def get_annotations(
self, mutated_peptide_mhci: PredictedEpitope, mutated_peptide_mhcii: PredictedEpitope) -> List[Annotation]:
"""
returns IEDB immunogenicity for best predicted MHC I (based on affinity) and MHC II (based on affinity) epitopes
"""
iedb = None
iedb_mhcii = None
if mutated_peptide_mhci and mutated_peptide_mhci.peptide:
iedb = self.calculate_iedb_immunogenicity(
peptide=mutated_peptide_mhci.peptide,
mhc_allele=mutated_peptide_mhci.hla,
mhc_score=mutated_peptide_mhci.affinity_score,
)
if mutated_peptide_mhcii and mutated_peptide_mhcii.peptide:
iedb_mhcii = self.calculate_iedb_immunogenicity(
peptide=mutated_peptide_mhcii.peptide,
mhc_allele=mutated_peptide_mhcii.hla,
mhc_score=mutated_peptide_mhcii.affinity_score,
)
annotations = [
AnnotationFactory.build_annotation(
value=iedb,
name="IEDB_Immunogenicity_MHCI",
),
AnnotationFactory.build_annotation(
name="IEDB_Immunogenicity_MHCII",
value=iedb_mhcii
)
]
return annotations
def get_annotations(self) -> List[Annotation]:
return [
AnnotationFactory.build_annotation(value=self.total_binding_score,
name="vaxrank_binding_score"),
AnnotationFactory.build_annotation(value=self.ranking_score,
name="vaxrank_total_score"),
]
def get_annotations(
self, mutated_peptide_mhci: PredictedEpitope,
mutated_peptide_mhcii: PredictedEpitope) -> List[Annotation]:
"""
returns dissimilarity for MHC I (affinity) MHC II (affinity)
"""
dissimilarity_mhci = None
dissimilarity_mhcii = None
if mutated_peptide_mhci and mutated_peptide_mhci.peptide:
dissimilarity_mhci = self.calculate_dissimilarity(
mutated_peptide=mutated_peptide_mhci.peptide,
mhc_affinity=mutated_peptide_mhci.affinity_score)
if mutated_peptide_mhcii and mutated_peptide_mhcii.peptide:
dissimilarity_mhcii = self.calculate_dissimilarity(
mutated_peptide=mutated_peptide_mhcii.peptide,
mhc_affinity=mutated_peptide_mhcii.affinity_score)
annotations = [
AnnotationFactory.build_annotation(
value=dissimilarity_mhci,
name="Dissimilarity_MHCI",
),
AnnotationFactory.build_annotation(
value=dissimilarity_mhcii,
name="Dissimilarity_MHCII",
)
]
return annotations
def get_annotations(self, mutated_peptide_mhci: PredictedEpitope,
mutated_peptide_mhcii: PredictedEpitope, amplitude,
mutation_in_anchor) -> List[Annotation]:
pathogen_similarity_9mer = None
pathogen_similarity_mhcii = None
recognition_potential = None
if mutated_peptide_mhci and mutated_peptide_mhci.peptide:
pathogen_similarity_9mer = self.get_pathogen_similarity(
peptide=mutated_peptide_mhci.peptide)
if pathogen_similarity_9mer is not None:
recognition_potential = self.calculate_recognition_potential(
amplitude=amplitude,
pathogen_similarity=pathogen_similarity_9mer,
mutation_in_anchor=mutation_in_anchor,
mhc_affinity_mut=mutated_peptide_mhci.affinity_score,
)
if mutated_peptide_mhcii and mutated_peptide_mhcii.peptide:
pathogen_similarity_mhcii = self.get_pathogen_similarity(
peptide=mutated_peptide_mhcii.peptide)
annotations = [
AnnotationFactory.build_annotation(
name="Pathogensimiliarity_MHCI_9mer",
value=pathogen_similarity_9mer,
),
AnnotationFactory.build_annotation(
name="Recognition_Potential_MHCI_9mer",
value=recognition_potential),
AnnotationFactory.build_annotation(
name="Pathogensimiliarity_MHCII",
value=pathogen_similarity_mhcii),
]
return annotations
def _get_positions_and_mutation_in_anchor(self, mutation):
"""
