def main(args):
full_vcf = hl.read_matrix_table(args.allreads_prefix + '.mt')
# liftover chains
rg37 = hl.get_reference('GRCh37')
rg38 = hl.get_reference('GRCh38')
rg37.add_liftover(
'gs://hail-common/references/grch37_to_grch38.over.chain.gz', rg38)
chips = hl.hadoop_open(args.chip_loci)
chip_dict = {}
for chip in chips:
chip = chip.strip().split()
chip_pos = hl.import_table(chip[1],
filter='\[Controls\]',
skip_blank_lines=True)
chip_pos = chip_pos.filter(
hl.array(list(map(str, range(1, 23))) + ['X', 'Y']).contains(
chip_pos.chr))
chip_pos = chip_pos.key_by(
locus=hl.locus(chip_pos.chr, hl.int(chip_pos.pos)))
# liftover chip position info
chip_pos = chip_pos.annotate(
new_locus=hl.liftover(chip_pos.locus, 'GRCh38'))
chip_pos = chip_pos.filter(hl.is_defined(chip_pos.new_locus))
chip_pos = chip_pos.key_by(locus=chip_pos.new_locus)
# filter full vcf to sites in genotype data
geno_vcf = full_vcf.filter_rows(hl.is_defined(
chip_pos[full_vcf.locus]))
hl.export_vcf(
geno_vcf,
'gs://neurogap/high_coverage/NeuroGap_30x_' + chip[0] + '.vcf.bgz')
def plinkify(ds, min=None, max=None):
vcf = utils.new_temp_file(prefix="plink", suffix="vcf")
plinkpath = utils.new_temp_file(prefix="plink")
hl.export_vcf(ds, vcf)
threshold_string = "{} {}".format("--min {}".format(min) if min else "",
"--max {}".format(max) if max else "")
plink_command = "plink --double-id --allow-extra-chr --vcf {} --genome full --out {} {}" \
.format(utils.uri_path(vcf),
utils.uri_path(plinkpath),
threshold_string)
result_file = utils.uri_path(plinkpath + ".genome")
syscall(plink_command, shell=True, stdout=DEVNULL, stderr=DEVNULL)
### format of .genome file is:
# _, fid1, iid1, fid2, iid2, rt, ez, z0, z1, z2, pihat, phe,
# dst, ppc, ratio, ibs0, ibs1, ibs2, homhom, hethet (+ separated)
### format of ibd is:
# i (iid1), j (iid2), ibd: {Z0, Z1, Z2, PI_HAT}, ibs0, ibs1, ibs2
results = {}
with open(result_file) as f:
f.readline()
for line in f:
row = line.strip().split()
results[(row[1], row[3])] = (list(map(float, row[6:10])),
list(map(int, row[14:17])))
return results
def main(args):
hl.init(
default_reference=args.import_build,
log="/import_vcf.log",
tmp_dir="hdfs:///import_vcf.tmp/",
)
logger.info("Importing filters info...")
filters_ht = hl.import_vcf(
args.part_two_path,
reference_genome=args.import_build,
force_bgz=True,
find_replace=("nul", "."),
).rows()
logger.info("Importing VCF...")
# NOTE: always assumes file is bgzipped
mt = hl.import_vcf(
args.part_one_path,
force_bgz=True,
reference_genome=args.import_build,
find_replace=("nul", "."),
)
mt = mt.annotate_rows(filters=filters_ht[mt.row_key].filters)
logger.info(f"MT count: {mt.count()}")
hl.export_vcf(mt, args.vcf_out, parallel="header_per_shard")
def export_qced_file(mt: hl.MatrixTable,
out_dir: str,
basename: str,
export_type='hail'):
outname = basename + '_qced'
if export_type == 'hail':
mt.write('{}GWASpy/Preimp_QC/{}.mt'.format(out_dir, outname),
overwrite=True)
elif export_type == 'plink':
hl.export_plink(dataset=mt,
output='{}GWASpy/Preimp_QC/{}'.format(
out_dir, outname),
fam_id=mt.fam_id,
ind_id=mt.s,
pat_id=mt.pat_id,
mat_id=mt.mat_id,
is_female=mt.is_female,
pheno=mt.is_case,
varid=mt.rsid)
else:
hl.export_vcf(mt,
'{}GWASpy/Preimp_QC/{}.vcf.bgz'.format(out_dir, outname))
def test_not_identical_headers(self):
t = new_temp_file('vcf')
mt = hl.import_vcf(resource('sample.vcf'))
hl.export_vcf(mt.filter_cols((mt.s != "C1048::HG02024") & (mt.s != "HG00255")), t)
with self.assertRaisesRegex(FatalError, 'invalid sample IDs'):
(hl.import_vcf([resource('sample.vcf'), t])
._force_count_rows())
def test_not_identical_headers(self):
t = new_temp_file('vcf')
mt = hl.import_vcf(resource('sample.vcf'))
hl.export_vcf(
mt.filter_cols((mt.s != "C1048::HG02024") & (mt.s != "HG00255")),
t)
with self.assertRaisesRegex(FatalError, 'invalid sample IDs'):
(hl.import_vcf([resource('sample.vcf'), t])._force_count_rows())
def test_export_vcf(self):
dataset = hl.import_vcf(resource('sample.vcf.bgz'))
vcf_metadata = hl.get_vcf_metadata(resource('sample.vcf.bgz'))
hl.export_vcf(dataset, '/tmp/sample.vcf', metadata=vcf_metadata)
dataset_imported = hl.import_vcf('/tmp/sample.vcf')
self.assertTrue(dataset._same(dataset_imported))
metadata_imported = hl.get_vcf_metadata('/tmp/sample.vcf')
self.assertDictEqual(vcf_metadata, metadata_imported)
def main(args):
'''
participant_data = args.participant_data
sample_map = args.sample_map
coverage_mt_path = args.coverage_mt_path
vcf_col = args.vcf_col
artifact_prone_sites_path = args.artifact_prone_sites_path
output_bucket = args.output_bucket
file_suffix = args.file_suffix
minimum_homref_coverage = args.minimum_homref_coverage
chunk_size = args.chunk_size
overwrite = args.overwrite
'''
input_tsv = args.input_tsv
artifact_prone_sites_path = args.artifact_prone_sites_path
chunk_size = args.chunk_size
overwrite = args.overwrite
output_bucket = args.output_bucket
file_suffix = args.file_suffix
minimum_homref_coverage = args.minimum_homref_coverage
coverage_mt_path = args.coverage_mt_path
'''
logger.info("Confirming existence of individual sample vcfs...")
