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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 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.

Requirements

footprint-tools requires Python 3.6+

We also recommend these non-Python analysis tools:

Installation

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}

Documentation & usage

User manual, API and examples can be found here

Citation

Vierstra2020 Vierstra, J., Lazar, J., Sandstrom, R. et al. Global reference mapping of human transcription factor footprints. Nature 583, 729–736 (2020)

Project details


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Source Distribution

footprint_tools-1.3.7.tar.gz (113.4 kB view hashes)

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