def test_eq(self):
reference = Record(None, Variant("1", 20, "A", "G"), set(), 0.0, set(),
InfoData(None, {}), SampleData([], []), False)
self.assertTrue(
reference == Record(None, Variant("1", 20, "A", "G"), set(
), 0.0, set(), InfoData(None, {}), SampleData([], []), False))
self.assertFalse(
reference == Record(None, Variant("2", 20, "A", "G"), set(
), 0.0, set(), InfoData(None, {}), SampleData([], []), False))
self.assertFalse(reference == Record(None, Variant(
"1", 20, "A", "G"), set("rs0"), 0.0, set(), InfoData(None, {}),
SampleData([], []), False))
self.assertFalse(
reference == Record(None, Variant("1", 20, "A", "G"), set(
), 5.0, set(), InfoData(None, {}), SampleData([], []), False))
self.assertFalse(
reference == Record(None, Variant("1", 20, "A", "G"), set(
), 0.0, set("CV"), InfoData(None, {}), SampleData([], []), False))
self.assertFalse(reference == Record(None, Variant(
"1", 20, "A", "G"), set(), 0.0, set(), InfoData(None, {'AF': []}),
SampleData([], []), False))
self.assertFalse(reference == Record(
None, Variant("1", 20, "A", "G"), set(), 0.0, set(),
InfoData(None, {}), SampleData([], ['NA12787']), False))
self.assertFalse(
reference == Record(None, Variant("1", 20, "A", "G"), set(
), 0.0, set(), InfoData(None, {}), SampleData([], []), True))
def test_should_format_multiple_values(self):
schema = Schema()
schema.set_info_data('K', 'A', 'Float', 'K')
schema.set_info_data('K2', 'A', 'String', 'K')
schema.set_info_data('K3', '0', 'Flag', 'K')
schema.set_info_data('K4', 'A', 'String', 'K')
info_data = InfoData(schema, {
'K3': None,
'K2': ['S2'],
'K': [1.0, 2.66, 3.0],
'K4': ['S4']
})
self.assertEqual('K=1.0,2.66,3.0;K2=S2;K3;K4=S4', info_data.to_vcf())
def test_doesnt_give_a_flying_damn_about_spurious_info(self):
chrom = "22"
variant = Variant(chrom, 11, "A", "C")
gv_builder = VCFBuilder(join(self.work_dir, "genotype.vcf"))
gv_builder.with_record_from_variant(
variant,
info=InfoData(None,
{"#f$@$e%$%^&k**()7!": ["#o$@$f%$%f^&**()7!"]}))
gv_builder.build().index()
driver = SVCDriver(self)
dodgy_sample = "bobs_your_uncle"
driver.with_ref_sequence(
"ACGCCCCCTGCAAAAAAAAAA", chrom=chrom, pos_from=0).with_read(
"...........C.........",
n_fwd=5,
n_rev=5,
chrom=chrom,
sample_name=dodgy_sample).with_genotype_alleles(
gv_builder.compressed_filename)
expect = driver.call(expected_success=True)
expect.with_output_vcf() \
.has_record_for_variant(variant)\
.with_sample(dodgy_sample)\
.has_genotype("1/1")
def generate_record_from_variant(self, variant, **kwargs):
annotations = {
'variant_id':
set(),
'quality':
None,
'filters':
set(),
'info':
InfoData(self.schema, {}),
'sample_info':
SampleData([key for key, _ in self.schema.iter_sample_data()],
self.schema.samples),
'from_multi_alt':
False,
}
for key, value in kwargs.items():
annotations[key] = value
return Record(schema=self.schema, variant=variant, **annotations)
def calls_variants(self, ref, sequence_list, candidate_ascii_haplotypes, prior, expected_ascii_haplotypes):
sample_bank = SampleBank(ref)
sample_bank.add_sample_with_seqs_and_quals("TEST", sequence_list, 1, 0)
variant_generator = AsciiVariantGenerator(sample_bank.reference)
candidate_variants = variant_generator.get_variants(candidate_ascii_haplotypes)
expected_variants = variant_generator.get_variants(expected_ascii_haplotypes)
candidate_variant_list = VCFBuilder(path.join(self.work_dir, "candiate_variants.vcf"))
candidate_variant_list.schema.set_info_data('AF', 'A', 'Float', 'Allele Frequency')
for var in candidate_variants:
candidate_variant_list.with_record_from_variant(
var, info=InfoData(candidate_variant_list.schema, {"AF": prior})
)
candidate_variant_list.build().index()
vc_wrapper_builder = VariantCallerBuilderFromSampleBank(sample_bank, self.work_dir)
vc_wrapper_builder.configuration[CANDIDATE_VARIANTS_FILE_KEY] = candidate_variant_list.compressed_filename
callset = vc_wrapper_builder.build().run().get_variant_callset(self)
self.assertEqual(callset.get_variants(), set(expected_variants))
def test_should_have_zero_bad_reads_for_candidate_variant_with_no_reads_covering_variant(self):
chrom = "1"
candidate_variant_list = VCFBuilder(path.join(self.work_dir, "candiate_variants.vcf"))
candidate_variant_list.schema.set_info_data('AF', 'A', 'Float', 'Allele Frequency')
variant_1 = Variant(chrom, 30, 'T', 'C')
candidate_variant_list.with_record_from_variant(
variant_1, info=InfoData(candidate_variant_list.schema, {"AF": [0.72]}))
