Explainable Generative Deep Learning for plant transcription factor binding sites discovery

Running the standalone program

Create & activate virtual environment and install other required dependencies

conda create -n ptfvac python=3.8.10 -y

conda activate ptfvac

chmod a+x install.sh

./install.sh

cd deeptfactor

sh meme.sh (install MEME)

Note: Always use Alphafold2 generated TF pdb file only (An example is provided). This standalone version was developed in Linux, and has been tested in Ubuntu 20.04 with Python 3.8.10.

Input

fastafile = File containing fasta sequences.

example.pdb = Alphafold2 generated example pdb file.

Running script

To detect the TF binding sites, In parent directory execute following command:

Usage: ./ptfvac "fastafile" "folderpath" "Alphafold generated PDB file" "folderpath of deeptfactor inside PTF-Vac"

eg: ./ptfvac fastafile folderpath example.pdb /home/user/ptfvac

To generate line plot, execute the following command:

python3 plot.py plot/seq1.txt (filename) (generated plots are interactive)

Output

results.txt = The TF binding site information regarding the binding site sequence and it's start and end coordinates in the sequence.

motiflogopwm.csv = Position weight matrix of binding site sequences.

motiflogopwm.pdf = Sequence logo of binding site sequences.

plots = A folder containing importance score plot.