def test_translate(self):
gcg_var = "A*NADEQSEWEREVICNKNSLFTDEKFILSA*YL*G*TELESI*KQCALVQKYRA*DTEHIKSSQRGLALSSPALFYSTLPEDSRNEKHLLCGWIICNAGTRQLATFPSRHRGEIQIILSFPGRPTQ*S*SDERGQAPFTGHIHQ*LQQVSGLQACPRFCAVVDEYQEEQE*HCQTSR*I*ETC*RDLYQ*CKFLFGRPSCQGIHCLAGERPRKARFPRRGRHC*RTWPQTC*WFFL**DEHHS**SCRQGLYKLVDSDQNH*QEITISLFKIIFTTSPASHVGCLKC*VL*I*EVYSEATLLCMPINKFSFSVV*PKITNGIKFYQNIAKISALKYESARFCYFLLILDEVPQPVYI*R*NYFSMI*FVNVNYSDLTYLHYNNRRIEELVATVVKLERELSS*NLCLKNTQLSMYQRYN*IKFSSFFTIV"
self.assertTrue(
str(
next(
generate_proteins_from_transcripts(
self.w_v, to_stop=False))) == gcg_var)
def main():
model = argparse.ArgumentParser(
description='Neoepitope prediction for TargetInspector.')
model.add_argument(
'-m',
'--method',
type=str,
choices=EpitopePredictorFactory.available_methods().keys(),
default="bimas",
help='The name of the prediction method')
model.add_argument('-v',
'--vcf',
type=str,
default=None,
help='Path to the vcf input file')
model.add_argument(
'-t',
'--type',
type=str,
choices=["VEP", "ANNOVAR", "SNPEFF"],
default="VEP",
help=
'Type of annotation tool used (Variant Effect Predictor, ANNOVAR exonic gene annotation, SnpEff)'
)
model.add_argument('-p',
'--proteins',
type=str,
default=None,
help='Path to the protein ID input file (in HGNC-ID)')
model.add_argument('-minl',
'--peptide_min_length',
type=int,
default=8,
help='Minimum peptide length for epitope prediction')
model.add_argument('-maxl',
'--peptide_max_length',
type=int,
default=12,
help='Maximum peptide length for epitope prediction')
model.add_argument(
'-a',
'--alleles',
type=str,
required=True,
help='Path to the allele file (one per line in new nomenclature)')
model.add_argument(
'-r',
'--reference',
type=str,
default='GRCh38',
help='The reference genome used for variant annotation and calling.')
model.add_argument(
'-fINDEL',
'--filterINDEL',
action="store_true",
help='Filter insertions and deletions (including frameshifts)')
model.add_argument('-fFS',
'--filterFSINDEL',
action="store_true",
help='Filter frameshift INDELs')
model.add_argument('-fSNP',
'--filterSNP',
action="store_true",
help='Filter SNPs')
model.add_argument('-etk',
'--etk',
action="store_true",
help=argparse.SUPPRESS)
model.add_argument('-bind',
'--predict_bindings',
action="store_true",
help='Predict bindings')
model.add_argument('-o',
'--output',
type=str,
required=True,
help='Path to the output file')
args = model.parse_args()
martDB = MartsAdapter(biomart=MARTDBURL[args.reference.upper()])
transcript_to_genes = {}
if args.vcf is None and args.proteins is None:
sys.stderr.write(
"At least a vcf file or a protein id file has to be provided.\n")
return -1
# if vcf file is given: generate variants and filter them if HGNC IDs ar given
if args.vcf is not None:
protein_ids = []
if args.proteins is not None:
with open(args.proteins, "r") as f:
for l in f:
l = l.strip()
if l != "":
protein_ids.append(l)
if args.type == "VEP":
variants = read_variant_effect_predictor(args.vcf,
gene_filter=protein_ids)
elif args.type == "SNPEFF":
variants = read_vcf(args.vcf)[0]
else:
variants = read_annovar_exonic(args.vcf, gene_filter=protein_ids)
variants = filter(lambda x: x.type != VariationType.UNKNOWN, variants)
if args.filterSNP:
variants = filter(lambda x: x.type != VariationType.SNP, variants)
