def test_mutant_amino_acids_in_mm10_chr9_82927102_refGT_altTG_pT441H():
# In the Isovar repository this test is weird because the VCF only
# mentions the G>T variant but doesn't include the subsequent nucleotide
# change T>G. To avoid having to think about phasing of variants I changed
# the VCF in vaxrank to contain a GT>TG variant.
arg_parser = make_variant_sequences_arg_parser()
args = arg_parser.parse_args([
"--vcf", data_path("b16.f10/b16.f10.Phip.vcf"),
"--bam", data_path("b16.f10/b16.combined.sorted.bam"),
])
reads_generator = allele_reads_generator_from_args(args)
ranked_list, _ = ranked_vaccine_peptides(
reads_generator=reads_generator,
mhc_predictor=RandomBindingPredictor(["H-2-Kb", "H-2-Db"]),
vaccine_peptide_length=15,
padding_around_mutation=5,
min_alt_rna_reads=1,
min_variant_sequence_coverage=1,
variant_sequence_assembly=True,
max_vaccine_peptides_per_variant=1)
for variant, vaccine_peptides in ranked_list:
vaccine_peptide = vaccine_peptides[0]
mutant_protein_fragment = vaccine_peptide.mutant_protein_fragment
check_mutant_amino_acids(
variant,
mutant_protein_fragment)
def test_keep_top_k_epitopes():
arg_parser = make_variant_sequences_arg_parser()
args = arg_parser.parse_args([
"--vcf", data_path("b16.f10/b16.f10.Phip.vcf"),
"--bam", data_path("b16.f10/b16.combined.sorted.bam"),
])
reads_generator = allele_reads_generator_from_args(args)
keep_k_epitopes = 3
ranked_list, _ = ranked_vaccine_peptides(
reads_generator=reads_generator,
mhc_predictor=RandomBindingPredictor(["H-2-Kb", "H-2-Db"]),
vaccine_peptide_length=15,
padding_around_mutation=5,
min_alt_rna_reads=1,
min_variant_sequence_coverage=1,
variant_sequence_assembly=True,
max_vaccine_peptides_per_variant=1,
num_mutant_epitopes_to_keep=keep_k_epitopes)
for variant, vaccine_peptides in ranked_list:
vaccine_peptide = vaccine_peptides[0]
eq_(keep_k_epitopes, len(vaccine_peptide.mutant_epitope_predictions))
# recompute the expected score, make sure the top-k argument from ranked_vaccine_peptides()
# propagated as expected
mutant_epitope_score = sum(
p.logistic_epitope_score() for p in vaccine_peptide.mutant_epitope_predictions)
assert_almost_equal(mutant_epitope_score, vaccine_peptide.mutant_epitope_score)
def test_mutant_amino_acids_in_mm10_chrX_8125624_refC_altA_pS460I():
# there are two co-occurring variants in the RNAseq data but since
# they don't happen in the same codon then we're considering the Varcode
# annotation to be correct
# TODO: deal with phasing of variants explicitly so that both
# variant positions are considered mutated
arg_parser = make_variant_sequences_arg_parser()
args = arg_parser.parse_args([
"--vcf", data_path("b16.f10/b16.f10.Wdr13.vcf"),
"--bam", data_path("b16.f10/b16.combined.sorted.bam"),
])
reads_generator = allele_reads_generator_from_args(args)
ranked_list, _ = ranked_vaccine_peptides(
reads_generator=reads_generator,
mhc_predictor=RandomBindingPredictor(["H-2-Kb", "H-2-Db"]),
vaccine_peptide_length=15,
padding_around_mutation=5,
max_vaccine_peptides_per_variant=1,
min_alt_rna_reads=1,
min_variant_sequence_coverage=1,
variant_sequence_assembly=True)
for variant, vaccine_peptides in ranked_list:
eq_(
1,
len(vaccine_peptides),
"Expected 1 vaccine peptide for variant '%s' but got %d" % (
variant, len(vaccine_peptides)))
vaccine_peptide = vaccine_peptides[0]
mutant_protein_fragment = vaccine_peptide.mutant_protein_fragment
check_mutant_amino_acids(variant, mutant_protein_fragment)
def test_mutant_amino_acids_in_mm10_chr9_82927102_refGT_altTG_pT441H():
