Code Examples

This page is for those using the Python API. For those wanting to use the command line application see the Command line interface page.

General usage of this package is to provide quick access to binding prediction methods and perform analysis on the results. There are multiple potential applications.

imports

import epitopepredict as ep
from epitopepredict import base, sequtils, analysis, plotting

create a Predictor object

#get list of predictors
print base.predictors 
['tepitope', 'netmhciipan', 'iedbmhc1', 'iedbmhc2', 'mhcflurry', 'mhcnuggets', 'iedbbcell']
p = base.get_predictor('tepitope')

get sequence data

#get data in genbank format into a dataframe
df = sequtils.genbank2Dataframe(genbankfile, cds=True)
#get sequences from fasta file
df = sequtils.fasta2Dataframe(fastafile)

run predictions for a protein sequence

seq = ep.testsequence
label = 'myprot' #optional label for your sequence
p = base.get_predictor('tepitope')
p.predict(sequence=seq, allele='HLA-DRB1*01:01', length=11, name=label)

run predictions for multiple proteins

#run for 2 alleles and save results to savepath
alleles = ["HLA-DRB1*01:01", "HLA-DRB1*03:05"]
p = base.get_predictor('tepitope')
p.predict_proteins(df, length=11, alleles=alleles, save=True, path=savepath)

run predictions for a list of peptides

from epitopepredict import peptutils
seqs = peptutils.create_random_sequences(5000)
p = ep.get_predictor('tepitope')
x = p.predict_peptides(seqs, alleles=alleles)

run with multiple cpus

x = p.predict_peptides(seqs, alleles=alleles, cpus=4)

load previous results into a predictor

p.load(path=path) #where path stores csv files for multiple proteins
p.load(filename=file) # where file is a csv formatted file of prediction results (can be 1 or more proteins)

results analysis

#get all the binders using the current data loaded into the predictor
#default is to use percentile cutoff per allele, returns a dataframe
p.get_binders(cutoff=5)
#get binders for only one protein by top median rank
p.get_binders(name=name, cutoff=20, cutoff_method='rank')

#get all promiscuous binders, returns a dataframe
pb = p.promiscuous_binders(n=2, cutoff=5) 
#same using score cutoff
pb = p.promiscuous_binders(n=2, cutoff_method='score', cutoff=500) 
#find clusters of binders in these results
cl = analysis.findClusters(b, method, dist=9, minsize=3)