if args.filterINDEL:
variants = filter(
lambda x: x.type not in [
VariationType.INS, VariationType.DEL, VariationType.FSDEL,
VariationType.FSINS
], variants)
if args.filterFSINDEL:
variants = filter(
lambda x: x.type not in
[VariationType.FSDEL, VariationType.FSINS], variants)
if not variants:
sys.stderr.write(
"No variants left after filtering. Please refine your filtering criteria.\n"
)
return -1
epitopes = []
minlength = args.peptide_min_length
maxlength = args.peptide_max_length
prots = [
p for p in generate_proteins_from_transcripts(
generate_transcripts_from_variants(variants, martDB,
EIdentifierTypes.ENSEMBL))
]
for peplen in range(minlength, maxlength + 1):
peptide_gen = generate_peptides_from_proteins(prots, peplen)
peptides_var = [x for x in peptide_gen]
# remove peptides which are not 'variant relevant'
peptides = [
x for x in peptides_var if any(
x.get_variants_by_protein(y) for y in x.proteins.keys())
]
epitopes.extend(peptides)
for v in variants:
for trans_id, coding in v.coding.iteritems():
if coding.geneID is not None:
transcript_to_genes[trans_id] = coding.geneID
else:
transcript_to_genes[trans_id] = 'None'
# else: generate protein sequences from given HGNC IDs and then epitopes
else:
proteins = []
with open(args.proteins, "r") as f:
for l in f:
ensembl_ids = martDB.get_ensembl_ids_from_id(
l.strip(), type=EIdentifierTypes.HGNC)[0]
protein_seq = martDB.get_product_sequence(
ensembl_ids[EAdapterFields.PROTID])
if protein_seq is not None:
transcript_to_genes[ensembl_ids[
EAdapterFields.TRANSID]] = l.strip()
proteins.append(
Protein(
protein_seq,
gene_id=l.strip(),
transcript_id=ensembl_ids[EAdapterFields.TRANSID]))
epitopes = []
for length in range(args.peptide_min_length, args.peptide_max_length):
epitopes.extend(generate_peptides_from_proteins(proteins, length))
# read in allele list
alleles = read_lines(args.alleles, in_type=Allele)
# predict bindings for all found neoepitopes
if args.predict_bindings:
result = EpitopePredictorFactory(args.method).predict(epitopes,
alleles=alleles)
with open(args.output, "w") as f:
alleles = result.columns
var_column = " Variants" if args.vcf is not None else ""
f.write("Sequence\tMethod\t" + "\t".join(a.name for a in alleles) +
"\tAntigen ID\t" + var_column + "\n")
for index, row in result.iterrows():
p = index[0]
method = index[1]
proteins = ",".join(
set([
transcript_to_genes[prot.transcript_id.split(
":FRED2")[0]] for prot in p.get_all_proteins()
]))
vars_str = ""
if args.vcf is not None:
vars_str = "\t" + "|".join(
set(
prot_id.split(":FRED2")[0] + ":" + ",".join(
repr(v) for v in set(
p.get_variants_by_protein(prot_id)))
for prot_id in p.proteins.iterkeys()
if p.get_variants_by_protein(prot_id)))
f.write(
str(p) + "\t" + method + "\t" +
"\t".join("%.3f" % row[a] for a in alleles) + "\t" +
proteins + vars_str + "\n")
if args.etk:
with open(args.output.rsplit(".", 1)[0] + "_etk.tsv", "w") as g:
alleles = result.columns
g.write("Alleles:\t" + "\t".join(a.name
for a in alleles) + "\n")
for index, row in result.iterrows():
p = index[0]
proteins = " ".join(
set([
transcript_to_genes[prot.transcript_id.split(
":FRED2")[0]] for prot in p.get_all_proteins()
]))
g.write(
str(p) + "\t" + "\t".join("%.3f" % row[a]
for a in alleles) + "\t" +
proteins + "\n")
