Python codes used in in Bioinformatics

This page describes about program developed by Raghava group which are commonly used for developing prediction model.


Program Purpose
fasta2sfasta Convert fasta format to single fasta format
pro2aac To calculate amino acid composition of protein
pro2aac_nt To calculate amino acid composition of N-terminal (nt) residues of a protein
pro2aac_ct To calculate amino acid composition of C-terminal (ct) residues of a protein
pro2aac_rest To calculate amino acid composition of a protein after removing N-, and C-terminal residues
pro2aac_split To calculate split amino acid composition (SSAC) of a protein
pro2dpc To calculate dipeptide composition of protein
pro2dpc_nt To calculate dipeptide composition of N-terminal (nt) residues of a protein
pro2dpc_ct To calculate dipeptide composition of C-terminal (ct) residues of a protein
pro2tpc To calculate tripeptide composition of protein
add_cols To add columns of two files
col2svm To generating SVM_light input format
col_mult To multiplying each column of input file with a number
col_mult_sel To multiplying selective columns with a number
perl col_rem To remove selective columns from a file
col_ext To extract selective columns from a file
col_corr To compute correlation co-efficient between two column
col_avg To calculate average column of two files
seq2pssm_imp To calculate PSSM matrix in column format without any normalization
pssm_n1 To normalize pssm profile based on 1/(1+e-x) formula
pssm_n2 To normalize pssm profile based on (numb -min)/(max -min) formula
pssm_n3 To normalize pssm profile based on (numb -min)*100/(max -min) formula
pssm_n4 To normalize pssm profile based on 1/(1+e-(x/100) formula
pssm_comp To compute PSSM composition (400 points)
col_sig Significance of columns in two column files
pssm2pat To generate patterns of given size from PSSM matrix
pssm_smooth To designed smooth pssm profile for plot
seq2motif To create motifs by sliding window of user defined length with option of adding terminal X
motif2bin To make binary input from the multifasta motif file
blast_similarity To perform blast


Click here to download GPSRPython package

GPSRPython

About Raghava

Professor Gajendra P.S. Raghava, Indraprasta Institute of Information Technology, New Delhi is a strong supporter of open source software and open access, all resources developed at his group are free for scientific use.