confirmed_vcfs = check_vcf_existence(
participant_data, vcf_col, sample_map, output_bucket
)
'''
logger.info("Combining VCFs...")
combined_mt = join_mitochondria_vcfs_into_mt(input_tsv, output_bucket,
chunk_size)
output_path_mt = f"{output_bucket}/raw_combined_mt.mt"
combined_mt = combined_mt.checkpoint(output_path_mt, overwrite=overwrite)
logger.info("Removing certain FT filters...")
combined_mt = remove_FT_values(combined_mt)
logger.info("Determining homoplasmic reference sites...")
combined_mt = determine_hom_refs(combined_mt, coverage_mt_path,
minimum_homref_coverage)
logger.info("Applying artifact_prone_site fiter...")
combined_mt = apply_mito_artifact_filter(combined_mt,
artifact_prone_sites_path)
logger.info("Writing combined MT and VCF...")
# set the file names for output files
out_vcf = f"{output_bucket}/combined_{file_suffix}.vcf.bgz"
out_mt = f"{output_bucket}/combined_{file_suffix}.mt"
combined_mt.write(out_mt, overwrite=True)
hl.export_vcf(combined_mt, out_vcf, metadata=META_DICT)
logger.info("DONE")
def main(args):
hl.init(master=f'local[{args.n_threads}]',
log=hl.utils.timestamp_path(os.path.join(tempfile.gettempdir(), 'extract_vcf'), suffix='.log'),
default_reference=args.reference)
sys.path.append('/')
add_args = []
if args.additional_args is not None:
add_args = args.additional_args.split(',')
load_module = importlib.import_module(args.load_module)
mt = getattr(load_module, args.load_mt_function)(*add_args)
if args.gene_map_ht_path is None:
interval = [hl.parse_locus_interval(args.interval)]
else:
gene_ht = hl.read_table(args.gene_map_ht_path)
if args.gene is not None:
gene_ht = gene_ht.filter(gene_ht.gene_symbol == args.gene)
interval = gene_ht.aggregate(hl.agg.take(gene_ht.interval, 1), _localize=False)
else:
interval = [hl.parse_locus_interval(args.interval)]
gene_ht = hl.filter_intervals(gene_ht, interval)
gene_ht = gene_ht.filter(hl.set(args.groups.split(',')).contains(gene_ht.annotation))
gene_ht.select(group=gene_ht.gene_id + '_' + gene_ht.gene_symbol + '_' + gene_ht.annotation, variant=hl.delimit(gene_ht.variants, '\t')
).key_by().drop('start').export(args.group_output_file, header=False)
# TODO: possible minor optimization: filter output VCF to only variants in `gene_ht.variants`
if not args.no_adj:
mt = mt.filter_entries(mt.adj)
mt = hl.filter_intervals(mt, interval)
if not args.input_bgen:
mt = mt.select_entries('GT')
mt = mt.filter_rows(hl.agg.count_where(mt.GT.is_non_ref()) > 0)
mt = mt.annotate_rows(rsid=mt.locus.contig + ':' + hl.str(mt.locus.position) + '_' + mt.alleles[0] + '/' + mt.alleles[1])
if args.callrate_filter:
mt = mt.filter_rows(hl.agg.fraction(hl.is_defined(mt.GT)) >= args.callrate_filter)
if args.export_bgen:
if not args.input_bgen:
mt = mt.annotate_entries(GT=hl.if_else(mt.GT.is_haploid(), hl.call(mt.GT[0], mt.GT[0]), mt.GT))
mt = gt_to_gp(mt)
mt = impute_missing_gp(mt, mean_impute=args.mean_impute_missing)
hl.export_bgen(mt, args.output_file, gp=mt.GP, varid=mt.rsid)
else:
mt = mt.annotate_entries(GT=hl.or_else(mt.GT, hl.call(0, 0)))
# Note: no mean-imputation for VCF
hl.export_vcf(mt, args.output_file)
def test_export_vcf(self):
dataset = hl.import_vcf(resource('sample.vcf.bgz'))
vcf_metadata = hl.get_vcf_metadata(resource('sample.vcf.bgz'))
hl.export_vcf(dataset, '/tmp/sample.vcf', metadata=vcf_metadata)
dataset_imported = hl.import_vcf('/tmp/sample.vcf')