candidate_variant_list.build().index()
svc_driver = SVCDriver(self)\
.with_allow_MNP_calls(True)\
.with_ref_sequence(
"TGTTATTAATCCCTTGTCAGATGTTATTAATCCCTTGTCAGTCCCTTGTCAGT", chrom=chrom)\
.with_read(
"...........................C.. ......................", n_fwd=10, n_rev=10, sample_name='sample_1')\
.with_read(
" ", n_fwd=10, n_rev=10, sample_name='sample_2')\
.with_candidate_variants_file(candidate_variant_list.compressed_filename)
expect = svc_driver.call()
vcf_expect = expect.with_output_vcf()
vcf_expect.missing_record_for_variant(variant_1)
def test_should_format_a_float_list(self):
schema = Schema()
schema.set_info_data('K', 'A', 'Integer', 'K')
info_data = InfoData(schema, {'K': [1.0, 2.66, 3.0]})
self.assertEqual('K=1.0,2.66,3.0', info_data.to_vcf())
def test_should_format_an_int_list(self):
schema = Schema()
schema.set_info_data('K', 'A', 'Integer', 'K')
info_data = InfoData(schema, {'K': [1, 2, 3]})
self.assertEqual('K=1,2,3', info_data.to_vcf())
def test_should_format_a_string_list(self):
schema = Schema()
schema.set_info_data('K', 'A', 'String', 'K')
info_data = InfoData(schema, {'K': ['V1', 'V2']})
self.assertEqual('K=V1,V2', info_data.to_vcf())
def test_should_format_a_string(self):
info_data = InfoData(None, {'K': 'V'})
self.assertEqual('K=V', info_data.to_vcf())
def test_should_format_no_data(self):
info_data = InfoData(None, {})
self.assertEqual('.', info_data.to_vcf())
def test_should_format_a_present_flag(self):
schema = Schema()
schema.set_info_data('F', '0', 'Flag', 'Flag')
info_data = InfoData(schema, {"F": None})
self.assertEqual('F', info_data.to_vcf())
def generate_records(schema, cols):
alts = cols[ALT_COL].split(',')
vars = [Variant(cols[CHROM_COL], int(cols[POS_COL]) -
1, cols[REF_COL], alt) for alt in alts]
info_data_list = []
if len(alts) == 1:
# deferred parsing is simple with a single alt
info_data_list.append(
DeferredInfoData(
schema,
lambda: defer_parse_info_field(
schema,
cols[INFO_COL])))
else:
# extract and split info data into lists of length n_alts
split_info_data = OrderedDict()
for key, value in parse_info_field(cols[INFO_COL]):
try:
info_metadata = schema.get_info_data(key)
except KeyError:
split_info_data[key] = [
DeferredInfoValue(
schema, key, value) for index in range(
len(alts))]
else:
split_info_data[key] = info_metadata.split_alts(
value if isinstance(value, list) else value.split(','), n_alts=len(alts)
)
# construct InfoData objects from prepared info data
for index in range(len(alts)):
info_dict = OrderedDict([
(key, values[index]) for key, values in list(split_info_data.items())
])
info_data_list.append(InfoData(schema, info_dict))
try:
sample_format = cols[FORMAT_COL].split(':')
except IndexError:
sample_data_list = repeat(None)
else:
# extract sample format
split_sample_data = {sample_name: sample_field.split(
':') for sample_name, sample_field in zip(schema.samples, cols[SAMPLE_COL:])}
sample_data_list = [
SampleData(
cols[FORMAT_COL].split(':'),
schema.samples) for _ in alts]
for sample_name, sample_items in list(split_sample_data.items()):
split_sample_items = {}
# extract data from sample fields
gt = None
for key, item in zip(sample_format, sample_items):
try:
if key == GENOTYPE_KEY:
gt = GenotypeCall(item)
values = [
GenotypeCall(gt.deliminator().join(
# Note: default value should be '.', but
# downstream tools aren't good enough to use it
{None: '.', 0: '0', 1 + index: '1'}.get(gt_index, '0') for gt_index in gt
))
for index in range(len(alts))
]
elif key == GENOTYPE_LIKELIHOODS_KEY or key == GENOTYPE_PHRED_LIKELIHOODS_KEY:
values = schema.get_sample_data(key).split_alts(
item.split(','), len(alts), gt)
else:
values = schema.get_sample_data(key).split_alts(
item.split(','), len(alts), None)
split_sample_items[key] = values
except Exception as e:
raise type(e)(
"Error parsing field {} for sample {}: {}".format(
key, sample_name, e))
# distribute data to each split sample meta-data container
for index in range(len(alts)):
sample_data = sample_data_list[index]
for key, value in list(split_sample_items.items()):
sample_data.add_sample_data(sample_name, key, value[index])
# generate & return record objects
for var, info_data, sample_data in zip(
vars, info_data_list, sample_data_list):
qual = variant_quality_from_vcf(cols[QUALITY_COL])
ids = variant_ids_from_vcf(cols[ID_COL])
filts = filters_from_vcf(cols[FILTER_COL])
yield Record(schema, var, ids, qual, filts, info_data, sample_data, len(alts) > 1)