scikit-bio 0.7.1.post1

Data structures, algorithms and educational resources for bioinformatics.

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: 0.7.1 (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:

• QIIME 2, Qiita, Emperor, tax2tree, ghost-tree, Platypus-Conquistador, An Introduction to Applied Bioinformatics.

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), one software engineer: Matthew Aton (@mataton) and one bioinformatician: Dr. Lars Hunger (@LarsHunger). 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 see our Zenodo page for how to cite.

Collaboration

For collaboration inquiries and other formal communications, please reach out to Dr. Qiyun Zhu at [e-mail]. 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).