# **PlantDRPpred**
A computational method to predict the plant disease resistance protein based on evolutionary profiles.
## Introduction
PlantDRPpred is a tool developed by Raghava-Lab in 2024. It is designed to predict whether a plant protein is Disease Resistant or not. It utilizes amino-acid compositions with XGBoost Classifier and PSSM as features to make predictions using an Random Forest Classifier. PlantDRPpred is also available as web-server at https://webs.iiitd.edu.in/raghava/plantdrppred. Please read/cite the content about the PlantDRPpred for complete information including algorithm behind the approach.
## PIP Installation
PIP version is also available for easy installation and usage of this tool. The following command is required to install the package
```
pip install plantdrppred
```
To know about the available option for the pip package, type the following command:
```
plantdrppred -h
```
# Standalone
Standalone version of PlantDRPpred is written in python3 and the following libraries are necessary for a successful run:
- scikit-learn = 1.3.2
- Pandas
- Numpy
- blastp
## Minimum USAGE
To know about the available option for the standalone, type the following command:
```
python plantdrppred.py -h
```
To run the example, type the following command:
```
python plantdrppred.py -i example_input.fasata
```
This will predict the probability whether a submitted sequence will PDR or non-PDR. It will use other parameters by default. It will save the output in "outfile.csv" in CSV (comma separated variables).
## Full Usage
```
usage: plantdrppred.py [-h] -i INPUT [-o OUTPUT] [-t THRESHOLD] [-m {1,2}] [-d {1,2}]
[-wd WORKING]
=======
```
To run the example, type the following command:
```
plantdrppred.py -i example_input.fasta
```
```
Please provide following arguments.
=======
Following is complete list of all options, you may get these options
usage: plantdrppred.py [-h]
[-i INPUT]
[-o OUTPUT]
[-m {1,2}]
```
```
Please provide following arguments
optional arguments:
options:
-h, --help show this help message and exit
-i INPUT, --input INPUT
Input: protein sequence in FASTA format
-o OUTPUT, --output OUTPUT
Output: File for saving results by default outfile.csv
-t THRESHOLD, --threshold THRESHOLD
Threshold: Value between 0 to 1 by default 0.50
-m {1,2}, --model {1,2}
Model: 1: PSSM feature based Random Forest Classifier , 2: PSSM
feature based Random Forest + BLAST , by default 1
-d {1,2}, --display {1,2}
Display: 1: PDR, 2: All proteins, by default 2
-wd WORKING, --working WORKING
Working Directory: Temporary directory to write files
```
**Input File:** It allow users to provide input in the FASTA format.
**Output File:** Program will save the results in the CSV format, in case user does not provide output file name, it will be stored in "outfile.csv".
**Threshold:** User should provide threshold between 0 and 1, by default its 0.5.
**Display type:** This option allow users to display only PDR proteins or all the input proteins.
**Working Directory:** Directory where intermediate files as well as final results will be saved
PlantDRPpred Package Files
=======================
It contains the following files, brief description of these files given below
LICENSE : License information
README.md : This file provide information about this package
blastdb : The folder contain blast database of all sequences in dataset
model : This folder contains two pickled models
ncbi_blast_2.15 : This folder contains blast psiblast and blastp(for linux)
plantdrppred.py : Main python program
possum : This folder contains the program POSSUM, that is used to calculate PSSM features
example_input.fasta : Example file containing protein sequences in FASTA format
example_output.csv : Example output file for the program
Raw data
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"description": "# **PlantDRPpred**\nA computational method to predict the plant disease resistance protein based on evolutionary profiles. \n## Introduction\nPlantDRPpred is a tool developed by Raghava-Lab in 2024. It is designed to predict whether a plant protein is Disease Resistant or not. It utilizes amino-acid compositions with XGBoost Classifier and PSSM as features to make predictions using an Random Forest Classifier. PlantDRPpred is also available as web-server at https://webs.iiitd.edu.in/raghava/plantdrppred. Please read/cite the content about the PlantDRPpred for complete information including algorithm behind the approach.\n\n## PIP Installation\nPIP version is also available for easy installation and usage of this tool. The following command is required to install the package \n```\npip install plantdrppred\n```\nTo know about the available option for the pip package, type the following command:\n```\nplantdrppred -h\n```\n# Standalone\n\nStandalone version of PlantDRPpred is written in python3 and the following libraries are necessary for a successful run:\n\n- scikit-learn = 1.3.2\n- Pandas\n- Numpy\n- blastp\n\n\n## Minimum USAGE\nTo know about the available option for the standalone, type the following command:\n```\npython plantdrppred.py -h\n```\nTo run the example, type the following command:\n```\npython plantdrppred.py -i example_input.fasata\n```\nThis will predict the probability whether a submitted sequence will PDR or non-PDR. It will use other parameters by default. It will save the output in \"outfile.csv\" in CSV (comma separated variables).\n\n## Full Usage\n```\nusage: plantdrppred.py [-h] -i INPUT [-o OUTPUT] [-t THRESHOLD] [-m {1,2}] [-d {1,2}]\n [-wd WORKING]\n=======\n```\nTo run the example, type the following command:\n```\nplantdrppred.py -i example_input.fasta\n\n```\n```\nPlease provide following arguments.\n=======\nFollowing is complete list of all options, you may get these options\nusage: plantdrppred.py [-h] \n [-i INPUT]\n [-o OUTPUT]\n [-m {1,2}] \n```\n```\nPlease provide following arguments\n\noptional arguments:\n\noptions:\n -h, --help show this help message and exit\n -i INPUT, --input INPUT\n Input: protein sequence in FASTA format\n -o OUTPUT, --output OUTPUT\n Output: File for saving results by default outfile.csv\n -t THRESHOLD, --threshold THRESHOLD\n Threshold: Value between 0 to 1 by default 0.50\n -m {1,2}, --model {1,2}\n Model: 1: PSSM feature based Random Forest Classifier , 2: PSSM\n feature based Random Forest + BLAST , by default 1\n -d {1,2}, --display {1,2}\n Display: 1: PDR, 2: All proteins, by default 2\n -wd WORKING, --working WORKING\n Working Directory: Temporary directory to write files\n```\n\n**Input File:** It allow users to provide input in the FASTA format.\n\n**Output File:** Program will save the results in the CSV format, in case user does not provide output file name, it will be stored in \"outfile.csv\".\n\n**Threshold:** User should provide threshold between 0 and 1, by default its 0.5.\n\n**Display type:** This option allow users to display only PDR proteins or all the input proteins.\n\n**Working Directory:** Directory where intermediate files as well as final results will be saved\n\nPlantDRPpred Package Files\n=======================\nIt contains the following files, brief description of these files given below\n\n\nLICENSE\t\t\t\t : License information\n\nREADME.md\t\t\t : This file provide information about this package\n\nblastdb : The folder contain blast database of all sequences in dataset \n\nmodel : This folder contains two pickled models\n\nncbi_blast_2.15 : This folder contains blast psiblast and blastp(for linux) \n\nplantdrppred.py : Main python program\n\npossum : This folder contains the program POSSUM, that is used to calculate PSSM features\n\nexample_input.fasta : Example file containing protein sequences in FASTA format\n\nexample_output.csv\t: Example output file for the program\n",
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