Bioframe: operations on genomic intervals in Pandas dataframes

Abstract

Motivation: Genomic intervals are one of the most prevalent data structures in computational genome biology, and used to represent features ranging from genes, to DNA binding sites, to disease variants. Operations on genomic intervals provide a language for asking questions about relationships between features. While there are excellent interval arithmetic tools for the command line, they are not smoothly integrated into Python, one of the most popular general-purpose computational and visualization environments.

Results: Bioframe is a library to enable flexible and performant operations on genomic interval dataframes in Python. Bioframe extends the Python data science stack to use cases for computational genome biology by building directly on top of two of the most commonly-used Python libraries, NumPy and Pandas. The bioframe API enables flexible name and column orders, and decouples operations from data formats to avoid unnecessary conversions, a common scourge for bioinformaticians. Bioframe achieves these goals while maintaining high performance and a rich set of features.

Availability and implementation: Bioframe is open-source under MIT license, cross-platform, and can be installed from the Python Package Index. The source code is maintained by Open2C on GitHub at https://github.com/open2c/bioframe.

Figure 1.

Performance comparison of bioframe v0.6.1, PyRanges v0.0.129, and pybedtools v0.9.1 (bedtools v2.30.0) for detecting overlapping intervals between pairs of DataFrames of randomly generated genomic intervals. (A) Run time and (B) Peak memory consumption of bioframe overlap vs. PyRanges join show comparable performance up to millions of intervals and comparable memory usage. Pybedtools intersect shows slower performance. Code for this performance comparison is available at https://bioframe.readthedocs.io/en/latest/guide-performance.html.