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View Code? Open in Web Editor NEWIntell_Pred is a Support Vector Machine-based classifier to predict the intelligence relate proteins based on 12669 meaningful protein sequence features.
Intell_Pred is a Support Vector Machine-based classifier to predict the intelligence relate proteins based on 12669 meaningful protein sequence features.
=============================================================================================== = = = Intelligence Protein Predictor by: = = = = Mohammad Reza Bakhtiarizadeh = = Mohammad Sadegh Vafaei = = Aida Shomali = = = = = = University of Tehran = = = = Contact: [email protected] = = = = Usage: /path/to/Intell_Pred options /path/to/sequences.fasta = = = =============================================================================================== 1) Introduction Intell_Pred is a Support Vector Machine-based classifier to predict the intelligence relate proteins based on 12669 meaningful protein sequence features. It takes protein/DNA FASTA sequences as input, and generate output about the potential of a protein to be involved in learning or memory, which are the most important components of intelligence. Intell_Pred depends on two programs (libsvm and iLearn) and can be run on Linux. Also, it use TransDecoder software to convert the DNA sequences (mRNA transcripts that converted to DNA) to protein. Moreover, Intell_Pred only consider protein sequences larger than 60 amino acids. 2) Pre-requisite It just need python3 software to be installed in your system. Also, user should make sure all t he following packages are installed in their Python environment: sys, os, shutil, scipy, argparse, collections, platform, math, re, numpy (1.13.1), sklearn (0.19.1), matplotlib (2.1.0), and pandas (0.20.1). These python packages are needed for iLearn software (https://github.com/Superzchen/iLearn). 3) Install dependencies Drag install.sh file to terminal for automatic installing all of the dependencies. This will build and install the libsvm, TransDecoder and iLearn software. $ git clone https://github.com/mrb20045/Intell_Pred $ cd Intell_Pred/ $ chmod 777 /full/path/to/install.sh $ /full/path/to/install.sh 4) Run Intell_Pred $ /full/path/to/Intell_Pred /full/path/to/Candidates.fa 5) Output The results will be stored in Intell_Pred_Results(name of yout input).txt. An example of Intell_Pred output is presented here. The score represents a protein's probability of belonging to the learning or memory classes. Intell_Pred applied a probability score >0.5 to designate putative related protein. ############################ # Intell_Pred Results # # # # 19/01/2020 16:16:03 # ############################ Total number of processed sequences: 3 ______________________________________________________________________________________________________________________________________________________ Protein_ID Intelligence (Score) Type Learning_Score Memory_Score ______________________________________________________________________________________________________________________________________________________ sp|P20272|CNR1_RAT Yes (0.99) Memory 0.87 0.99 ______________________________________________________________________________________________________________________________________________________ sp|Q920P3|BRNP1_MOUSE Yes (0.99) Memory 0.90 0.99 ______________________________________________________________________________________________________________________________________________________ sp|P43527|CASP1_RAT Yes (0.88) Memory 0.50 0.52 ______________________________________________________________________________________________________________________________________________________ Summary of the Resuts _____________________________________________________________ Title Score>0.5 Score>0.75 Score>0.90 _____________________________________________________________ Intelligence 3 3 3 Learning 2 2 0 Memory 3 2 2 _____________________________________________________________
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