def variants_to_protein_sequences_dataframe(
expressed_vcf="data/b16.f10/b16.expressed.vcf",
not_expressed_vcf="data/b16.f10/b16.not-expressed.vcf",
tumor_rna_bam="data/b16.f10/b16.combined.sorted.bam",
min_mapping_quality=0,
max_protein_sequences_per_variant=1,
variant_sequence_assembly=False):
"""
Helper function to load pair of VCFs and tumor RNA BAM
and use them to generate a DataFrame of expressed variant protein
sequences.
"""
expressed_variants = load_vcf(expressed_vcf)
not_expressed_variants = load_vcf(not_expressed_vcf)
combined_variants = VariantCollection(
list(expressed_variants) + list(not_expressed_variants))
alignment_file = load_bam(tumor_rna_bam)
read_collector = ReadCollector(min_mapping_quality=min_mapping_quality)
read_evidence_gen = read_collector.read_evidence_generator(
variants=combined_variants, alignment_file=alignment_file)
creator = ProteinSequenceCreator(
max_protein_sequences_per_variant=max_protein_sequences_per_variant,
variant_sequence_assembly=variant_sequence_assembly)
protein_sequences_generator = \
creator.protein_sequences_from_read_evidence_generator(read_evidence_gen)
df = protein_sequences_generator_to_dataframe(protein_sequences_generator)
return df, expressed_variants, combined_variants
def variants_to_protein_sequences_dataframe(
expressed_vcf="data/b16.f10/b16.expressed.vcf",
not_expressed_vcf="data/b16.f10/b16.not-expressed.vcf",
tumor_rna_bam="data/b16.f10/b16.combined.sorted.bam",
min_mapping_quality=0,
max_protein_sequences_per_variant=1,
variant_sequence_assembly=False):
"""
Helper function to load pair of VCFs and tumor RNA BAM
and use them to generate a DataFrame of expressed variant protein
sequences.
"""
expressed_variants = load_vcf(expressed_vcf)
not_expressed_variants = load_vcf(not_expressed_vcf)
combined_variants = VariantCollection(
list(expressed_variants) + list(not_expressed_variants))
samfile = load_bam(tumor_rna_bam)
allele_reads_generator = reads_overlapping_variants(
variants=combined_variants,
samfile=samfile,
min_mapping_quality=min_mapping_quality)
protein_sequences_generator = reads_generator_to_protein_sequences_generator(
allele_reads_generator,
max_protein_sequences_per_variant=max_protein_sequences_per_variant,
variant_sequence_assembly=variant_sequence_assembly)
df = protein_sequences_generator_to_dataframe(protein_sequences_generator)
return df, expressed_variants, combined_variants
def test_variants_to_protein_sequences_dataframe_protein_sequence_length():
expressed_variants = load_vcf("data/b16.f10/b16.expressed.vcf")
parser = make_protein_sequences_arg_parser()
parser.print_help()
for desired_length in range(9, 20, 3):
args = parser.parse_args([
"--vcf", data_path("data/b16.f10/b16.vcf"),
"--bam", data_path("data/b16.f10/b16.combined.sorted.bam"),
"--max-protein-sequences-per-variant", "1",
"--protein-sequence-length", str(desired_length),
])
df = protein_sequences_dataframe_from_args(args)
eq_(
len(df),
len(expressed_variants),
"Expected %d entries for protein_sequence_length=%d, got %d results: %s" % (
len(expressed_variants),
desired_length,
len(df),
df))
protein_sequences = df["amino_acids"]
print(protein_sequences)
protein_sequence_lengths = protein_sequences.str.len()
assert (protein_sequence_lengths == desired_length).all(), (
protein_sequence_lengths,)
def test_variants_to_reference_contexts_dataframe():
variants = load_vcf("data/b16.f10/b16.vcf")
assert len(variants) > 0
df = variants_to_reference_contexts_dataframe(variants, context_size=10)
print(df)
groups = df.groupby(["chr", "pos", "ref", "alt"])
# make sure we have at least one reference context for each
# of the B16 coding variants
eq_(len(groups), len(variants))