self.assertTrue(dataset._same(dataset_imported))
no_sample_dataset = dataset.filter_cols(False).select_entries()
hl.export_vcf(no_sample_dataset, '/tmp/no_sample.vcf', metadata=vcf_metadata)
no_sample_dataset_imported = hl.import_vcf('/tmp/no_sample.vcf')
self.assertTrue(no_sample_dataset._same(no_sample_dataset_imported))
metadata_imported = hl.get_vcf_metadata('/tmp/sample.vcf')
self.assertDictEqual(vcf_metadata, metadata_imported)
def main(args):
hl.init(default_reference="GRCh38", log="/qc_annotations.log")
if args.compute_info:
compute_info().write(get_info(split=False).path,
overwrite=args.overwrite)
if args.split_info:
split_info().write(get_info(split=True).path, overwrite=args.overwrite)
if args.export_info_vcf:
info_ht = get_info(split=False).ht()
hl.export_vcf(ht_to_vcf_mt(info_ht), info_vcf_path())
if args.generate_allele_data:
mt = get_gnomad_v3_mt(key_by_locus_and_alleles=True)
generate_allele_data(mt.rows()).write(allele_data.path,
overwrite=args.overwrite)
if args.generate_ac: # TODO: compute AC and qc_AC as part of compute_info
mt = get_gnomad_v3_mt(key_by_locus_and_alleles=True, samples_meta=True)
mt = hl.experimental.sparse_split_multi(mt, filter_changed_loci=True)
ht = generate_ac(mt).checkpoint(
"gs://gnomad-tmp/ac_tmp.ht",
overwrite=args.overwrite,
_read_if_exists=not args.overwrite,
)
ht.repartition(10000, shuffle=False).write(qc_ac.path,
overwrite=args.overwrite)
if args.generate_fam_stats:
mt = get_gnomad_v3_mt(key_by_locus_and_alleles=True, samples_meta=True)
mt = hl.experimental.sparse_split_multi(mt, filter_changed_loci=True)
fam_stats_ht = generate_fam_stats(mt, trios.path)
fam_stats_ht = fam_stats_ht.checkpoint(
"gs://gnomad-tmp/fam_stats_tmp.ht",
overwrite=args.overwrite,
_read_if_exists=not args.overwrite,
)
fam_stats_ht = fam_stats_ht.repartition(10000, shuffle=False)
fam_stats_ht.write(fam_stats.path, overwrite=args.overwrite)
if args.export_transmitted_singletons_vcf:
export_transmitted_singletons_vcf()
if args.vep:
run_vep(vep_version=args.vep_version).write(vep.path,
overwrite=args.overwrite)
def main(args):
print("main")
run_hash = "91b132aa"
ht = hl.read_table(
f'{temp_dir}/ddd-elgh-ukbb/variant_qc/models/{run_hash}/{run_hash}_rf_result_ranked_denovo_ddd_comp.ht'
)
mt = hl.read_matrix_table(
f'{temp_dir}/ddd-elgh-ukbb/Sanger_cohorts_chr1-7and20_split_sampleqc_filtered.mt'
)
mt = mt.annotate_rows(Variant_Type=hl.cond(
(hl.is_snp(mt.alleles[0], mt.alleles[1])), "SNP",
hl.cond(
hl.is_insertion(mt.alleles[0], mt.alleles[1]), "INDEL",
hl.cond(hl.is_deletion(mt.alleles[0], mt.alleles[1]), "INDEL",
"Other"))))
mt = mt.annotate_rows(info=mt.info.annotate(
rf_probability=ht[mt.row_key].rf_probability['TP']))
mt = mt.annotate_rows(info=mt.info.annotate(score=ht[mt.row_key].score))
filter_column_annotation = (
hl.case().when(
((mt.Variant_Type == "SNP") & (mt.info.rf_probability <= 0.90)),
"PASS").when(((mt.Variant_Type == "INDEL") &
(mt.info.rf_probability <= 0.80)),
"PASS").default(".") # remove everything else
)
# mt_annotated = mt.annotate_rows(mt.filters=filter_column_annotation)
mt1 = mt.annotate_rows(filtercol=((filter_column_annotation)))
mt_fail = mt1.filter_rows(mt1.filtercol == ".")
print(mt_fail.count())
mt2 = mt1.annotate_rows(filters=mt1.filters.add(mt1.filtercol))
mt_fail2 = mt2.filter_rows(mt2.filters.contains("."))
