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Genomic footprint detection

Project description

footprint-tools: de novo genomic footprint detection

footprint-tools is a python module for de novo detection of genomic footprints from DNase I data.

Footprint-tools detects footprints by simulating expected cleavage rates using a 6-mer DNase I cleavage preference model combined with density smoothing. Statistical significance of per-nucleotide cleavages are computed from a series emperically fit negative binomial distribution.


footprint-tools requires Python (>=3.5) and depends on the following additional packages:

We also recommend these non-python analysis tools:


To install the latest release, type:

pip install footprint-tools

Documentation & usage

User manual, API and examples can be found here


Vierstra2020 Global reference mapping and dynamics of human transcription factor footprints. Vierstra J et al. (2020) bioRxiv.

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