def _mixmhcprediction(
self, mhc_alleles: List[str], potential_ligand_sequences
) -> pd.DataFrame:
"""
Performs MixMHCpred prediction for desired hla allele and writes result to temporary file.
"""
outtmp = intermediate_files.create_temp_file(prefix="mixmhcpred", suffix=".txt")
tmpfasta = intermediate_files.create_temp_fasta(
potential_ligand_sequences, prefix="tmp_sequence_"
)
command = [
self.configuration.mix_mhc_pred,
"-a",
",".join(mhc_alleles),
"-i",
tmpfasta,
"-o",
outtmp,
]
self.runner.run_command(
cmd=command
)
try:
results = pd.read_csv(outtmp, sep="\t", comment="#")
except EmptyDataError:
message = "Results from MixMHCpred are empty, something went wrong [{}]. MHC I alleles {}, ligands {}".format(
" ".join(command), ",".join(mhc_alleles), potential_ligand_sequences
)
logger.error(message)
results = pd.DataFrame()
os.remove(outtmp)
return results
def _calculate_tcell_predictor_score(self, gene, substitution, epitope,
score):
"""returns Tcell_predictor score given mps in dictionary format"""
tmp_tcellPredIN = intermediate_files.create_temp_file(
prefix="tmp_TcellPredicIN_", suffix=".txt")
tcell_predictor_score = None
if not ModelValidator.has_peptide_rare_amino_acids(epitope):
tcell_predictor_score = self._wrapper_tcellpredictor(
gene=gene,
substitution=substitution,
epitope=epitope,
score=score,
tmpfile_in=tmp_tcellPredIN,
)
return tcell_predictor_score
def get_annotation(
self, sample_id, mutated_peptide_mhci: PredictedEpitope, wt_peptide_mhci: PredictedEpitope, peptide_variant_position, mutation
) -> Annotation:
"""wrapper function to determine neoag immunogenicity score for a mutated peptide sequence"""
neoag_score = None
if mutation.wild_type_xmer and mutated_peptide_mhci.peptide and wt_peptide_mhci.peptide:
# TODO: move this tmp file creation inside the method
tmp_file_name = intermediate_files.create_temp_file(
prefix="tmp_neoag_", suffix=".txt"
)
self._prepare_tmp_for_neoag(
sample_id,
mutated_peptide_mhci.peptide,
mutated_peptide_mhci.affinity_score,
wt_peptide_mhci.peptide,
peptide_variant_position,
tmp_file_name,
)
neoag_score = self._apply_gbm(tmp_file_name)
annotation = AnnotationFactory.build_annotation(
value=neoag_score, name="Neoag_immunogenicity"
)
return annotation