BiocPy is an effort to develop core data structures and representations from Bioconductor to Python. These structures, including BiocFrame and GenomicRanges, serve as essential and foundational data structures, acting as the building blocks for extensive and complex representations. For example, container classes like SummarizedExperiment, SingleCellExperiment, and MultiAssayExperiment represent single or multi-omic experimental data and metadata.

For complete list of all packages, visit the GitHub:BiocPy repository.

• BiocFrame: Bioconductor-like data frames in Python. (GitHub, Docs)
• IRanges: Python implementation of the IRanges package to support interval arithmetic. (GitHub, Docs)
• GenomicRanges: Container class to represent genomic locations and support genomic analysis. (GitHub, Docs, BioC)
• SummarizedExperiment: Container class to represent genomic experiments, following Bioconductor's SummarizedExperiment. (GitHub, Docs)
• SingleCellExperiment: Container class to represent single-cell experiments; follows Bioconductor’s SingleCellExperiment. (GitHub, Docs)
• MultiAssayExperiment: Container class to represent multiple experiments and assays performed over a set of samples, following Bioconductor's MAE R/Bioc Package. (GitHub, Docs)

• scranpy: Python bindings to single-cell analysis methods from libscran and related C++ libraries. (GitHub, Docs)
• singler: Python bindings to the singleR algorithm to annotate cell types from known references. (GitHub, Docs)

• rds2py: Read RDS files directly in Python, supporting Bioconductor's SummarizedExperiment and SingleCellExperiment, in addition to matrices, data frames, and vectors. (GitHub, Docs)

• BiocUtils: Common utilities for use across packages, mostly to mimic convenient aspects of base R. (GitHub, Docs)
• mopsy: Helper functions to perform row or column operations over numpy and scipy matrices, providing an interface similar to base R matrix methods/MatrixStats methods. (GitHub, Docs)
• pyBiocFileCache: File system-based cache for resources & metadata. (GitHub, Docs)

All packages in the BiocPy ecosystem are published to PyPI. Use the biocpy wrapper to install the core packages:

pip install biocpy

Individual packages can also be installed separately. Refer to package's documentation for more details.