def get_rates_by_constraint(constraint, cache_dir, threshold=1e-4, ratio=1.0):
''' get mutation rates in and out of constrained regions
'''
rates = {'constrained': [], 'unconstrained': []}
mut_dict = load_mutation_rates()
ensembl = EnsemblRequest(cache_dir, 'grch37')
for tx_id, group in constraint.groupby('transcript'):
tx = construct_gene_object(ensembl, tx_id.split('.')[0])
sites = SiteRates(tx, mut_dict)
constrained_sites = get_constrained_positions(tx, group, threshold,
ratio)
cqs = [
'nonsense', 'missense', 'synonymous', 'splice_lof', 'splice_region'
]
gene_rates = get_gene_rates(tx, sites, cqs, constrained_sites)
# now add the gene rates to the larger list of all genes
for category in ['constrained', 'unconstrained']:
gene_rates[category]['symbol'] = list(group['gene'])[0]
gene_rates[category]['chrom'] = list(group['chr'])[0]
gene_rates[category]['length'] = tx.chrom_pos_to_cds(
tx.get_cds_end())['pos']
rates[category].append(gene_rates[category])
return rates
def main():
args = get_options()
ensembl = EnsemblRequest(args.cache, args.genome_build)
cadd = pysam.TabixFile(args.cadd)
constraint = load_regional_constraint(args.constraint)
# open de novo mutations
all_de_novos = open_mutations(args.de_novos)
mut_dict = load_mutation_rates()
output = open(args.output, 'w')
output.write('symbol\tseverity_p_value\n')
for symbol in all_de_novos:
if symbol in ['', '.']:
continue
print(symbol)
de_novos = all_de_novos[symbol]
p_value = analyse_gene(ensembl, mut_dict, cadd, symbol, de_novos,
constraint, WEIGHTS)
line = '{}\t{}\n'.format(symbol, p_value)
output.write(line)
def annotate_constraint(data, constraint_path, threshold=1e-3, ratio=0.4):
''' annotate per-site rates by whether the site is under regional constraint
'''
# default to unconstrained
data['constrained'] = False
constraint = load_regional_constraint(constraint_path)
mut_dict = load_mutation_rates()
ensembl = EnsemblRequest(cache_dir, 'grch37')
modified = []
for symbol, group in data.groupby('symbol'):
if symbol not in set(constraint['gene']):
sites = set([])
else:
regional = constraint[constraint['gene'] == symbol]
tx_id = list(regional['transcript'])[0]
tx = construct_gene_object(ensembl, tx_id.split('.')[0])
sites = get_constrained_positions(tx, regional, threshold, ratio)
gene_constraint = group['constrained'].copy()
gene_constraint.loc[group['pos'].isin(sites)] = True
group['constrained'] = gene_constraint
modified.append(group)
return pandas.concat(modified)
def main():
args = get_options()
ensembl = EnsemblRequest(args.cache_folder, args.genome_build.lower())
mut_dict = load_mutation_rates(args.rates)
output = open(args.out, "wt")
args.func(ensembl, mut_dict, output, args)
def main():
args = get_options()
ensembl = EnsemblRequest('cache', 'grch37')
mut_dict = load_mutation_rates()
dominant = load_dominant(args.known)
data = pandas.DataFrame(
columns=['symbol', 'chrom', 'pos', 'ref', 'alt', 'cq', 'prob'])
data['pos'] = data['pos'].astype(int)
for symbol in dominant:
print(symbol)
rates = get_gene_rates(symbol, ensembl, mut_dict)
data = data.append(rates, ignore_index=True)
with gzip.open(args.output, 'wt') as handle:
data.to_csv(handle, sep='\t', index=False)
def setUpClass(self):
self.temp_dir = tempfile.mkdtemp()