mt_pass = mt2.filter_rows(mt2.filters.contains("PASS"))
print(mt_fail2.count())
print(mt_pass.count())
mt2 = mt2.checkpoint(
f'{tmp_dir}/Sanger_cohorts_chr1-7and20_after_RF_final.mt',
overwrite=True)
hl.export_vcf(
mt2,
f'{tmp_dir}/Sanger_cohorts_chr1-7and20_after_RF_final.vcf.bgz',
parallel='separate_header')
def main(args):
tgp_vcf = hl.import_vcf(args.input_vcf,
force_bgz=True,
header_file='gs://neurogap/reference_data/header',
reference_genome='GRCh38',
min_partitions=200)
sample_info = hl.import_table(args.sample_info).key_by('sample')
tgp_vcf = tgp_vcf.annotate_cols(**sample_info[tgp_vcf.s])
afr_vcf = tgp_vcf.filter_cols(
(tgp_vcf.super_pop == 'AFR')
& ~((tgp_vcf.pop == 'ASW') |
(tgp_vcf.pop == 'ACB'))) # NA18498 is missing
sample_info.filter(hl.is_missing(
tgp_vcf.cols()[sample_info['sample']])).show()
hl.export_vcf(afr_vcf, args.output_filename)
def test_export_vcf(self):
dataset = hl.import_vcf(resource('sample.vcf.bgz'))
vcf_metadata = hl.get_vcf_metadata(resource('sample.vcf.bgz'))
hl.export_vcf(dataset, '/tmp/sample.vcf', metadata=vcf_metadata)
dataset_imported = hl.import_vcf('/tmp/sample.vcf')
self.assertTrue(dataset._same(dataset_imported))
no_sample_dataset = dataset.filter_cols(False).select_entries()
hl.export_vcf(no_sample_dataset,
'/tmp/no_sample.vcf',
metadata=vcf_metadata)
no_sample_dataset_imported = hl.import_vcf('/tmp/no_sample.vcf')
self.assertTrue(no_sample_dataset._same(no_sample_dataset_imported))
metadata_imported = hl.get_vcf_metadata('/tmp/sample.vcf')
self.assertDictEqual(vcf_metadata, metadata_imported)
def main(args):
print("main")
run_hash = "91b132aa"
ht = hl.read_table(
f'{temp_dir}/ddd-elgh-ukbb/variant_qc/models/{run_hash}/{run_hash}_rf_result_ranked_denovo_ddd_comp.ht'
)
mt = hl.read_matrix_table(
f'{temp_dir}/ddd-elgh-ukbb/variant_qc/Sanger_cohorts_chr1-7and20_after_RF_final.mt'
)
hl.export_vcf(
mt,
f'{tmp_dir}/Sanger_cohorts_chr1-7and20_after_RF_final.vcf.bgz',
parallel='separate_header')
def test_export_plink(self):
vcf_file = resource('sample.vcf')
mt = hl.split_multi_hts(hl.import_vcf(vcf_file, min_partitions=10))
# permute columns so not in alphabetical order!
import random
indices = list(range(mt.count_cols()))
random.shuffle(indices)
mt = mt.choose_cols(indices)
split_vcf_file = uri_path(new_temp_file())
hl_output = uri_path(new_temp_file())
plink_output = uri_path(new_temp_file())
merge_output = uri_path(new_temp_file())
hl.export_vcf(mt, split_vcf_file)
hl.export_plink(mt, hl_output)
run_command(["plink", "--vcf", split_vcf_file,
"--make-bed", "--out", plink_output,
"--const-fid", "--keep-allele-order"])
data = []
with open(uri_path(plink_output + ".bim")) as file:
for line in file:
row = line.strip().split()
row[1] = ":".join([row[0], row[3], row[5], row[4]])
data.append("\t".join(row) + "\n")
with open(plink_output + ".bim", 'w') as f:
f.writelines(data)
run_command(["plink", "--bfile", plink_output,
"--bmerge", hl_output, "--merge-mode",
"6", "--out", merge_output])
same = True
with open(merge_output + ".diff") as f:
for line in f:
row = line.strip().split()
if row != ["SNP", "FID", "IID", "NEW", "OLD"]:
same = False
break
self.assertTrue(same)
def test_impute_sex_same_as_plink(self):
import subprocess as sp
ds = hl.import_vcf(resource('x-chromosome.vcf'))
sex = hl.impute_sex(ds.GT, include_par=True)
vcf_file = utils.uri_path(utils.new_temp_file(prefix="plink", suffix="vcf"))
out_file = utils.uri_path(utils.new_temp_file(prefix="plink"))
hl.export_vcf(ds, vcf_file)
try:
out = sp.check_output(
["plink", "--vcf", vcf_file, "--const-fid", "--check-sex",
"--silent", "--out", out_file],
stderr=sp.STDOUT)
except sp.CalledProcessError as e:
print(e.output)
raise e
plink_sex = hl.import_table(out_file + '.sexcheck',
delimiter=' +',
types={'SNPSEX': hl.tint32,
'F': hl.tfloat64})
plink_sex = plink_sex.select('IID', 'SNPSEX', 'F')
plink_sex = plink_sex.select(
s=plink_sex.IID,
is_female=hl.cond(plink_sex.SNPSEX == 2,
True,
hl.cond(plink_sex.SNPSEX == 1,
False,
hl.null(hl.tbool))),
f_stat=plink_sex.F).key_by('s')
sex = sex.select(s=sex.s,
is_female=sex.is_female,
f_stat=sex.f_stat)
self.assertTrue(plink_sex._same(sex.select_globals(), tolerance=1e-3))
ds = ds.annotate_rows(aaf=(agg.call_stats(ds.GT, ds.alleles)).AF[1])
self.assertTrue(hl.impute_sex(ds.GT)._same(hl.impute_sex(ds.GT, aaf='aaf')))
def test_export_plink(self):
vcf_file = resource('sample.vcf')
mt = hl.split_multi_hts(hl.import_vcf(vcf_file, min_partitions=10))
split_vcf_file = uri_path(new_temp_file())
hl_output = uri_path(new_temp_file())
plink_output = uri_path(new_temp_file())
merge_output = uri_path(new_temp_file())
hl.export_vcf(mt, split_vcf_file)
hl.export_plink(mt, hl_output)
run_command([
"plink", "--vcf", split_vcf_file, "--make-bed", "--out",
plink_output, "--const-fid", "--keep-allele-order"
])
data = []
with open(uri_path(plink_output + ".bim")) as file:
for line in file:
row = line.strip().split()
row[1] = ":".join([row[0], row[3], row[5], row[4]])
data.append("\t".join(row) + "\n")
with open(plink_output + ".bim", 'w') as f:
f.writelines(data)
run_command([
"plink", "--bfile", plink_output, "--bmerge", hl_output,
"--merge-mode", "6", "--out", merge_output
])
same = True
with open(merge_output + ".diff") as f:
for line in f:
row = line.strip().split()
if row != ["SNP", "FID", "IID", "NEW", "OLD"]:
same = False
break
self.assertTrue(same)
def export_transmitted_singletons_vcf():
"""
Exports the transmitted singleton Table to a VCF.