returns if mutation is in anchor position for best affinity epitope over all lengths and best 9mer affinity
"""
position_9mer = None
mutation_in_anchor_9mer = None
if self.best_ninemer_epitope_by_affinity.peptide and mutation.wild_type_xmer:
position_9mer = EpitopeHelper.position_of_mutation_epitope(
wild_type=self.best_ninemer_wt_epitope_by_affinity.peptide,
mutation=self.best_ninemer_epitope_by_affinity.peptide,
)
mutation_in_anchor_9mer = EpitopeHelper.position_in_anchor_position(
position_mhci=position_9mer,
peptide_length=len(
self.best_ninemer_epitope_by_affinity.peptide),
)
annotations = [
AnnotationFactory.build_annotation(
value=position_9mer,
name="Best_affinity_MHCI_9mer_position_mutation"),
AnnotationFactory.build_annotation(
value=mutation_in_anchor_9mer,
name="Best_affinity_MHCI_9mer_anchor_mutated",
),
]
return annotations
def test_booleans(self):
self.assertEqual(
"1",
AnnotationFactory.build_annotation("test", True).value)
self.assertEqual(
"0",
AnnotationFactory.build_annotation("test", False).value)
def get_annotation(
self, mutated_peptide_mhci: PredictedEpitope,
mutated_peptide_mhcii: PredictedEpitope) -> List[Annotation]:
"""
wrapper function for HEX (Homology evaluation of Xenopeptides) (Chiaro et al., 2021)
"""
# TODO: add annotation of b score when re-implemented in python. The annotation is too slow for bigger datasets
hex_aln_score_mhci = None
hex_aln_score_mhcii = None
# hex_b_score = None
if mutated_peptide_mhci and mutated_peptide_mhci.peptide:
# hex_aln_score, hex_b_score = self.apply_hex(netmhcpan.best_epitope_by_affinity.peptide).split(" ")
hex_aln_score_mhci = self.apply_hex(mutated_peptide_mhci.peptide)
if mutated_peptide_mhcii and mutated_peptide_mhcii.peptide:
hex_aln_score_mhcii = self.apply_hex(mutated_peptide_mhcii.peptide)
annotations = [
AnnotationFactory.build_annotation(
value=hex_aln_score_mhci, name="Hex_alignment_score_MHCI"),
AnnotationFactory.build_annotation(
value=hex_aln_score_mhcii, name="Hex_alignment_score_MHCII")
# AnnotationFactory.build_annotation(
# value=hex_b_score, name="hex_B_score"
#)
]
return annotations
def test_strings(self):
# TODO: I am wondering if we should transform this to NA. Franziska what do you think?
self.assertEqual("",
AnnotationFactory.build_annotation("test", "").value)
self.assertEqual(
"blabla",
AnnotationFactory.build_annotation("test", "blabla").value)
def get_annotations(self, mutated_peptide_mhci: PredictedEpitope,
amplitude: Amplitude) -> List[Annotation]:
bdg_cutoff_classical_mhci = 50
bdg_cutoff_alternative_mhci = 5000
amplitude_cutoff_mhci = 10
cdn = None
adn = None
if mutated_peptide_mhci.peptide:
cdn = self.classify_adn_cdn(
score_mutation=mutated_peptide_mhci.affinity_score,
amplitude=amplitude.amplitude_mhci_affinity,
bdg_cutoff_classical=bdg_cutoff_classical_mhci,
bdg_cutoff_alternative=bdg_cutoff_alternative_mhci,
amplitude_cutoff=amplitude_cutoff_mhci,
category="CDN",
)
adn = self.classify_adn_cdn(
score_mutation=mutated_peptide_mhci.affinity_score,
amplitude=amplitude.amplitude_mhci_affinity,
bdg_cutoff_classical=bdg_cutoff_classical_mhci,
bdg_cutoff_alternative=bdg_cutoff_alternative_mhci,
amplitude_cutoff=amplitude_cutoff_mhci,
category="ADN",
)
annotations = [
AnnotationFactory.build_annotation(name="CDN_MHCI", value=cdn),
AnnotationFactory.build_annotation(name="ADN_MHCI", value=adn),
]
return annotations