def test_variants_to_reference_contexts_dataframe():
variants = load_vcf("data/b16.f10/b16.vcf")
assert len(variants) > 0
df = variants_to_reference_contexts_dataframe(variants, context_size=10)
print(df)
groups = df.groupby(["chr", "pos", "ref", "alt"])
# make sure we have at least one reference context for each
# of the B16 coding variants
eq_(len(groups), len(variants))
def test_translate_variant_collection():
variants = load_vcf("data/b16.f10/b16.vcf")
samfile = load_bam("data/b16.f10/b16.combined.sorted.bam")
result = list(translate_variants(reads_supporting_variants(variants, samfile)))
eq_(
len(result),
4,
"Expected %d translated variants but got %d: %s" % (
len(variants),
len(result),
result))
def test_translate_variant_collection():
variants = load_vcf("data/b16.f10/b16.vcf")
samfile = load_bam("data/b16.f10/b16.combined.sorted.bam")
read_evidence_gen = ReadCollector().read_evidence_generator(
variants,
samfile)
translation_gen = ProteinSequenceCreator().translate_variants(read_evidence_gen)
translations = list(translation_gen)
eq_(
len(translations),
4,
"Expected %d translated variants but got %d: %s" % (
len(variants),
len(translations),
translations))
def test_variants_to_protein_sequences_dataframe_filtered_all_reads_by_mapping_quality(
):
# since the B16 BAM has all MAPQ=255 values then all the reads should get dropped
# if we set the minimum quality to 256
variants = load_vcf("data/b16.f10/b16.vcf")
alignment_file = load_bam("data/b16.f10/b16.combined.sorted.bam")
read_collector = ReadCollector(min_mapping_quality=256)
read_evidence_gen = read_collector.read_evidence_generator(
variants=variants, alignment_file=alignment_file)
creator = ProteinSequenceCreator(max_protein_sequences_per_variant=1, )
protein_sequences_generator = creator.protein_sequences_from_read_evidence_generator(
read_evidence_gen)
df = protein_sequences_generator_to_dataframe(protein_sequences_generator)
print(df)
eq_(len(df), 0, "Expected 0 entries, got %d: %s" % (len(df), df))
def test_variants_to_protein_sequences_dataframe_filtered_all_reads_by_mapping_quality():
# since the B16 BAM has all MAPQ=255 values then all the reads should get dropped
# if we set the minimum quality to 256
variants = load_vcf("data/b16.f10/b16.vcf")
samfile = load_bam("data/b16.f10/b16.combined.sorted.bam")
allele_reads_generator = reads_overlapping_variants(
variants=variants,
samfile=samfile,
min_mapping_quality=256)
protein_sequences_generator = reads_generator_to_protein_sequences_generator(
allele_reads_generator,
max_protein_sequences_per_variant=1)
df = protein_sequences_generator_to_dataframe(protein_sequences_generator)
print(df)
eq_(
len(df),
0,
"Expected 0 entries, got %d: %s" % (len(df), df))
def test_protein_sequence_creator_protein_length():
variants = load_vcf("data/b16.f10/b16.vcf")
alignment_file = load_bam("data/b16.f10/b16.combined.sorted.bam")
read_collector = ReadCollector()
for desired_length in [21, 15, 10]:
creator = ProteinSequenceCreator(
max_protein_sequences_per_variant=1,
protein_sequence_length=desired_length)
read_evidence_gen = read_collector.read_evidence_generator(
variants=variants, alignment_file=alignment_file)
protein_sequences_generator = creator.protein_sequences_from_read_evidence_generator(
read_evidence_gen)
df = protein_sequences_generator_to_dataframe(
protein_sequences_generator)
print(df)
protein_sequences = df["amino_acids"]
print(protein_sequences)
protein_sequence_lengths = protein_sequences.str.len()
assert (protein_sequence_lengths == desired_length).all(), (
protein_sequence_lengths, )
