Metadata-Version: 2.1
Name: plantdrppred
Version: 0.3
Summary: A tool to predict Plant Diesease Resistance Protein
Home-page: https://github.com/raghavagps/plantdrppred
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn >=1.3.0
Requires-Dist: argparse
Requires-Dist: biopython
Requires-Dist: requests

# PlantDRPpred
A method for prediction of Plant Disease Resistance Protein

# Introduction
PlantDRPpred is developed for predicting, mapping and scanning plant resistances proteins . More information on PlantDRPpred is available from its web server http://webs.iiitd.edu.in/raghava/plantdrppred. This page provide information about standalone version of PlantDRPpred.

## 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
- Pandas
- Numpy
- blastp


**Minimum USAGE** 

To know about the available option for the standalone, type the following command:
```
PlantDRPpred.py -h
```
To run the example, type the following command:
```
PlantDRPpred.py -i seq.fasta

```
where seq.fasta is a input FASTA file. This will predict plant resistances protein in FASTA format. It will use other parameters by default. It will save output in "output_result.csv" in CSV (comma separated variables).

**Full Usage**: 
```
Following is complete list of all options, you may get these options
usage: toxinpred2.py [-h] 
                     [-i INPUT]
                     [-o OUTPUT]
                     [-m {1,2}] 
```
```
Please provide following arguments

optional arguments:

  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        Input: protein or peptide sequence in FASTA format or
                        single sequence per line in single letter code
  -o OUTPUT, --output OUTPUT
                        Output: File for saving results by default outfile.csv
  -m {1,2}, -- model Model
                        Model: 1: AAC based SVC, 2: PSSM based ET

```

**Input File**: It allow users to provide input in two format; i) FASTA format (standard) (e.g. seq.fasta)  

**Output File**: Program will save result in CSV format, in case user do not provide output file name, it will be stored in output_result.csv.


**Models**: In this program, two models have been incorporated;  
  i) Model1 for predicting given input protein sequence as R protein and non-R proteins  using SVC based on amino-acid composition of the proteins; 

  ii) Model2 for predicting given input peptide/protein sequence as R proteins and non-R protein using Hybrid approach, which is the ensemble of ET + BLAST. It combines the scores generated from machine learning (ET), and BLAST as Hybrid Score, and the prediction is based on Hybrid Score.


PlantDRPpred Package Files
=======================
It contain following files, brief description of these files given below

LICENSE       	: License information

Fea_Seq : This folder contains the gerated features 

pfeature : This folder allow to genrate AAC feature 

PSSM : This folder allow to genrate PSSM feature 

README.md     	: This file provide information about this package

PlantDRPpred.py 	: Main python program 

Models        : Model file required for running Machine-learning model

seq.fasta	: Example file contain peptide sequences in FASTA format



# Reference
.</a>