:return: None
"""
qc_ac_ht = qc_ac.ht()
for transmission_confidence in ['raw', 'adj']:
ts_ht = qc_ac_ht.filter(
(fam_stats.ht()[qc_ac_ht.key][f'n_transmitted_{transmission_confidence}'] == 1) &
(qc_ac_ht.ac_qc_samples_raw == 2)
)
ts_ht = ts_ht.annotate(
s=hl.null(hl.tstr)
)
ts_mt = ts_ht.to_matrix_table_row_major(columns=['s'], entry_field_name='s')
ts_mt = ts_mt.filter_cols(False)
hl.export_vcf(ts_mt, get_transmitted_singleton_vcf_path(transmission_confidence), tabix=True)
def main(args):
"""
Subset joint-called VCF or MT to desired samples.
Used when CMG/MGRC collaborators request VCFs of their data.
"""
hl.init(log="/subset.log", default_reference="GRCh38")
if args.vcf_path:
logger.info("Importing VCF...")
logger.warning("Assuming VCF is bgzipped!")
mt = hl.import_vcf(args.vcf_path,
force_bgz=True,
reference_genome=args.import_build)
else:
logger.info("Reading in MT...")
mt = hl.read_matrix_table(args.mt_path)
logger.info(f"Input MT counts: {mt.count()}")
mt.describe()
if args.mapping:
logger.info("Mapping VCF IDs to seqr IDs...")
logger.warning("Assuming mapping file the field names s and seqr_id!")
mt = remap_sample_ids(mt, args.mapping)
logger.info("Subsetting to specified samples and their variants...")
mt = subset_samples_and_variants(mt,
args.sample_list,
header=args.no_header,
table_key=args.table_key)
logger.info(f"MT counts after subsetting: {mt.count()}")
logger.info("Exporting VCF...")
if "bgz" not in args.vcf_out:
logger.warning(
"Path to output VCF does not contain '.bgz'; export might be really slow!"
)
hl.export_vcf(mt, args.vcf_out, parallel=args.parallel)
print("importing vds files")
vds = hl.read_matrix_table(vds_splitmulti_file)
print("removing lcr")
lcr = hl.import_bed(lcr_file, reference_genome='GRCh38')
vds = vds.filter_rows(hl.is_defined(lcr[vds.locus]), keep=False)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# II. Downcoding
# Only keep GT for individual column
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
print("downcoding...")
vds = vds.select_entries(vds.GT)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# III. Write output VCF
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
print("writing out vcf...")
hl.export_vcf(vds, qced_vcf_file)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# print Runtime
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
stop = timeit.default_timer()
print("runtime: " + str(stop - start) + " seconds")
def export_vcf():
mt = hl.read_matrix_table(resource('profile.mt'))
out = hl.utils.new_temp_file(suffix='vcf.bgz')
hl.export_vcf(mt, out)
def prepare_downloads_for_dataset(dataset_id):
output_path = pipeline_config.get("output", "staging_path")
dataset_prefix = os.path.join(output_path, dataset_id.lower())
output_prefix = os.path.join(output_path, "downloads", dataset_id)
gene_results_path = os.path.join(dataset_prefix, "gene_results.ht")
gene_results = hl.read_table(gene_results_path)
validate_gene_results_table(gene_results)
gene_group_result_fields = gene_results.group_results.dtype.value_type.fields
gene_results_dsv = gene_results
gene_results_dsv = gene_results_dsv.transmute(group_results=hl.array(
gene_results_dsv.group_results
).map(lambda group_and_result: group_and_result[1].annotate(
group=group_and_result[0]).select("group", *gene_group_result_fields)))
gene_results_dsv = gene_results_dsv.explode(gene_results_dsv.group_results,
name="group_result")
gene_results_dsv = gene_results_dsv.transmute(
**gene_results_dsv.group_result)
gene_results_dsv.export(
os.path.join(output_prefix, f"{dataset_id}_gene_results.tsv.bgz"))
variant_results_path = os.path.join(dataset_prefix, "variant_results.ht")
variant_results = hl.read_table(variant_results_path)
validate_variant_results_table(variant_results)
variant_group_result_fields = variant_results.group_results.dtype.value_type.fields
variant_results_dsv = variant_results
variant_results_dsv = variant_results_dsv.transmute(
**variant_results_dsv.info)
variant_results_dsv = variant_results_dsv.transmute(
group_results=hl.array(variant_results_dsv.group_results).map(
lambda group_and_result: group_and_result[
1].annotate(group=group_and_result[0]).select(
"group", *variant_group_result_fields)))
variant_results_dsv = variant_results_dsv.explode(
variant_results_dsv.group_results, name="group_result")
variant_results_dsv = variant_results_dsv.transmute(
