/DeepCAPE

A Deep Convolutional Neural Network for The Accurate Prediction of Enhancers

Primary LanguagePythonMIT LicenseMIT

DeepCAPE

DeepCAPE is a deep convolutional neural network to predict enhancers via the integration of DNA sequences and corresponding DNase-seq data.

Requirements

  • keras
  • numpy
  • hickle
  • random
  • tensorflow

Installation

Download DeepCAPE by

git clone https://github.com/ShengquanChen/DeepCAPE
 Arguments:  
  inputfile: preprocessed data (hkl format)  
  outputfile: trained model (h5 format)

Installation has been tested in a Linux/MacOS platform with Python2.7.

OpenAnnotate

Use OpenAnnotate (http://health.tsinghua.edu.cn/openannotate/) to annotate the chromatin accessibility of genomic regions across diverse types of cell lines, tissues, and systems. We also provide a simplified demo in OpenAnnotate for the demonstration of DeepCAPE pipeline.

Citation

Chen, Shengquan, Mingxin Gan, Hairong Lv, and Rui Jiang. "DeepCAPE: a deep convolutional neural network for the accurate prediction of enhancers." Genomics, Proteomics & Bioinformatics (2021).

License

This project is licensed under the MIT License - see the LICENSE file for details