/s12864-026-13499-9},
url = {https://link.springer.com/article/10.1186/s12864-026-13499-9},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - The recount3 Python package for programmatic access to uniformly processed RNA-seq data
AU - Alexander Alsalihi
AU - Robert M. Flight
AU - Hunter N. B. Moseley
PY - 2026
DA - 2026/11/03
JO - BMC Genomics
DO - 10.1186/s12864-026-13499-9
UR - https://link.springer.com/article/10.1186/s12864-026-13499-9
AB - The recount3 online resource provides tens of thousands of uniformly processed RNA-seq samples across human and mouse from major sequencing repositories like the Sequence Read Archive. While access to these datasets has traditionally been centered in the R/Bioconductor ecosystem, the growing prominence of Python in bioinformatics and machine learning necessitates native, efficient tooling for Python users. Therefore, we present the recount3 Python package with robust application programming interface (API) and command-line interface (CLI) for discovering, downloading, and materializing recount3 resources. The software orchestrates uniform resource locator (URL) resolution, persistent on-disk caching, and the automatic parsing of data into analysis-ready data structures, including Pandas DataFrames and BiocPy RangedSummarizedExperiment objects. The recount3 Python package drastically lowers the barrier to entry for large-scale utilization of RNA-seq data in Python-based computational pipelines, bridging the gap between massive public transcriptomic data and modern machine learning ecosystems.
SN - 1471-2164
ER -