GSTpred: A server for the prediction of GST protein

 

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GSTPred Standalone

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**                                                                      **
** 	GSTpred: Predicting Glutathione S-transferase proteins 		**
**                                                                      **
** Developed By: Nitish Mishra, Manish Kumar and G.P.S. Raghava		**
** Indraprastha Institute of Information Technology, New Delhi, India			**
**                                                                      **
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About GSTpred
=============
Glutathione S-transferases (GST) proteins play vital role in living organism 
that includes detoxification of exogenous and endogenous chemicals, surviva-
bility during stress condition. We developed a method GSTpred for predicting 
GST proteins from their amino acid sequence. We developed SVM  models using 
amino acid, dipeptide and tripeptide composition and achieved maximum accuracy
91.59%, 95.79% and 97.66% respectively. Its web version is available from
 http://webs.iiitd.edu.in/raghava/gstpred/.

Prerequest for GSTpred
======================
GSTpred is written in PERL language, in order to run GSTpred one need to 
have or install PERL (http://www.perl.com/). It is a SVM based program 
where models have been developed using SVM_light. Thus user should have 
or install svm_classify of SVM_light from
http://www.cs.cornell.edu/People/tj/svm_light/.

Downloand and Installation
==========================
1. Download gstpred.tar.gz 
2. Unzip using command: gunzip gstpred.tar.gz
3. Extract files: tar -xf gstpred.tar
4. Change directory: cd gstpred
5. Install program : perl install.pl 
6. This will configure and create gstpred.pl program

How to Use
==========
1. Run using "gstpred.pl" or "./gstpred.pl" or "perl gstpred.pl"
2. Usage: gstpred.pl -i [input_file] -m [mode] -t [threshold]

Options: 
-i    	Input_file containing sequence(s) in the FASTA format.
-m   	Type of Composition
       	m: monopeptide
       	d: dipeptide
        t: tripeptide
-t   	 SVM threshold (from -1.00 to 1.00; default 0.0)
Files and Directory
==================
  Files                         Description
README                  Description file (This file)
install.pl              File used to install gstpred
gst_models              Directory having SVM models
gstpred_main            Base program
seq.fa                  Example sequrnce file in fast format
gstpred.pl              Created after running install.pl 

Example Usage
=============
gstpred.pl -i seq.fa -m m -t 0.0

Licence
=======
GSTpred is a standalone software package developed for predicting Glutathione 
S-transferase (GST) proteins from their amino acid sequence. This program is 
developed at Dr. Raghava's group (Indraprastha Institute of Information Technology, 
New Delhi, India) by  "Nitish Mishra, Manish Kumar and G.P.S. Raghava). 
This program remains the copyright property of the Institute of Microbial 
Technology, New Delhi, INDIA an institution of the CSIR, Govt of India. This 
program may be freely used by anybody subject to the following conditions: 

1. The authors nor the Indraprastha Institute of Information Technology New Delhi assume 
any responsibility for any losses or damage that may be caused by the use or 
misuse of the accompanying software. 

2. The authors nor the Indraprastha Institute of Information Technology New Delhi give any 
warranty with regards to the software being able to function on any computer. 

3. The accompanying software may not be copied nor distributed with any 
modifications, and this document file MUST be included with all copies 

4. No fee may be charged for the copying and/or distribution of the 
accompanying software. 

5. Users must agree to accept any risk as a condition of the free use of 
the accompanying software. 

Any suggestion, bug report will be greatly appreciated. Please send them to: 


Dr. G P S Raghava, Professor & Head Department of Computational Biology
Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India.
Email:raghava@iiitd.ac.in Web:http://webs.iiitd.edu.in/raghava/