Finds a sequence motif from a set of provided peptide sequences. Uses the Gibbs Motif Finding algorithm as outlined in the Bioinformatics Algorithms textbook: https://stepic.org/Bioinformatics-Algorithms-2/
usage: FIND_MOTIF.py inputfile outputfile k motif_file
inputfile: collection of unaligned sequences belongign to the same protein family
outputfile: filename where the motif sequences from each original sequence (from inputfile) will be outputted. This file can then be uploaded to servers such as Weblogo 3 to create motif logos.
k: length of the motif
motif_file: filename where the motif profile matrix will be outputted
From a specified proteome or list of proteomes, rank all proteins in terms of the probability of the sequence best matching a given motif profile being a genuine motif. The boxplot function generates boxplots of the proteomes, with whiskers at 3IQR and outliers indicated. Linees 157-159 (commented out) also sorts and lists the top 25 proteins according to probability.
Usage: find_most_probable.py input proteomes output
input: input motif file. Usually motif_file as outputted by FIND_MOTIF.py
proteomes: a list of the proteomes to be assessed. This should be a text file with the filenames of the fasta formatted proteins on each line. The boxplot uses these filenames for labeling the results.
output: output image file where the boxplot will be saved.
athaliana.fasta: Sample proteome of the plant Arabidopsis thaliana
spombe.fasta: Sample proteome of the fungus Schizosaccharomyces pombe
list_proteomes.txt: Sample input file for find_most_probable.py listing the proteomes to be queried
hpt_sequences.txt: sample input reference set for FIND_MOTIF.py