def get_annotations(self) -> List[Annotation]:
return [
AnnotationFactory.build_annotation(
value=self.amplitude_mhci_affinity_9mer,
name="Amplitude_MHCI_affinity_9mer",
),
AnnotationFactory.build_annotation(
value=self.amplitude_mhci_affinity,
name="Amplitude_MHCI_affinity"),
]
def test_float_values_precision_limit(self):
self.assertEqual(
"0.12346",
AnnotationFactory.build_annotation("test", 0.123456789).value)
self.assertEqual(
"0.12346",
AnnotationFactory.build_annotation("test", 0.1234567891234).value)
self.assertEqual(
"0.12345",
AnnotationFactory.build_annotation("test", 0.12345).value)
self.assertEqual(
"0.123",
AnnotationFactory.build_annotation("test", 0.123).value)
def get_annotations(self, mhc: List[Mhc2], mutation: Mutation,
uniprot) -> List[Annotation]:
best_peptide, best_rank, best_allele = self.run(mhc=mhc,
mutation=mutation,
uniprot=uniprot)
return [
AnnotationFactory.build_annotation(
value=best_peptide, name="MixMHC2pred_best_peptide"),
AnnotationFactory.build_annotation(value=best_rank,
name="MixMHC2pred_best_rank"),
AnnotationFactory.build_annotation(value=best_allele,
name="MixMHC2pred_best_allele"),
]
def get_annotations(self,
sequence_not_in_uniprot: bool) -> List[Annotation]:
return [
AnnotationFactory.build_annotation(
name="mutation_not_found_in_proteome",
value=sequence_not_in_uniprot)
]
def get_annnotations(
self, mutated_peptide_mhci: PredictedEpitope,
wt_peptide_mhci: PredictedEpitope,
mutated_peptide_mhcii: PredictedEpitope,
wt_peptide_mhcii: PredictedEpitope) -> List[Annotation]:
improved_binding_mhci = None
self_similarity_mhci = None
self_similarity_mhcii = None
if mutated_peptide_mhci and wt_peptide_mhci and \
mutated_peptide_mhci.peptide and wt_peptide_mhci.peptide:
improved_binding_mhci = self.is_improved_binder(
score_mutation=mutated_peptide_mhci.rank,
score_wild_type=wt_peptide_mhci.rank,
)
self_similarity_mhci = self.get_self_similarity(
mutated_peptide=mutated_peptide_mhci.peptide,
wt_peptide=wt_peptide_mhci.peptide,
)
if mutated_peptide_mhcii and wt_peptide_mhcii and \
mutated_peptide_mhcii.peptide and wt_peptide_mhcii.peptide:
self_similarity_mhcii = self.get_self_similarity(
mutated_peptide=mutated_peptide_mhcii.peptide,
wt_peptide=wt_peptide_mhcii.peptide,
)
annotations = [
AnnotationFactory.build_annotation(value=improved_binding_mhci,
name="Improved_Binder_MHCI"),
AnnotationFactory.build_annotation(value=self_similarity_mhcii,
name="Selfsimilarity_MHCII"),
AnnotationFactory.build_annotation(value=self_similarity_mhci,
name="Selfsimilarity_MHCI"),
AnnotationFactory.build_annotation(
value=self.self_similarity_of_conserved_binder_only(
is_improved_binder=improved_binding_mhci,
similarity=self_similarity_mhci,
),
name="Selfsimilarity_MHCI_conserved_binder",
),
]
return annotations
def get_annotations(
self,
netmhcpan: BestAndMultipleBinder,
mut_not_in_prot,
expr,
vaf_tum,
vaf_transcr,
) -> List[Annotation]:
"""
returns number of mismatches between best MHCI / MHC II epitopes (rank) and their corresponding WTs
"""
num_mismatches_mhc1 = None
priority_score = None
if netmhcpan.best_wt_epitope_by_rank.peptide and netmhcpan.best_epitope_by_rank.peptide:
num_mismatches_mhc1 = EpitopeHelper.number_of_mismatches(
epitope_wild_type=netmhcpan.best_wt_epitope_by_rank.peptide,
epitope_mutation=netmhcpan.best_epitope_by_rank.peptide,
)
priority_score = self.calc_priority_score(
