Metadata-Version: 2.1
Name: TRAPT
Version: 0.0.1
Summary: A novel deep learning framework for transcription regulators prediction via integraing large-scale epigenomic data.
Author-email: zhangguorui <mp798378522@gmail.com>
Project-URL: Homepage, https://github.com/LicLab-bio/TRAPT
Project-URL: Bug Tracker, https://github.com/LicLab-bio/TRAPT/issues
Project-URL: Blog, http://www.licpathway.net/TRAPT
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE

### TRAPT

[TRAPT](http://www.licpathway.net/TRAPT) is a novel deep learning framework for transcription regulators prediction via integraing large-scale epigenomic data.

### Usage

First, download library: 

[TRAPT library](http://www.licpathway.net/TRAPT/download)


Second, install TRAPT:

```sh
pip install TRAPT
```

Run TRAPT using a [case](http://www.licpathway.net/TRAPT/download):

```python
from TRAPT.TRAPT import Args, RP_Matrix, runTRAPT

# library path
library = 'library'
# input file path
input = 'ESR1@DataSet_01_111_down500.txt'
# output file path
output = 'output/ESR1@DataSet_01_111_down500'

rp_matrix = RP_Matrix(library)
args = Args(input, output)
os.system(f'mkdir -p {output}')
runTRAPT([rp_matrix, args])
```

### Detail

```sh
# 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
# TRAPT predicts TR activity
python3 TRAPT.py library input output
```