# don't predict bindings!
# different output format!
else:
with open(args.output, "w") as f:
var_column = " Variants" if args.vcf is not None else ""
f.write("Sequence\tAntigen ID\t" + var_column + "\n")
for epitope in epitopes:
p = epitope
proteins = ",".join(
set([
transcript_to_genes[prot.transcript_id.split(
":FRED2")[0]] for prot in p.get_all_proteins()
]))
vars_str = ""
if args.vcf is not None:
vars_str = "\t" + "|".join(
set(
prot_id.split(":FRED2")[0] + ":" + ",".join(
repr(v) for v in set(
p.get_variants_by_protein(prot_id)))
for prot_id in p.proteins.iterkeys()
if p.get_variants_by_protein(prot_id)))
f.write(str(p) + "\t" + proteins + vars_str + "\n")
with open(args.output.replace('.csv', '.txt'), "w") as f:
for epitope in epitopes:
f.write(str(epitope) + "\n")
return 0
def main():
model = argparse.ArgumentParser(
description='Neoepitope protein fasta generation from variant vcf')
model.add_argument('-v',
'--vcf',
type=str,
default=None,
help='Path to the vcf input file')
model.add_argument(
'-t',
'--type',
type=str,
choices=["VEP", "ANNOVAR", "SnpEff"],
default="VEP",
help=
'Type of annotation tool used (Variant Effect Predictor, ANNOVAR exonic gene annotation, SnpEff)'
)
model.add_argument('-p',
'--proteins',
type=str,
default=None,
help='Path to the protein ID input file (in HGNC-ID)')
model.add_argument(
'-r',
'--reference',
type=str,
default='GRCh38',
help='The reference genome used for varinat annotation and calling.')
model.add_argument(
'-fINDEL',
'--filterINDEL',
action="store_true",
help='Filter insertions and deletions (including frameshifts)')
model.add_argument('-fFS',
'--filterFSINDEL',
action="store_true",
help='Filter frameshift INDELs')
model.add_argument('-fSNP',
'--filterSNP',
action="store_true",
help='Filter SNPs')
model.add_argument('-o',
'--output',
type=str,
required=True,
help='Path to the output file')
args = model.parse_args()
martDB = MartsAdapter(biomart=MARTDBURL[args.reference.upper()])
if args.vcf is None:
sys.stderr.write(
"At least a vcf file or a protein id file has to be provided.\n")
return -1
# if vcf file is given: generate variants and filter them if HGNC IDs ar given
if args.vcf is not None:
protein_ids = []
if args.proteins is not None:
with open(args.proteins, "r") as f:
for l in f:
l = l.strip()
if l != "":
protein_ids.append(l)
if args.type == "VEP":
variants = read_variant_effect_predictor(args.vcf,
gene_filter=protein_ids)
elif args.type == "SNPEFF":
variants = read_vcf(args.vcf)[0]
else:
variants = read_annovar_exonic(args.vcf, gene_filter=protein_ids)
if args.filterSNP:
variants = filter(lambda x: x.type != VariationType.SNP, variants)
if args.filterINDEL:
variants = filter(
lambda x: x.type not in [
VariationType.INS, VariationType.DEL, VariationType.FSDEL,
VariationType.FSINS
], variants)
if args.filterFSINDEL:
variants = filter(
lambda x: x.type not in
[VariationType.FSDEL, VariationType.FSINS], variants)
if not variants:
sys.stderr.write(
"No variants left after filtering. Please refine your filtering criteria.\n"
)
return -1
variants = filter(lambda x: x.type != VariationType.UNKNOWN, variants)
#generate transcripts
transcripts = generate_transcripts_from_variants(
variants, martDB, EIdentifierTypes.ENSEMBL)
#generate proteins
proteins = filter(
lambda x: any(
x.get_variants_by_protein(tid)
for tid in x.proteins.iterkeys()),
generate_proteins_from_transcripts(transcripts))
#write fasta file
with open(output, "w") as f:
for p in proteins:
f.write('>' + str(p.transcript_id) + '|' + str(p.vars) +