**variant_results_dsv.group_result)
variant_results_dsv.export(
os.path.join(output_prefix, f"{dataset_id}_variant_results.tsv.bgz"))
variant_results_groups = variant_results.aggregate(
hl.agg.explode(hl.agg.collect_as_set,
variant_results.group_results.keys()))
variant_info_fields = variant_results.info.dtype.fields
variant_base_fields = set(
variant_results.row_value) - {"info", "group_results"}
all_fields = list(variant_base_fields) + list(variant_info_fields) + list(
variant_group_result_fields)
assert len(all_fields) == len(set(all_fields)), "Conflicting field names"
variant_results_vcf = variant_results
variant_results_vcf = variant_results_vcf.annotate(
groups=variant_results_vcf.group_results.keys())
variant_results_vcf = variant_results_vcf.select(info=hl.struct(
**{f: variant_results_vcf[f]
for f in variant_base_fields},
**{f: variant_results_vcf.info[f]
for f in variant_info_fields},
groups=variant_results_vcf.groups,
**dict(
map(
lambda f: (
f,
variant_results_vcf.groups.map(
lambda group: variant_results_vcf.group_results[group][
f]),
),
variant_group_result_fields,
)),
), )
def _convert_type(field):
if isinstance(field.dtype, hl.tarray):
if field.dtype.element_type == hl.tbool:
return field.map(hl.int)
return field
variant_results_vcf = variant_results_vcf.annotate(
info=variant_results_vcf.info.annotate(**{
f: _convert_type(variant_results_vcf.info[f])
for f in all_fields
}))
with NamedTemporaryFile("w") as header_file:
header_file.write(
f"analysis_groups={','.join(variant_results_groups)}")
hl.export_vcf(
variant_results_vcf,
os.path.join(output_prefix,
f"{dataset_id}_variant_results.vcf.bgz"),
append_to_header=f"file://{header_file.name}",
metadata={
"info": {
**{
f: {
"Number": str(len(variant_results_groups))
}
for f in variant_group_result_fields
}
}
},
)
def export_vcf():
mt = hl.read_matrix_table(resource('profile.mt'))
out = hl.utils.new_temp_file(suffix='vcf.bgz')
hl.export_vcf(mt, out)
def main(args):
hl.init(default_reference='GRCh38')
hgdp_inputs = []
tgp_inputs = []
with hl.hadoop_open(
'gs://hgdp_tgp/misc/tgp_plus_hgdp_30x_reblocked_gvcfs.txt',
'r') as f:
for line in f:
line = line.strip()
hgdp_inputs.append(line) if 'HGDP' in line else tgp_inputs.append(
line)
temp_bucket = 'gs://gnomad-tmp/tgp_hgdp'
if args.get_sample_names:
get_sample_names_from_list_of_files(tgp_inputs,
get_samples_path('tgp'))
get_sample_names_from_list_of_files(hgdp_inputs,
get_samples_path('hgdp'))
if args.create_sparse_mt:
sample_names = get_sample_list_in_order(get_samples_path('tgp'),
tgp_inputs)
hl.experimental.run_combiner(tgp_inputs,
out_file=get_reference_mt_path(
'tgp', sparse=True),
tmp_path=temp_bucket,
overwrite=args.overwrite,
header=get_header_path('tgp'),
sample_names=sample_names,
use_genome_default_intervals=True)
sample_names = get_sample_list_in_order(get_samples_path('hgdp'),
hgdp_inputs)
hl.experimental.run_combiner(hgdp_inputs,
out_file=get_reference_mt_path(
'hgdp', sparse=True),
tmp_path=temp_bucket,
overwrite=args.overwrite,
header=get_header_path('hgdp'),
sample_names=sample_names,
use_genome_default_intervals=True)
tgp_mt = hl.read_matrix_table(get_reference_mt_path('tgp',
sparse=True))
tgp_mt = tgp_mt.annotate_entries(
gvcf_info=tgp_mt.gvcf_info.drop('MQ0', 'VariantType')).drop(
'AB', 'MQ0')
hgdp_mt = hl.read_matrix_table(
get_reference_mt_path('hgdp', sparse=True))
hgdp_mt = hgdp_mt.annotate_entries(gvcf_info=hgdp_mt.gvcf_info.select(
*tgp_mt.gvcf_info))
mt = combine_gvcfs([tgp_mt, hgdp_mt])
mt.write(get_reference_mt_path(sparse=True), overwrite=args.overwrite)
if args.densify_mt:
mt = hl.read_matrix_table(get_reference_mt_path(
sparse=True)).key_rows_by('locus', 'alleles')
mt = hl.experimental.densify(hl.experimental.sparse_split_multi(mt))
mt = mt.filter_rows(hl.len(mt.alleles) > 1)
mt.naive_coalesce(5000).write(get_reference_mt_path(), args.overwrite)
mt = hl.read_matrix_table(get_reference_mt_path()).drop('gvcf_info')
hl.export_vcf(mt,
get_reference_mt_path(extension='vcf.bgz'),
parallel='header_per_shard')
def export_vcf(mt_path):
mt = hl.read_matrix_table(hl.read_matrix_table(mt_path))
out = hl.utils.new_temp_file(suffix='vcf.bgz')
hl.export_vcf(mt, out)
with herzog.Cell("python"):
mt = mt.annotate_rows(info=mt.info.annotate(AC=mt.variant_qc.AC))
with herzog.Cell("markdown"):
"""
We export the VCF as several files (shards) to speed up the process.
