def __init__(
self,
hla_alleles,
netmhc_command = "netMHCpan"):
self.netmhc_command = netmhc_command
try:
run_command([self.netmhc_command])
except:
assert False, "Failed to run %s" % self.netmhc_command
try:
valid_alleles_str = check_output([self.netmhc_command, "-listMHC"])
assert len(valid_alleles_str) > 0, \
"%s returned empty allele list" % self.self.netmhc_command
valid_alleles = set([])
for line in valid_alleles_str.split("\n"):
if not line.startswith("#"):
valid_alleles.add(line)
except:
logging.warning("Failed to run %s -listMHC", self.netmhc_command)
valid_alleles = None
self.alleles = []
for allele in hla_alleles:
allele = normalize_hla_allele_name(allele.strip().upper())
# for some reason netMHCpan drop the "*" in names
# such as "HLA-A*03:01" becomes "HLA-A03:01"
if valid_alleles and allele.replace("*", "") not in valid_alleles:
print "Skipping %s (not available in NetMHCpan)" % allele
else:
self.alleles.append(allele)
# don't run the MHC predictor twice for homozygous alleles,
# only run it for unique alleles
self.alleles = set(self.alleles)
def __init__(self, hla_alleles, netmhc_command="netMHCpan"):
self.netmhc_command = netmhc_command
try:
run_command([self.netmhc_command])
except:
assert False, "Failed to run %s" % self.netmhc_command
try:
valid_alleles_str = check_output([self.netmhc_command, "-listMHC"])
assert len(valid_alleles_str) > 0, \
"%s returned empty allele list" % self.self.netmhc_command
valid_alleles = set([])
for line in valid_alleles_str.split("\n"):
if not line.startswith("#"):
valid_alleles.add(line)
except:
logging.warning("Failed to run %s -listMHC", self.netmhc_command)
valid_alleles = None
self.alleles = []
for allele in hla_alleles:
allele = normalize_hla_allele_name(allele.strip().upper())
# for some reason netMHCpan drop the "*" in names
# such as "HLA-A*03:01" becomes "HLA-A03:01"
if valid_alleles and allele.replace("*", "") not in valid_alleles:
print "Skipping %s (not available in NetMHCpan)" % allele
else:
self.alleles.append(allele)
# don't run the MHC predictor twice for homozygous alleles,
# only run it for unique alleles
self.alleles = set(self.alleles)
def predict(self, df, mutation_window_size = None):
"""
Given a dataframe of mutated amino acid sequences, run each sequence
through NetMHCpan.
If mutation_window_size is not None then only make predictions for that
number residues away from mutations.
Expects the input DataFrame to have the following fields:
- SourceSequence
- MutationStart
- MutationEnd
- GeneInfo
- Gene
- GeneMutationInfo
- PeptideMutationInfo
- TranscriptId
"""
input_filename, peptide_entries = create_input_fasta_file(
df,
mutation_window_size=mutation_window_size
)
alleles_str = \
",".join(allele.replace("*", "") for allele in self.alleles)
output_file = tempfile.NamedTemporaryFile(
"r+",
prefix="netMHCpan_output",
delete=False)
command = [
self.netmhc_command,
"-xls",
"-xlsfile", output_file.name,
"-l", "9",
"-f", input_filename,
"-a", alleles_str]
print " ".join(command)
with CleanupFiles(
filenames = [input_filename],
files = [output_file]):
run_command(command)
results = parse_xls_file(
output_file.read(),
peptide_entries,
mutation_window_size=mutation_window_size)
assert len(results) > 0, "No epitopes from netMHCpan"
return pd.DataFrame.from_records(results)
def predict(self, df, mutation_window_size=None):
"""
Given a dataframe of mutated amino acid sequences, run each sequence
through NetMHCpan.
If mutation_window_size is not None then only make predictions for that
number residues away from mutations.
Expects the input DataFrame to have the following fields:
- SourceSequence
- MutationStart
- MutationEnd
- GeneInfo
- Gene
- GeneMutationInfo
- PeptideMutationInfo
- TranscriptId
"""
input_filename, peptide_entries = create_input_fasta_file(
df, mutation_window_size=mutation_window_size)
alleles_str = \
",".join(allele.replace("*", "") for allele in self.alleles)
output_file = tempfile.NamedTemporaryFile("r+",
prefix="netMHCpan_output",
delete=False)
command = [
self.netmhc_command, "-xls", "-xlsfile", output_file.name, "-l",
"9", "-f", input_filename, "-a", alleles_str
]
print " ".join(command)
with CleanupFiles(filenames=[input_filename], files=[output_file]):
run_command(command)
results = parse_xls_file(output_file.read(),
peptide_entries,
mutation_window_size=mutation_window_size)
assert len(results) > 0, "No epitopes from netMHCpan"
return pd.DataFrame.from_records(results)
