scikit-bio: Bioinformatics in Python — scikit-bio

A community-driven Python library for bioinformatics, providing versatile data structures, algorithms and educational resources.
Robust, performant and scalable algorithms tailored for the vast landscape of biological data analysis spanning genomics, microbiomics, ecology, evolutionary biology and more. Built to unveil the insights hidden in complex, multi-omic data.
Fundamental bioinformatics algorithms enriched by comprehensive documentation, examples and references, offering a rich resource for classroom and laboratory education (with proven success). Designed to spark curiosity and foster innovation.
Industry-standard, production-ready Python codebase featuring a stable, unit-tested API that streamlines development and integration. Licensed under the 3-Clause BSD, it provides an expansive platform for both academic research and commercial ventures.
See detailed instructions on installing scikit-bio on various platforms.
Latest release (2026-09-21):
scikit-bio is participating in Google Summer of Code 2026. Contributors are welcome!
scikit-bio paper published in Nature Methods. Full text available.
scikit-bio workshop at ISMB 2024. Materials are publicly available.
DOE grant for scikit-bio development in multi-omics and complex modeling.
New website: scikit.bio and organization: scikit-bio are online.
Biological sequences: Efficient data structure with a flexible grammar for easy manipulation, annotation, alignment, and conversion into motifs or k-mers for in-depth analysis.
Phylogenetic trees: Scalable tree structure tailored for evolutionary biology, supporting diverse operations in navigation, manipulation, comparison, and construction.
Community diversity analysis for ecological studies, with an extensive suite of metrics such as UniFrac and PD, optimized to handle large-scale community datasets.
Ordination methods, such as PCoA, CA, and RDA, to uncover patterns underlying high-dimensional data, facilitating insightful visualization.
Multivariate statistical tests, such as PERMANOVA, BIOENV, and Mantel, to decode complex relationships across data matrices and sample properties.
Compositional data processing and analysis, such as CLR transform and ANCOM, built for various omic data types from high-throughput experiments.
© Copyright 2014-2026, scikit-bio development team.
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