vaf_tumor=vaf_tum,
vaf_rna=vaf_transcr,
transcript_expr=expr,
no_mismatch=num_mismatches_mhc1,
score_mut=netmhcpan.best_epitope_by_rank.rank,
score_wt=netmhcpan.best_wt_epitope_by_rank.rank,
mut_not_in_prot=mut_not_in_prot,
)
annotations = [
AnnotationFactory.build_annotation(
value=num_mismatches_mhc1, name="Number_of_mismatches_MCHI"
),
# priority score with rank score
AnnotationFactory.build_annotation(
value=priority_score,
name="Priority_score",
),
]
return annotations
def get_annotations_dai(
self, mutated_peptide_mhci: PredictedEpitope,
wt_peptide_mhcii: PredictedEpitope) -> List[Annotation]:
dai = None
if mutated_peptide_mhci.peptide and wt_peptide_mhcii.peptide:
dai = self.dai(
score_mutation=mutated_peptide_mhci.affinity_score,
score_wild_type=wt_peptide_mhcii.affinity_score,
affin_filtering=True,
)
annotations = [
AnnotationFactory.build_annotation(name="DAI_MHCI_affinity",
value=dai),
]
return annotations
def get_annotations_mhc2(self, mutated_peptide_mhcii: PredictedEpitope,
amplitude: Amplitude) -> List[Annotation]:
bdg_cutoff_classical_mhcii = 1
bdg_cutoff_alternative_mhcii = 4
amplitude_cutoff_mhcii = 4
cdn = None
adn = None
if mutated_peptide_mhcii.peptide:
cdn = self.classify_adn_cdn(
score_mutation=mutated_peptide_mhcii.rank,
amplitude=amplitude.amplitude_mhcii_rank,
bdg_cutoff_classical=bdg_cutoff_classical_mhcii,
bdg_cutoff_alternative=bdg_cutoff_alternative_mhcii,
amplitude_cutoff=amplitude_cutoff_mhcii,
category="CDN",
)
adn = self.classify_adn_cdn(
score_mutation=mutated_peptide_mhcii.rank,
amplitude=amplitude.amplitude_mhcii_rank,
bdg_cutoff_classical=bdg_cutoff_classical_mhcii,
bdg_cutoff_alternative=bdg_cutoff_alternative_mhcii,
amplitude_cutoff=amplitude_cutoff_mhcii,
category="ADN",
)
annotations = [
AnnotationFactory.build_annotation(
value=cdn,
name="CDN_MHCII",
),
AnnotationFactory.build_annotation(
value=adn,
name="ADN_MHCII",
),
]
return annotations
def get_annotations(self, neoantigen: Neoantigen,
netmhcpan: BestAndMultipleBinder) -> List[Annotation]:
# TODO: this is difficult to extend to more complex mutations (eg: MNVs, indels) as only considers first mutated
# position
tcell_predictor_score = None
if neoantigen.mutation.wild_type_xmer and netmhcpan.best_ninemer_epitope_by_affinity.peptide:
mutation_position = neoantigen.mutation.position[0]
wild_type_aminoacid = neoantigen.mutation.wild_type_xmer[
mutation_position - 1] # it is 1-based
mutated_aminoacid = neoantigen.mutation.mutated_xmer[
mutation_position - 1]
tcell_predictor_score = self._calculate_tcell_predictor_score(
gene=neoantigen.gene,
substitution=wild_type_aminoacid + mutated_aminoacid,
epitope=netmhcpan.best_ninemer_epitope_by_affinity.peptide,
score=netmhcpan.best_ninemer_epitope_by_affinity.affinity_score
)
annotations = [
AnnotationFactory.build_annotation(
value=tcell_predictor_score,
name="Tcell_predictor_score",
)
]
return annotations
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
def get_annotations(self) -> List[Annotation]:
return [
AnnotationFactory.build_annotation(
name="Expression_mutated_transcript", value=self.expression),
]
def test_integers(self):
self.assertEqual("123",
AnnotationFactory.build_annotation("test", 123).value)
self.assertEqual(
"-123",
AnnotationFactory.build_annotation("test", -123).value)
def test_none(self):
self.assertEqual(