# In the Isovar repository this test is weird because the VCF only
# mentions the G>T variant but doesn't include the subsequent nucleotide
# change T>G. To avoid having to think about phasing of variants I changed
# the VCF in vaxrank to contain a GT>TG variant.
arg_parser = make_variant_sequences_arg_parser()
args = arg_parser.parse_args([
"--vcf",
data_path("b16.f10/b16.f10.Phip.vcf"),
"--bam",
data_path("b16.f10/b16.combined.sorted.bam"),
])
reads_generator = allele_reads_generator_from_args(args)
ranked_list = ranked_vaccine_peptides(reads_generator=reads_generator,
mhc_predictor=RandomBindingPredictor(
["H-2-Kb", "H-2-Db"]),
vaccine_peptide_length=15,
padding_around_mutation=5,
min_alt_rna_reads=1,
min_variant_sequence_coverage=1,
variant_sequence_assembly=True,
max_vaccine_peptides_per_variant=1)
for variant, vaccine_peptides in ranked_list:
vaccine_peptide = vaccine_peptides[0]
mutant_protein_fragment = vaccine_peptide.mutant_protein_fragment
check_mutant_amino_acids(variant, mutant_protein_fragment)
def test_mutant_amino_acids_in_mm10_chrX_8125624_refC_altA_pS460I():
# there are two co-occurring variants in the RNAseq data but since
# they don't happen in the same codon then we're considering the Varcode
# annotation to be correct
# TODO: deal with phasing of variants explicitly so that both
# variant positions are considered mutated
arg_parser = make_variant_sequences_arg_parser()
args = arg_parser.parse_args([
"--vcf",
data_path("b16.f10/b16.f10.Wdr13.vcf"),
"--bam",
data_path("b16.f10/b16.combined.sorted.bam"),
])
reads_generator = allele_reads_generator_from_args(args)
ranked_list = ranked_vaccine_peptides(reads_generator=reads_generator,
mhc_predictor=RandomBindingPredictor(
["H-2-Kb", "H-2-Db"]),
vaccine_peptide_length=15,
padding_around_mutation=5,
max_vaccine_peptides_per_variant=1,
min_alt_rna_reads=1,
min_variant_sequence_coverage=1,
variant_sequence_assembly=True)
for variant, vaccine_peptides in ranked_list:
eq_(
1, len(vaccine_peptides),
"Expected 1 vaccine peptide for variant '%s' but got %d" %
(variant, len(vaccine_peptides)))
vaccine_peptide = vaccine_peptides[0]
mutant_protein_fragment = vaccine_peptide.mutant_protein_fragment
check_mutant_amino_acids(variant, mutant_protein_fragment)
def test_keep_top_k_epitopes():
arg_parser = make_variant_sequences_arg_parser()
args = arg_parser.parse_args([
"--vcf",
data_path("b16.f10/b16.f10.Phip.vcf"),
"--bam",
data_path("b16.f10/b16.combined.sorted.bam"),
])
reads_generator = allele_reads_generator_from_args(args)
keep_k_epitopes = 3
ranked_list, _ = ranked_vaccine_peptides(
reads_generator=reads_generator,
mhc_predictor=RandomBindingPredictor(["H-2-Kb", "H-2-Db"]),
vaccine_peptide_length=15,
padding_around_mutation=5,
min_alt_rna_reads=1,
min_variant_sequence_coverage=1,
variant_sequence_assembly=True,
max_vaccine_peptides_per_variant=1,
num_mutant_epitopes_to_keep=keep_k_epitopes)
for variant, vaccine_peptides in ranked_list:
vaccine_peptide = vaccine_peptides[0]
eq_(keep_k_epitopes, len(vaccine_peptide.mutant_epitope_predictions))
# recompute the expected score, make sure the top-k argument from ranked_vaccine_peptides()
# propagated as expected
mutant_epitope_score = sum(
p.logistic_epitope_score()
for p in vaccine_peptide.mutant_epitope_predictions)
assert_almost_equal(mutant_epitope_score,
vaccine_peptide.mutant_epitope_score)
def new_run_arg_parser():
# inherit commandline options from Isovar
arg_parser = make_variant_sequences_arg_parser(
prog="vaxrank",
description=(
"Select personalized vaccine peptides from cancer variants, "
"expression data, and patient HLA type."),
)
add_translation_args(arg_parser)
add_mhc_args(arg_parser)
add_vaccine_peptide_args(arg_parser)
add_output_args(arg_parser)
add_optional_output_args(arg_parser)
add_supplemental_report_args(arg_parser)
return arg_parser
def new_run_arg_parser():
# inherit commandline options from Isovar
arg_parser = make_variant_sequences_arg_parser(
prog="vaxrank",
description=(
"Select personalized vaccine peptides from cancer variants, "
"expression data, and patient HLA type."),
)
add_translation_args(arg_parser)
add_mhc_args(arg_parser)
add_vaccine_peptide_args(arg_parser)
add_output_args(arg_parser)
add_optional_output_args(arg_parser)
add_supplemental_report_args(arg_parser)
return arg_parser
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function, division, absolute_import
import logging
import logging.config
import pkg_resources
from isovar.cli.variant_sequences import make_variant_sequences_arg_parser, variant_sequences_dataframe_from_args
logging.config.fileConfig(pkg_resources.resource_filename("isovar.cli", "logging.conf"))
logger = logging.getLogger(__name__)
parser = make_variant_sequences_arg_parser(add_sequence_length_arg=True)
parser.add_argument(
"--output",
default="isovar-variant-sequences-results.csv",
help="Name of CSV file which contains predicted sequences",
)
if __name__ == "__main__":
args = parser.parse_args()
logger.info(args)
df = variant_sequences_dataframe_from_args(args)
logger.info(df)
df.to_csv(args.output)
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function, division, absolute_import
import logging
import logging.config
import pkg_resources
from isovar.cli.variant_sequences import (
make_variant_sequences_arg_parser,
variant_sequences_dataframe_from_args
)
logging.config.fileConfig(
pkg_resources.resource_filename('isovar.cli', 'logging.conf'))
logger = logging.getLogger(__name__)
parser = make_variant_sequences_arg_parser(add_sequence_length_arg=True)
parser.add_argument(
"--output",
default="isovar-variant-sequences-results.csv",
help="Name of CSV file which contains predicted sequences")
if __name__ == "__main__":
args = parser.parse_args()
logger.info(args)
df = variant_sequences_dataframe_from_args(args)
logger.info(df)
df.to_csv(args.output)