Bioframe: operations on genomic intervals in Pandas dataframes

Area of Science:

• Computational Biology
• Bioinformatics

Background:

• Genomic intervals are fundamental data structures in computational genome biology.
• Existing command-line tools for genomic interval operations lack seamless Python integration.

Purpose of the Study:

• To introduce Bioframe, a Python library for flexible and performant genomic interval dataframe operations.
• To extend the Python data science stack for computational genome biology use cases.

Main Methods:

• Bioframe builds upon NumPy and Pandas, leveraging their performance and widespread adoption.
• The API supports flexible column orders and decouples operations from specific data formats.
• The library is implemented in Python for easy integration into existing workflows.

Main Results:

• Bioframe provides a rich set of features for genomic interval analysis.
• It achieves high performance while maintaining flexibility in data handling.
• The library avoids common bioinformatician pain points like unnecessary data conversions.

Conclusions:

• Bioframe enhances the Python data science ecosystem for genomic interval analysis.
• It offers a performant and flexible solution for computational genome biology.
• The library is readily available and open-source for the research community.