NOT_AVAILABLE_VALUE,
AnnotationFactory.build_annotation("test", None).value)
def get_annotations(self) -> List[Annotation]:
annotations = []
if self.best_predicted_epitope_rank:
annotations.extend([
AnnotationFactory.build_annotation(
value=self.best_predicted_epitope_rank.rank,
name="Best_rank_MHCII_score",
),
AnnotationFactory.build_annotation(
value=self.best_predicted_epitope_rank.peptide,
name="Best_rank_MHCII_score_epitope",
),
AnnotationFactory.build_annotation(
value=self.best_predicted_epitope_rank.hla.name,
name="Best_rank_MHCII_score_allele",
)
])
if self.best_predicted_epitope_affinity:
annotations.extend([
AnnotationFactory.build_annotation(
value=self.best_predicted_epitope_affinity.affinity_score,
name="Best_affinity_MHCII_score",
),
AnnotationFactory.build_annotation(
value=self.best_predicted_epitope_affinity.peptide,
name="Best_affinity_MHCII_epitope",
),
AnnotationFactory.build_annotation(
value=self.best_predicted_epitope_affinity.hla.name,
name="Best_affinity_MHCII_allele",
)
])
if self.best_predicted_epitope_rank_wt:
annotations.extend([
AnnotationFactory.build_annotation(
value=self.best_predicted_epitope_rank_wt.rank,
name="Best_rank_MHCII_score_WT",
),
AnnotationFactory.build_annotation(
value=self.best_predicted_epitope_rank_wt.peptide,
name="Best_rank_MHCII_score_epitope_WT",
),
AnnotationFactory.build_annotation(
value=self.best_predicted_epitope_rank_wt.hla.name,
name="Best_rank_MHCII_score_allele_WT",
)
])
if self.best_predicted_epitope_affinity_wt:
annotations.extend([
AnnotationFactory.build_annotation(
value=self.best_predicted_epitope_affinity_wt.
affinity_score,
name="Best_affinity_MHCII_score_WT",
),
AnnotationFactory.build_annotation(
value=self.best_predicted_epitope_affinity_wt.peptide,
name="Best_affinity_MHCII_epitope_WT",
),
AnnotationFactory.build_annotation(
value=self.best_predicted_epitope_affinity_wt.hla.name,
name="Best_affinity_MHCII_allele_WT",
)
])
if self.organism == ORGANISM_HOMO_SAPIENS:
annotations.extend([
AnnotationFactory.build_annotation(value=self.phbr_ii,
name="PHBR_II")
])
annotations.extend([
# generator rate
AnnotationFactory.build_annotation(value=self.generator_rate,
name="Generator_rate_MHCII"),
AnnotationFactory.build_annotation(
value=self.generator_rate_cdn,
name="Generator_rate_CDN_MHCII"),
AnnotationFactory.build_annotation(
value=self.generator_rate_adn,
name="Generator_rate_ADN_MHCII"),
])
return annotations
def get_annotations(self, mutation) -> List[Annotation]:
annotations = []
if self.best_epitope_by_rank:
annotations.extend([
AnnotationFactory.build_annotation(
value=self.best_epitope_by_rank.rank,
name="Best_rank_MHCI_score"),
AnnotationFactory.build_annotation(
value=self.best_epitope_by_rank.peptide,
name="Best_rank_MHCI_score_epitope",
),
AnnotationFactory.build_annotation(
value=self.best_epitope_by_rank.hla.name,
name="Best_rank_MHCI_score_allele")
])
if self.best_epitope_by_affinity:
annotations.extend([
AnnotationFactory.build_annotation(
value=self.best_epitope_by_affinity.affinity_score,
name="Best_affinity_MHCI_score",
),
AnnotationFactory.build_annotation(
value=self.best_epitope_by_affinity.peptide,
name="Best_affinity_MHCI_epitope",
),
AnnotationFactory.build_annotation(
value=self.best_epitope_by_affinity.hla.name,
name="Best_affinity_MHCI_allele",
)
])
if self.best_ninemer_epitope_by_rank:
annotations.extend([
