footprint-tools is a python module for de novo detection of genomic footprints from DNase I data 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 3.6+
We also recommend these non-Python analysis tools:
To install the latest release, type:
pip install footprint-tools
If you run into errors, try installing footprint-tools in a conda environment (using the YAML file provided):
# Clone repository
git clone https://github.com/jvierstra/footprint-tools.git
# Create conda enviroment from config YAML file
cd footprint-tools
conda env create -f conda-env.yml
# Activate conda environment
conda activate footprint-tools
# Run commands
ftd --version
ftd {commands}
User manual, API and examples can be found here
Vierstra2020 Vierstra, J., Lazar, J., Sandstrom, R. et al. Global reference mapping of human transcription factor footprints. Nature 583, 729–736 (2020)