TRAPT is a novel deep learning framework for transcription regulators prediction via integraing large-scale epigenomic data.
First, download library:
Second, install TRAPT:
conda create --name TRAPT python=3.7
conda activate TRAPT
pip install TRAPT
Run TRAPT using a case:
Use shell commands:
# help
trapt --help
# run
trapt --library library \
--input ESR1@DataSet_01_111_down500.txt \
--output output/test/ESR1@DataSet_01_111_down500
Using the python interface:
import os
from TRAPT.Tools import Args, RP_Matrix
from TRAPT.Run import runTRAPT
# library path
library = 'library'
# input file path
input = 'ESR1@DataSet_01_111_down500.txt'
# output file path
output = 'output/test/ESR1@DataSet_01_111_down500'
rp_matrix = RP_Matrix(library)
args = Args(input, output)
runTRAPT([rp_matrix, args])
# Constructing TR-RP matrix
python3 CalcTRRPMatrix.py library
# Constructing H3K27ac-RP matrix
python3 CalcSampleRPMatrix.py H3K27ac library
# Constructing ATAC-RP matrix
python3 CalcSampleRPMatrix.py ATAC library
# Reconstruct TR-H3K27ac adjacency matrix
python3 DLVGAE.py H3K27ac library
# Reconstruct TR-ATAC adjacency matrix
python3 DLVGAE.py ATAC library
# Prediction (TR-H3K27ac)-RP matrix
python3 CalcTRSampleRPMatrix.py H3K27ac library
# Prediction (TR-ATAC)-RP matrix
python3 CalcTRSampleRPMatrix.py ATAC library