def cli(ctx, bam, reference, variants, bed4_file, min_freq, mutation_db,
reporting_threshold, output):
rs = parse_references_from_fasta(reference)
mapped_read_collection_arr = []
for r in rs:
# Create a MappedReadCollection object
mapped_read_collection_arr.append(parse_mapped_reads_from_bam(r, bam))
variants_obj = parse_nt_variants_from_vcf(variants, rs)
# Mask the unconfident differences
for mrc in mapped_read_collection_arr:
mrc.mask_unconfident_differences(variants_obj)
# Parse the genes from the gene file
genes = parse_BED4_file(bed4_file, rs[0].name)
# Determine which frames our genes are in
frames = set()
for gene in genes:
frames.add(genes[gene]['frame'])
# Create an AACensus object
aa_census = AACensus(reference, mapped_read_collection_arr, genes, frames)
# Create AAVar collection and print the aavf file
aa_vars = AAVariantCollection.from_aacensus(aa_census)
# Filter for mutant frequency
aa_vars.filter('mf' + str(min_freq), 'freq<' + str(min_freq), True)
# Build the mutation database
mutation_db = MutationDB(mutation_db, genes)
# Generate the mutation report
if output:
output.write(
aa_vars.report_dr_mutations(mutation_db, reporting_threshold))
output.close()
else:
click.echo(
aa_vars.report_dr_mutations(mutation_db, reporting_threshold))
def aavar(bam, reference, variants, genes_file, min_freq, mutation_db, output):
rs = parse_references_from_fasta(reference)
mapped_read_collection_arr = []
for r in rs:
# Create a MappedReadCollection object
mapped_read_collection_arr.append(parse_mapped_reads_from_bam(r, bam))
variants_obj = parse_nt_variants_from_vcf(variants, rs)
# Mask the unconfident differences
for mrc in mapped_read_collection_arr:
mrc.mask_unconfident_differences(variants_obj)
# Parse the genes from the gene file
genes = parse_genes_file(genes_file, rs[0].name)
# Determine which frames our genes are in
frames = set()
for gene in genes:
frames.add(genes[gene]['frame'])
# Create an AACensus object
aa_census = AACensus(reference, mapped_read_collection_arr, genes, frames)
# Create AAVar collection and print the hmcf file
aa_vars = AAVariantCollection.from_aacensus(aa_census)
# Filter for mutant frequency
aa_vars.filter('mf0.01', 'freq<0.01', True)
# Build the mutation database and update collection
if mutation_db is not None:
mutation_db = MutationDB(mutation_db, genes)
aa_vars.apply_mutation_db(mutation_db)
if output:
output.write(aa_vars.to_hmcf_file(CONFIDENT))
else:
click.echo(aa_vars.to_hmcf_file(CONFIDENT))
def codonvar(bam, reference, offset, bed4_file, variants, error_rate, output):
rs = parse_references_from_fasta(reference)
mapped_read_collection_arr = []
# Create a MappedReadCollection object
for r in rs:
mapped_read_collection_arr.append(parse_mapped_reads_from_bam(r, bam))
if variants:
variants_obj = parse_nt_variants_from_vcf(variants, rs)
else:
variants = NTVariantCollection.from_mapped_read_collections(
error_rate, rs, *mapped_read_collection_arr)
variants.filter('q30', 'QUAL<30', True)
variants.filter('ac5', 'AC<5', True)
variants.filter('dp100', 'DP<100', True)
variants_obj = variants
# Mask the unconfident differences
for mrc in mapped_read_collection_arr:
mrc.mask_unconfident_differences(variants_obj)
# Parse the genes from the gene file
genes = parse_BED4_file(bed4_file, rs[0].name)
# Determine which frames our genes are in
frames = set()
for gene in genes:
frames.add(genes[gene]['frame'])
aa_census = AACensus(reference, mapped_read_collection_arr, genes, frames)
codon_variants = CodonVariantCollection.from_aacensus(aa_census)
if output:
output.write(codon_variants.to_csv_file(offset))
output.close()
else:
click.echo(codon_variants.to_csv_file(offset))
def setup(self):
reference = TEST_PATH + "/data/hxb2_pol.fas"
bam = TEST_PATH + "/data/align.bam"
genes_file = TEST_PATH + "/data/hxb2_pol.bed"
mutation_db = TEST_PATH + "/data/mutation_db.tsv"
min_freq = 0.01
rs = parse_references_from_fasta(reference)
mapped_read_collection_arr = []
for r in rs:
# Create a MappedReadCollection object
mapped_read_collection_arr.append(
parse_mapped_reads_from_bam(r, bam))
variants_obj = parse_nt_variants_from_vcf(VARIANTS_FILE, rs)
# Mask the unconfident differences
for mrc in mapped_read_collection_arr:
mrc.mask_unconfident_differences(variants_obj)
# Parse the genes from the gene file
genes = parse_genes_file(genes_file, rs[0].name)
# Determine which frames our genes are in
frames = set()
for gene in genes:
frames.add(genes[gene]['frame'])
