scikit-bio is an open-source, BSD-licensed Python 3 package providing data structures, algorithms and educational resources for bioinformatics.
Visit the scikit-bio website: https://scikit.bio to learn more about this project.
Releases
Latest release: 0.7.4 (documentation, changelog). Compatible with Python 3.10 and above.
Installation
Install the latest release of scikit-bio using conda:
conda install -c conda-forge scikit-bio
Or using pip:
pip install scikit-bio
See further instructions on installing scikit-bio on various platforms.
Adoption
Some of the projects that we know of that are using scikit-bio are:
License
scikit-bio is available under the new BSD license. See LICENSE.txt for scikit-bio’s license, and the licenses directory for the licenses of third-party software that is (either partially or entirely) distributed with scikit-bio.
Team
Our core development team consists of three lead developers: Dr. Qiyun Zhu at Arizona State University (ASU) (@qiyunzhu), Dr. James Morton at Gutz Analytics (@mortonjt), and Dr. Daniel McDonald at the University of California San Diego (UCSD) (@wasade), and one dedicated software engineer: Matthew Aton at ASU (@mataton). Dr. Rob Knight at UCSD (@rob-knight) provides guidance on the development and research. Dr. Greg Caporaso (@gregcaporaso) at Northern Arizona University (NAU), the former leader of the scikit-bio project, serves as an advisor on the current project.
Credits
We thank the many contributors to scikit-bio. A complete list of contributors to the scikit-bio codebase is available at GitHub. This however may miss the larger community who contributed by testing the software and providing valuable comments, who we hold equal appreciation to.
Wanna contribute? We enthusiastically welcome community contributors! Whether it’s adding new features, improving code, or enhancing documentation, your contributions drive scikit-bio and open-source bioinformatics forward. Start your journey by reading the Contributor’s guidelines.
Funding
The development of scikit-bio is currently supported by the U.S. Department of Energy, Office of Science under award number DE-SC0024320, awarded to Dr. Qiyun Zhu at ASU (lead PI), Dr. James Morton at Gutz Analytics, and Dr. Rob Knight at UCSD.
Citation
If you use scikit-bio for any published research, please cite:
Aton, M., McDonald, D., Cañardo Alastuey, J. et al. Scikit-bio: a fundamental Python library for biological omic data analysis. Nat Methods (2025). https://doi.org/10.1038/s41592-025-02981-z
The full text of this article is available at: https://rdcu.be/eUcOO.
Collaboration
For collaboration inquiries and other formal communications, please reach out to Dr. Qiyun Zhu at qiyun.zhu@asu.edu. We welcome academic and industrial partnerships to advance our mission.
Branding
The logo of scikit-bio was created by Alina Prassas. Vector and bitmap image files are available at the logos directory.
Pre-history
scikit-bio began from code derived from PyCogent and QIIME, and the contributors and/or copyright holders have agreed to make the code they wrote for PyCogent and/or QIIME available under the BSD license. The contributors to PyCogent and/or QIIME modules that have been ported to scikit-bio are listed below:
Rob Knight (@rob-knight), Gavin Huttley (@gavinhuttley), Daniel McDonald (@wasade), Micah Hamady, Antonio Gonzalez (@antgonza), Sandra Smit, Greg Caporaso (@gregcaporaso), Jai Ram Rideout (@jairideout), Cathy Lozupone (@clozupone), Mike Robeson (@mikerobeson), Marcin Cieslik, Peter Maxwell, Jeremy Widmann, Zongzhi Liu, Michael Dwan, Logan Knecht (@loganknecht), Andrew Cochran, Jose Carlos Clemente (@cleme), Damien Coy, Levi McCracken, Andrew Butterfield, Will Van Treuren (@wdwvt1), Justin Kuczynski (@justin212k), Jose Antonio Navas Molina (@josenavas), Matthew Wakefield (@genomematt) and Jens Reeder (@jensreeder).
4 maintainers
Release files for scikit-bio 0.7.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| scikit_bio-0.7.4.tar.gz | 6.0 MB | Details |
Built distributions (wheels)
Total release size: 246.5 MB
Release files / scikit_bio-0.7.4.tar.gz
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No |
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twine/7.0.0 CPython/3.13.14
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Release files / scikit_bio-0.7.4-cp311-cp311-macosx_11_0_arm64.whl
| Download URL | scikit_bio-0.7.4-cp311-cp311-macosx_11_0_arm64.whl |
|---|---|
| Size | 6.9 MB |
| Tags | CPython 3.11 macOS 11.0+ ARM64 |
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No |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
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Release files / scikit_bio-0.7.4-cp311-cp311-macosx_10_9_x86_64.whl
| Download URL | scikit_bio-0.7.4-cp311-cp311-macosx_10_9_x86_64.whl |
|---|---|
| Size | 6.9 MB |
| Tags | CPython 3.11 macOS 10.9+ x86-64 |
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SHA-256 checksum How to use checksums |
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No |
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twine/7.0.0 CPython/3.13.14
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Release files / scikit_bio-0.7.4-cp310-cp310-win_amd64.whl
| Download URL | scikit_bio-0.7.4-cp310-cp310-win_amd64.whl |
|---|---|
| Size | 7.0 MB |
| Tags | CPython 3.10 Windows x86-64 |
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SHA-256 checksum How to use checksums |
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twine/7.0.0 CPython/3.13.14
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Release files / scikit_bio-0.7.4-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
| Download URL | scikit_bio-0.7.4-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl |
|---|---|
| Size | 10.7 MB |
| Tags | CPython 3.10 Linux glibc 2.17+ x86-64 Linux glibc 2.28+ x86-64 |
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SHA-256 checksum How to use checksums |
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No |
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twine/7.0.0 CPython/3.13.14
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Release files / scikit_bio-0.7.4-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl
| Download URL | scikit_bio-0.7.4-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl |
|---|---|
| Size | 10.8 MB |
| Tags | CPython 3.10 Linux glibc 2.17+ ARM64 Linux glibc 2.28+ ARM64 |
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No |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Release files / scikit_bio-0.7.4-cp310-cp310-macosx_11_0_arm64.whl
| Download URL | scikit_bio-0.7.4-cp310-cp310-macosx_11_0_arm64.whl |
|---|---|
| Size | 6.9 MB |
| Tags | CPython 3.10 macOS 11.0+ ARM64 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Release files / scikit_bio-0.7.4-cp310-cp310-macosx_10_9_x86_64.whl
| Download URL | scikit_bio-0.7.4-cp310-cp310-macosx_10_9_x86_64.whl |
|---|---|
| Size | 6.9 MB |
| Tags | CPython 3.10 macOS 10.9+ x86-64 |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
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