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.
Requirements
footprint-tools
requires Python (>=3.5) and depends on the following additional
packages:
- cython (>=0.22)
- numpy (>=1.10)
- scipy (>=0.16)
- genome_tools (1.0.1) (http://www.github.com/jvierstra/genome_tools)
- pysam
- pyfaidx
- statsmodels
Additional recomended analysis tools:
- bedops
- kentUtils
Installation
To install the latest release, type:
pip install footprint-tools
Documentation & usage
User manual, API and examples can be found here
Citation
.. [Vierstra2020] Global reference mapping and dynamics of human transcription factor footprints. Vierstra J et al. (2020)
bioRxiv.
<https://doi.org/10.1101/2020.01.31.927798>
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