'_var_' + '\n')
f.write(str(p) + '\n')
else:
sys.stderr.write(
"At least a vcf file or a protein id file has to be provided.\n")
return -1
return 0
def test_translate(self):
gcg_var = "A*NADEQSEWEREVICNKNSLFTDEKFILSA*YL*G*TELESI*KQCALVQKYRA*DTEHIKSSQRGLALSSPALFYSTLPEDSRNEKHLLCGWIICNAGTRQLATFPSRHRGEIQIILSFPGRPTQ*S*SDERGQAPFTGHIHQ*LQQVSGLQACPRFCAVVDEYQEEQE*HCQTSR*I*ETC*RDLYQ*CKFLFGRPSCQGIHCLAGERPRKARFPRRGRHC*RTWPQTC*WFFL**DEHHS**SCRQGLYKLVDSDQNH*QEITISLFKIIFTTSPASHVGCLKC*VL*I*EVYSEATLLCMPINKFSFSVV*PKITNGIKFYQNIAKISALKYESARFCYFLLILDEVPQPVYI*R*NYFSMI*FVNVNYSDLTYLHYNNRRIEELVATVVKLERELSS*NLCLKNTQLSMYQRYN*IKFSSFFTIV"
self.assertTrue(str(generate_proteins_from_transcripts(self.w_v, to_stop=False).next()) == gcg_var)
def main():
model = argparse.ArgumentParser(description='Neoepitope protein fasta generation from variant vcf')
model.add_argument(
'-v', '--vcf',
type=str,
default=None,
help='Path to the vcf input file'
)
model.add_argument(
'-t', '--type',
type=str,
choices=["VEP", "ANNOVAR", "SNPEFF"],
default="VEP",
help='Type of annotation tool used (Variant Effect Predictor, ANNOVAR exonic gene annotation, SnpEff)'
)
model.add_argument(
'-p','--proteins',
type=str,
default=None,
help='Path to the protein ID input file (in HGNC-ID)'
)
model.add_argument(
'-r' ,'--reference',
type=str,
default='GRCh38',
help='The reference genome used for varinat annotation and calling.'
)
model.add_argument(
'-fINDEL' ,'--filterINDEL',
action="store_true",
help='Filter insertions and deletions (including frameshifts)'
)
model.add_argument(
'-fFS' ,'--filterFSINDEL',
action="store_true",
help='Filter frameshift INDELs'
)
model.add_argument(
'-fSNP' ,'--filterSNP',
action="store_true",
help='Filter SNPs'
)
model.add_argument(
'-o','--output',
type=str,
required=True,
help='Path to the output file'
)
args = model.parse_args()
martDB = MartsAdapter(biomart=MARTDBURL[args.reference.upper()])
if args.vcf is None:
sys.stderr.write("At least a vcf file or a protein id file has to be provided.\n")
return -1
# if vcf file is given: generate variants and filter them if HGNC IDs ar given
if args.vcf is not None:
protein_ids = []
if args.proteins is not None:
with open(args.proteins, "r") as f:
for l in f:
l = l.strip()
if l != "":
protein_ids.append(l)
if args.type == "VEP":
variants = read_variant_effect_predictor(args.vcf, gene_filter=protein_ids)
elif args.type == "SNPEFF":
variants = read_vcf(args.vcf)[0]
else:
variants = read_annovar_exonic(args.vcf, gene_filter=protein_ids)
if args.filterSNP:
variants = filter(lambda x: x.type != VariationType.SNP, variants)
if args.filterINDEL:
variants = filter(lambda x: x.type not in [VariationType.INS,
VariationType.DEL,
VariationType.FSDEL,
VariationType.FSINS], variants)
if args.filterFSINDEL:
variants = filter(lambda x: x.type not in [VariationType.FSDEL, VariationType.FSINS], variants)
if not variants:
sys.stderr.write("No variants left after filtering. Please refine your filtering criteria.\n")
return -1
variants = filter(lambda x: x.type != VariationType.UNKNOWN, variants)
#generate transcripts
transcripts = generate_transcripts_from_variants(variants, martDB, EIdentifierTypes.ENSEMBL)
#generate proteins
proteins = generate_proteins_from_transcripts(transcripts)
#write fasta file
with open(args.output, "w") as f:
for p in proteins:
f.write('>' + str(p.transcript_id) + '|' + str(p.vars) + '_var_' + '\n')
f.write(str(p)+ '\n')
else:
sys.stderr.write("At least a vcf file or a protein id file has to be provided.\n")
return -1
return 0