shalujhanwar/gep

Generalized Enhancer Predictor (GEP)

Generalized Enhancer Predictor (GEP)

Generalized Enhancer Predictor (GEP) is a Random Forest based machine-learning tool for identification of putative enhancers using chromatin dynamics data. The Random Forest model is implemented in python. The package also contains accessary programs required for the analysis.

Documentation can be found at https://generalized-enhancer-predictor-gep.readthedocs.io/en/latest/

Article:

https://www.biorxiv.org/content/early/2018/09/20/421230

License:

This project is licensed under the GNU Lesser General Public License v3.0.

References:

@article {Jhanwar421230,
	author = {Jhanwar, Shalu and Ossowski, Stephan and Davila-Velderrain, Jose},
	title = {Genome-wide active enhancer identification using cell type-specific signatures of epigenomic activity},
	year = {2018},
	doi = {10.1101/421230},
	publisher = {Cold Spring Harbor Laboratory},		
	URL = {https://www.biorxiv.org/content/early/2018/09/20/421230},
	eprint = {https://www.biorxiv.org/content/early/2018/09/20/421230.full.pdf},
	journal = {bioRxiv}
}

Support:

Please use “GitHub Issues” to report bugs occur during the implementation of the software.