# Create an AACensus object
aa_census = AACensus(reference, mapped_read_collection_arr, genes,
frames)
# Find the AA mutations
self.aa_collection = AAVariantCollection.from_aacensus(aa_census)
# Build the mutation database
self.mutation_db = MutationDB(mutation_db, genes)
def aavar(bam, reference, bed4_file, variants, mutation_db, min_freq,
error_rate, output):
rs = parse_references_from_fasta(reference)
mapped_read_collection_arr = []
for r in rs:
# Create a MappedReadCollection object
mapped_read_collection_arr.append(parse_mapped_reads_from_bam(r, bam))
if variants:
variants_obj = parse_nt_variants_from_vcf(variants, rs)
else:
variants = NTVariantCollection.from_mapped_read_collections(
error_rate, rs, *mapped_read_collection_arr)
variants.filter('q30', 'QUAL<30', True)
variants.filter('ac5', 'AC<5', True)
variants.filter('dp100', 'DP<100', True)
variants_obj = variants
# Mask the unconfident differences
for mrc in mapped_read_collection_arr:
mrc.mask_unconfident_differences(variants_obj)
# Parse the genes from the gene file
genes = parse_BED4_file(bed4_file, rs[0].name)
# Determine which frames our genes are in
frames = set()
for gene in genes:
frames.add(genes[gene]['frame'])
# Create an AACensus object
aa_census = AACensus(reference, mapped_read_collection_arr, genes, frames)
# Create AAVar collection and print the aavf file
aa_vars = AAVariantCollection.from_aacensus(aa_census)
# Filter for mutant frequency
aa_vars.filter('mf0.01', 'freq<0.01', True)
# Build the mutation database and update collection
if mutation_db is not None:
mutation_db = MutationDB(mutation_db, genes)
aa_vars.apply_mutation_db(mutation_db)
aavf_obj = aa_vars.to_aavf_obj("aavar", os.path.basename(reference),
CONFIDENT)
records = list(aavf_obj)
if output:
writer = parser.Writer(output, aavf_obj)
else:
writer = parser.Writer(sys.stdout, aavf_obj)
for record in records:
writer.write_record(record)
if output:
output.close
writer.close()
def test_valid_vcf_file(self):
"""Tests to ensure that valid vcf files are parsed properly."""
reference = TEST_PATH + \
"/data/hxb2_pol.fas"
bam = TEST_PATH + "/data/align.bam"
rs = parse_references_from_fasta(reference)
mapped_read_collection_arr = []
for r in rs:
# Create a MappedReadCollection object
mapped_read_collection_arr.append(
parse_mapped_reads_from_bam(r, bam))
variants_obj = NTVariantCollection(rs)
for i in range(0, 20):
variant = NTVariant(chrom="hxb2_pol",
pos=i,
id=".",
ref='a',
alt='t',
qual="50",
filter="PASS",
info={
"DP": "300",
"AC": "1",
"AF": "0.0025"
})
variants_obj.variants["hxb2_pol"][i]['t'] = variant
#Create a valid vcf file
valid_vcf_file = TEST_PATH + "/data/valid_vcf_file.vcf"
with open(valid_vcf_file, "w+") as f:
f.write(
"##fileformat=VCFv4.2\n"
"##fileDate=20171005\n"
"##source=quasitools\n"
"##INFO=<ID=DP,Number=1,Type=Integer,Description=\"Total Depth\">\n"
"##INFO=<ID=AC,Number=A,Type=Integer,Description=\"Allele Count\">\n"
"##INFO=<ID=AF,Number=A,Type=Float,Description=\"Allele Frequency\">\n"
"##FILTER=<ID=q30,Description=\"Quality below 30\">\n"
"##FILTER=<ID=dp100,Description=\"Read depth below 100\">\n"
"##FILTER=<ID=ac5,Description=\"Allele count below 5\">\n"
"#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO")
for rid in variants_obj.variants:
for pos in variants_obj.variants[rid]:
for alt in variants_obj.variants[rid][pos]:
variant = variants_obj.variants[rid][pos][alt]
f.write("\n%s\t%i\t%s\t%s\t%s\t%s\t%s" %
(variant.chrom, int(
variant.pos), variant.id, variant.ref,
variant.alt, variant.qual, variant.filter))
f.write(
"\tDP=%i;AC=%i;AF=%0.4f" %
(int(variant.info["DP"]), int(variant.info["AC"]),
float(variant.info["AF"])))
parsed_nt_var = parse_nt_variants_from_vcf(valid_vcf_file, rs)
# Check equality of parsed NTVariantCollection vs. the valid NTVariantCollection
for rid in parsed_nt_var.variants:
for pos in parsed_nt_var.variants[rid]:
for alt in parsed_nt_var.variants[rid][pos]:
parsed_variant = parsed_nt_var.variants[rid][pos][alt]
variant = variants_obj.variants[rid][pos][alt]
assert parsed_variant.chrom == variant.chrom
assert parsed_variant.pos == variant.pos
assert parsed_variant.id == variant.id
assert parsed_variant.ref == variant.ref
assert parsed_variant.alt == variant.alt
assert parsed_variant.qual == variant.qual
assert parsed_variant.filter == variant.filter
assert parsed_variant.info["DP"] == variant.info["DP"]
assert parsed_variant.info["AC"] == variant.info["AC"]
assert parsed_variant.info["AF"] == variant.info["AF"]
os.remove(valid_vcf_file)