"""
with herzog.Cell("python"):
start_vcf_write_time = time.time()
with herzog.Cell("python"):
mt = mt.repartition(25)
hl.export_vcf(mt,
bucket + 'MyProject_MAFgt0.01.vcf.bgz',
parallel='header_per_shard')
with herzog.Cell("python"):
elapsed_vcf_write_time = time.time() - start_vcf_write_time
with herzog.Cell("python"):
print(timedelta(seconds=elapsed_vcf_write_time))
with herzog.Cell("markdown"):
"""
Check that these files were successfully loaded to the bucket:
"""
with herzog.Cell("python"):
get_ipython().system(
mt.count_rows() * 2)))
mt = mt.annotate_rows(info=mt.info.annotate(
cohort_names=mt.MAF_cohorts.keys()))
mt = mt.annotate_rows(info=mt.info.annotate(
MAF_cohorts_values=mt.MAF_cohorts.values()))
mt = mt.annotate_rows(info=mt.info.annotate(
AN_cohorts_values=mt.AN_cohorts.values()))
mt = mt.annotate_rows(info=mt.info.annotate(
AC_cohorts=mt.AC_cohorts.values()))
mt = mt.annotate_rows(info=mt.info.annotate(
missingness_cohorts_values=mt.missingness_cohorts.values()))
mt = mt.checkpoint(
f'{lustre_dir}/variant_qc/megaWES_mt_with_stats_w20_June_2021.mt',
overwrite=True)
hl.export_vcf(
mt,
f'{lustre_dir}/variant_qc/megaWES_mt_after_RF_cohort_stats_w20.vcf.bgz',
parallel='separate_header')
mt1 = mt.select_entries()
mt_fin = mt1.filter_cols(mt1['s'] == 'sample')
hl.export_vcf(
mt_fin,
f"{lustre_dir}/variant_qc/megaWES_mt_after_RF_stats_for_VEP_w20.vcf.bgz",
parallel='separate_header')
def main(
json_str: str,
dataset: str,
reference: Optional[str],
multi_fam: bool,
skip_mt: bool,
skip_vcf: bool,
):
"""
This takes the family structures encoded in the JSON str and creates a number
of single-family objects in Test
The option to include a multi-family structure is useful for experimenting with
operations mapping MOI patterns per-family within a larger dataset.
This will be useful in analysing runtime/cost of analysing a single family with
and without extracting from a larger dataset first
Additional options permit the skipping of either the VCF or MT for each family subset
"""
# parse the families dict from the input string, e.g. '{'fam1':['sam1','sam2']}'
families_dict = json.loads(json_str)
gcp_test_bucket = f'gs://cpg-{dataset}-test'
# path built in this way to pass separate components to the GCP file check
gcp_main_bucket = f'gs://cpg-{dataset}-main'
mt_in_bucket = os.path.join('mt', f'{dataset}.mt')
gcp_mt_full = os.path.join(gcp_main_bucket, mt_in_bucket)
# collect all unique sample IDs for a single filter on the MT
all_samples = get_all_unique_members(families_dict)
mt = read_mt(gcp_mt_full, reference=reference)
if multi_fam:
# pull all samples from all requested families
multi_fam_mt = obtain_mt_subset(mt, list(all_samples))
# force-write this family MT to a test location
multi_fam_mt.write(
os.path.join(gcp_test_bucket, 'multiple_families.mt'),
overwrite=True
)
# check samples all present
check_samples_in_mt(all_samples, families_dict, mt)
# for each family, dump both a small MT and a VCF containing the same samples/variants
for family, samples in families_dict.items():
# pull out only this family's samples from the MT
family_mt = obtain_mt_subset(mt, samples)
if not skip_mt:
# write this family MT to a test location
family_mt.write(os.path.join(gcp_test_bucket, f'{family}.mt'))
if not skip_vcf:
# revert to a VCF file format, and write to a test location
# hail generic method, so the MT is an argument
hl.export_vcf(family_mt, os.path.join(gcp_test_bucket, f'{family}.vcf.bgz'))
#Now need to filter rows again by doing a new variant_qc.
#Drop the previous variant_qc and sample_qc
print(
"8. Drop old sample and variant QC and calculate new ones after filtering"
)
fields_to_drop = ['variant_QC_Hail', 'sample_QC_Hail']
mt1 = mt_entries_filtered.drop(*fields_to_drop)
mt2 = hl.sample_qc(mt1, name='sample_QC_Hail')
mt3 = hl.variant_qc(mt2, name='variant_QC_Hail')
#Remove variants with missingness > 3%
mt = mt3.filter_rows(mt3.variant_QC_Hail.call_rate >= 0.97)
print("Finished filtering. Now writing out.")
