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Code repository for PEAS (Predict Enhancers from ATAC-seq), including feature extraction files and easy to use python script for training enhancer models and predicting enhancers using MLP Neural Networks.
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PEASTools
PEASUI
example
extraction_files
models
.project
LICENSE.txt
PEAS.jar
PEASFeatureExtraction.sh
PEASManual.docx
PEASManual.pdf
PEASPredictionAnnotator.py
PEASPredictor.py
PEASTools.jar
PEASTrainer.py
PEASTssPromoter.py
PEASUtil.py
README.md

README.md

PEAS (Predict Enhancers from ATAC-seq)

##Requirements & Dependencies

  1. Bash (can execute shell scripts)
  2. Java version 1.8.0_171 or more recent (https://java.com/en/download/)
  3. SAMTools (https://github.com/samtools/samtools/releases)
  4. MACS2 (https://github.com/taoliu/MACS)
  5. HOMER (http://homer.ucsd.edu/homer/)
  6. Python (https://www.python.org/downloads/) with the following libraries:
  • numpy
  • pandas
  • sklearn
  • matplotlib
pip install numpy pandas scikit-learn matplotlib

conda install --upgrade numpy pandas scikit-learn matplotlib

Please ensure the following commands are available in terminal:

  1. java -jar
  2. samtools
  3. macs2
  4. findMotifsGenome.pl
  5. annotatePeaks.pl

Note: python can be configured in the PEAS GUI.

Running PEAS

To run PEAS, download and extract the latest PEAS zip file (https://github.com/UcarLab/PEAS/releases) and run the PEAS.jar file either by double clicking or by running it in the command line: java -jar PEAS.jar (Requires Java 1.8.0_171, https://java.com/en/download/),

Please refer to the Manual (PEASManual.pdf) for installing dependencies and for further information on how to run feature extraction and prediction scripts.

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