Prophage-SOS-dependency-Predictor(PSP)

PSP is a novel bioinformatics tool to predict prophage induction modes by analyzing the heterology index (HI) of LexA protein binding to target DNA, classifying prophages into SOS-dependent (SdPs) and SOS-independent (SiPs).

Dependencies

• PSP is a Python script that relies on:

DIAMOND
MEME
Python3
scikit-learn

Installation

(1) git

git clone https://github.com/mujiezhang/PSP.git
cd PSP
python psp.py -h

(2) conda

conda create -n PSP python=3.12
conda activate PSP
conda install PSP

usage: psp -h

Input files

PSP needs four files as inputs,i.e.,

• -hf: a host genome in fasta format
• -vf: a single viral genome in fasta format
• -motif: a motif file provided by psp as 19-motifs-meme.txt
• -lexa: lexa database for diamond blastp provided by psp as uniprot_swiss_prot_LexA.dmnd

other parameters *-wd: woking path to save result files

How to run

The users can only specify the required parameters:

python psp.py -hf host-genome.fasta -vf virus-genome.fasta -motif 19-motifs-meme.txt -lexa uniprot_swiss_prot_LexA.dmnd -wd output_dir

for example:

python psp.py -hf E.coli-HS.fasta -vf phiECO1.fasta -motif 19-motifs-meme.txt -lexa uniprot_swiss_prot_LexA.dmnd -wd .

Running this example with one core takes approximately two minutes. And you will get two files: host_whole_genome_HI.tsv and prediction.tsv

Attention

• PSP is designed for complete host and corresponding complete virus for that host. Using incomplete genome as input may influence the prediction accuracy.

Citation

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