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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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PEAS (Predict Enhancers from ATAC-seq)

NEW: Singularity image file (.sif)

The latest release (v1.2) provides a singularity image file and definition. Documentation for using this image can be found in the /singularity/ folder. This image sets up the environment to successfully run PEAS (after configuring the reference genomes) while reducing the number of user inputs for making promoter & enhancer predictions.

Note: Please have enough disk space for 5x the size of the input bamfile.

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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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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