AnnotationFactory.build_annotation(
value=self.best_ninemer_epitope_by_rank.rank,
name="Best_rank_MHCI_9mer_score",
),
AnnotationFactory.build_annotation(
value=self.best_ninemer_epitope_by_rank.peptide,
name="Best_rank_MHCI_9mer_epitope",
),
AnnotationFactory.build_annotation(
value=self.best_ninemer_epitope_by_rank.hla.name,
name="Best_rank_MHCI_9mer_allele",
)
])
if self.best_ninemer_epitope_by_affinity:
annotations.extend([
AnnotationFactory.build_annotation(
value=self.best_ninemer_epitope_by_affinity.affinity_score,
name="Best_affinity_MHCI_9mer_score",
),
AnnotationFactory.build_annotation(
value=self.best_ninemer_epitope_by_affinity.hla.name,
name="Best_affinity_MHCI_9mer_allele",
),
AnnotationFactory.build_annotation(
value=self.best_ninemer_epitope_by_affinity.peptide,
name="Best_affinity_MHCI_9mer_epitope",
)
])
# wt
if self.best_wt_epitope_by_affinity:
annotations.extend([
AnnotationFactory.build_annotation(
value=self.best_wt_epitope_by_affinity.affinity_score,
name="Best_affinity_MHCI_score_WT",
),
AnnotationFactory.build_annotation(
value=self.best_wt_epitope_by_affinity.peptide,
name="Best_affinity_MHCI_epitope_WT",
),
AnnotationFactory.build_annotation(
value=self.best_wt_epitope_by_affinity.hla.name,
name="Best_affinity_MHCI_allele_WT",
)
])
if self.best_wt_epitope_by_rank:
annotations.extend([
AnnotationFactory.build_annotation(
value=self.best_wt_epitope_by_rank.rank,
name="Best_rank_MHCI_score_WT"),
AnnotationFactory.build_annotation(
value=self.best_wt_epitope_by_rank.peptide,
name="Best_rank_MHCI_score_epitope_WT",
),
AnnotationFactory.build_annotation(
value=self.best_wt_epitope_by_rank.hla.name,
name="Best_rank_MHCI_score_allele_WT",
)
])
if self.best_ninemer_wt_epitope_by_rank:
annotations.extend([
AnnotationFactory.build_annotation(
value=self.best_ninemer_wt_epitope_by_rank.rank,
name="Best_rank_MHCI_9mer_score_WT",
),
AnnotationFactory.build_annotation(
value=self.best_ninemer_wt_epitope_by_rank.peptide,
name="Best_rank_MHCI_9mer_epitope_WT",
),
AnnotationFactory.build_annotation(
value=self.best_ninemer_wt_epitope_by_rank.hla.name,
name="Best_rank_MHCI_9mer_allele_WT",
)
])
if self.best_ninemer_wt_epitope_by_affinity:
annotations.extend([
AnnotationFactory.build_annotation(
value=self.best_ninemer_wt_epitope_by_affinity.
affinity_score,
name="Best_affinity_MHCI_9mer_score_WT",
),
AnnotationFactory.build_annotation(
value=self.best_ninemer_wt_epitope_by_affinity.hla.name,
name="Best_affinity_MHCI_9mer_allele_WT",
),
AnnotationFactory.build_annotation(
value=self.best_ninemer_wt_epitope_by_affinity.peptide,
name="Best_affinity_MHCI_9mer_epitope_WT",
)
])
if self.organism == ORGANISM_HOMO_SAPIENS:
annotations.extend([
AnnotationFactory.build_annotation(value=self.phbr_i,
name="PHBR_I")
])
annotations.extend([
# generator rate
AnnotationFactory.build_annotation(value=self.generator_rate,
name="Generator_rate_MHCI"),
AnnotationFactory.build_annotation(value=self.generator_rate_cdn,
name="Generator_rate_CDN_MHCI"),
AnnotationFactory.build_annotation(value=self.generator_rate_adn,
name="Generator_rate_ADN_MHCI")
])
annotations.extend(
self._get_positions_and_mutation_in_anchor(mutation))
return annotations
def get_annotations_mhc2(self) -> List[Annotation]:
return [
AnnotationFactory.build_annotation(value=self.amplitude_mhcii_rank,
name="Amplitude_MHCII_rank")
]