#8.Export VCF only variants -no genotypes
print("Export VCF only variants -no genotypes")
mt1 = mt.select_entries()
mt2 = mt1.filter_cols(mt1['s'] == 'samplenone')
hl.export_vcf(mt2, f"{tmp_dir}/intervalwes/VCFs/shard1.vcf.bgz")
#9. Write matrixtable
print("Write matrixtable")
mt = mt.checkpoint(f"{tmp_dir}/intervalwes/shard1_filtered.mt",
overwrite=True)
print("Finished writing mt. ")
#this is a python script loosely based on Kumar and Konrad's effort here: https://github.com/mkveerapen/covid19_sequencing
#again, some of the QC at our institution was done by our genome center, and therefore you should refer to the above link for more thorough QC
#specifically, variant recalibration should still be done, even if not shown here, can discuss with me on how to do it using gatk.
import hail as hl
#tmp_dir is where some of the temporary computations are done. I would make sure to assign it to a folder that does not have a strict data cap.
hl.init(spark_conf=None, tmp_dir='/path/to/tmp_dir/')
#import the data and sample QC
hl.import_vcf('/path/to/sequence.file.normID.noChrM.vcf.gz',
min_partitions=4,
reference_genome='GRCh38',
force_bgz=True).write('/hailFiles/hail.full.normID.noChrM.mt',
overwrite=True)
mtAll = hl.read_matrix_table('/hailFiles/hail.full.noChrM.mt')
mtAll = mtAll.annotate_entries(AB=(mtAll.AD[1] / hl.sum(mtAll.AD)))
mtAll = hl.sample_qc(mtAll)
mtAll = mtAll.filter_cols((mtAll.sample_qc.call_rate >= 0.97)
& (mtAll.sample_qc.dp_stats.mean >= 20))
mtAll = mtAll.filter_entries((mtAll.GQ >= 20) & (mtAll.DP >= 10) & (
(mtAll.GT.is_hom_ref() & (mtAll.AB <= 0.1))
| (mtAll.GT.is_het() & (mtAll.AB >= 0.25) & (mtAll.AB <= 0.75))
| (mtAll.GT.is_hom_var() & (mtAll.AB >= 0.9))))
hl.export_vcf(mtAll, '/path/to/sequence.file.normID.GTflt.AB.noChrM.vcf.gz')
sample_file=ukb_sf,
index_file_map=file_map,
_row_fields=['rsid'])
# Extracting SNPs of interest
mt_f = hl.filter_intervals(mt, ploci)
mt_f = hl.variant_qc(mt_f)
chromdat['chrompos'] = chromdat['chrom'] + ':' + chromdat[
'hg19_pos'].astype(str)
chromdat_hl = hl.Table.from_pandas(chromdat)
chromdat_hl = chromdat_hl.annotate(
locus=hl.parse_locus(chromdat_hl.chrompos, reference_genome='GRCh37'))
chromdat_hl = chromdat_hl.key_by('locus')
mt_f = mt_f.annotate_rows(**chromdat_hl[mt_f.locus])
flip = hl.case().when(mt_f.ea == mt_f.alleles[0],
True).when(mt_f.ea == mt_f.alleles[1],
False).or_missing()
mt_f = mt_f.annotate_rows(flip=flip)
mt_f = mt_f.annotate_rows(
prior=2 *
hl.if_else(mt_f.flip, mt_f.variant_qc.AF[0], mt_f.variant_qc.AF[1]))
mt_f = mt_f.select_entries(G=hl.coalesce(
hl.if_else(mt_f.flip, 2 - mt_f.GT.n_alt_alleles(),
mt_f.GT.n_alt_alleles()), mt_f.prior))
## Exporting result
output = '/ludc/Home/daniel_c/dva/files/ukbgeno/chrom{}.vcf.bgz'.format(ch)
hl.export_vcf(mt_f, output)
## Removing log file
logfile = glob.glob('*.log')
os.remove(logfile[0])
#! /usr/bin/python import sys import hail as hl n_samples = int(sys.argv[1]) n_variants = int(sys.argv[2]) path = sys.argv[3] mt = hl.balding_nichols_model(1, n_samples, n_variants) mt = mt.key_cols_by(s = hl.str(mt.sample_idx)) mt = mt.annotate_entries(GT = hl.unphased_diploid_gt_index_call(hl.rand_bool(0.5) * 2)) hl.export_vcf(mt, path + ".vcf") hl.export_plink(mt, path)
mt.cohort,
hl.min((hl.agg.count_where(hl.is_missing(mt['GT']))) /
mt.count_rows() * 2)))
mt = mt.annotate_rows(info=mt.info.annotate(
cohort_names=mt.MAF_cohorts.keys()))
mt = mt.annotate_rows(info=mt.info.annotate(
MAF_cohorts_values=mt.MAF_cohorts.values()))
mt = mt.annotate_rows(info=mt.info.annotate(
AN_cohorts_values=mt.AN_cohorts.values()))
mt = mt.annotate_rows(info=mt.info.annotate(
AC_cohorts=mt.AC_cohorts.values()))
mt = mt.annotate_rows(info=mt.info.annotate(
missingness_cohorts_values=mt.missingness_cohorts.values()))
# mt = mt.checkpoint(
# f'{tmp_dir}/Sanger_WES_mt_with_stats.mt', overwrite=True)
hl.export_vcf(
mt,
f'{tmp_dir}/Sanger_WES_chr1-7and20_after_RF_cohort_stats.vcf.bgz',
parallel='separate_header')
mt1 = mt.select_entries()
mt_fin = mt1.filter_cols(mt1['s'] == 'sample')
hl.export_vcf(mt_fin,
f"{tmp_dir}/Sanger_WES_chr1-7and20_stats_for_VEP.vcf.bgz",
parallel='separate_header')