self.ensembl = EnsemblRequest(self.temp_dir, genome_build="grch37")
class TestEnsemblRequestPy(unittest.TestCase):
""" unit test the EnsemblRequest class
"""
@classmethod
def setUpClass(self):
self.temp_dir = tempfile.mkdtemp()
self.ensembl = EnsemblRequest(self.temp_dir, genome_build="grch37")
@classmethod
def tearDownClass(self):
shutil.rmtree(self.temp_dir)
def test_open_url(self):
""" test that open_url() works correctly
"""
headers = {"Content-Type": "application/json"}
url = "http://rest.ensembl.org/overlap/id/ENSG00000172320?feature=gene"
(response, status_code, headers) = self.ensembl.open_url(url, headers)
response = json.loads(response)
self.assertEqual(status_code, 200)
self.assertEqual(response, [{
"source": "ensembl_havana",
"logic_name": "ensembl_havana_gene",
"feature_type": "gene",
"external_name": "OR5A1",
"seq_region_name": "11",
"strand": 1,
"id": "ENSG00000172320",
"gene_id": "ENSG00000172320",
"version": 3,
"assembly_name": "GRCh38",
"description": "olfactory receptor family 5 subfamily A member 1 [Source:HGNC Symbol;Acc:HGNC:8319]",
"end": 59451380,
"biotype": "protein_coding",
"start": 59436469}]
)
def test_get_genes_for_hgnc_id(self):
""" test that get_genes_for_hgnc_id() works correctly
"""
genes = self.ensembl.get_genes_for_hgnc_id("KMT2A")
self.assertEqual(genes, ['ENSG00000118058', 'ENSG00000267910'])
def test_get_previous_symbol(self):
""" test that get_previous_symbol() works correctly
"""
prev = self.ensembl.get_previous_symbol("KMT2A")
self.assertEqual(prev, ["MLL"])
# make a check for a gene with multiple documents, to check that we
# don't raise an error
prev = self.ensembl.get_previous_symbol("KRT16P1")
self.assertEqual(prev, ["KRT14P"])
def test_get_transcript_ids_for_ensembl_gene_ids(self):
""" test that get_transcript_ids_for_ensembl_gene_ids() works correctly
"""
hgnc = ["KMT2A", "MLL"]
ensg = ['ENSG00000118058', 'ENSG00000267910']
enst = self.ensembl.get_transcript_ids_for_ensembl_gene_ids(ensg, hgnc)
self.assertEqual(set(enst), set(['ENST00000534358', 'ENST00000531904',
'ENST00000389506', 'ENST00000354520', 'ENST00000532204',
'ENST00000529852', 'ENST00000527869', 'ENST00000533790',
'ENST00000392873']))
def test_get_genomic_seq_for_transcript(self):
""" check that get_genomic_seq_for_transcript() works correctly
"""
seq = self.ensembl.get_genomic_seq_for_transcript("ENST00000302030", expand=0)
self.assertEqual(seq, ('11', 59210617, 59211667, '+', 'CTTGTCCTTGTGGTCC'
'ACGGGAAGCATGTCCATAACCAAAGCCTGGAACAGCTCATCAGTGACCATGTTCATCCTCCTGGGA'
'TTCACAGACCATCCAGAACTCCAGGCCCTCCTCTTTGTGACCTTCCTGGGCATCTATCTTACCACC'
'CTGGCCTGGAACCTGGCCCTCATTTTTCTGATCAGAGGTGACACCCATCTGCACACACCCATGTAC'
'TTCTTCCTAAGCAACTTATCTTTCATTGACATCTGCTACTCTTCTGCTGTGGCTCCCAATATGCTC'
'ACTGACTTCTTCTGGGAGCAGAAGACCATATCATTTGTGGGCTGTGCTGCTCAGTTTTTTTTCTTT'
'GTCGGCATGGGTCTGTCTGAGTGCCTCCTCCTGACTGCTATGGCATACGACCGATATGCAGCCATC'
'TCCAGCCCCCTTCTCTACCCCACTATCATGACCCAGGGCCTCTGTACACGCATGGTGGTTGGGGCA'
'TATGTTGGTGGCTTCCTGAGCTCCCTGATCCAGGCCAGCTCCATATTTAGGCTTCACTTTTGCGGA'
'CCCAACATCATCAACCACTTCTTCTGCGACCTCCCACCAGTCCTGGCTCTGTCTTGCTCTGACACC'
'TTCCTCAGTCAAGTGGTGAATTTCCTCGTGGTGGTCACTGTCGGAGGAACATCGTTCCTCCAACTC'
'CTTATCTCCTATGGTTACATAGTGTCTGCGGTCCTGAAGATCCCTTCAGCAGAGGGCCGATGGAAA'
'GCCTGCAACACGTGTGCCTCGCATCTGATGGTGGTGACTCTGCTGTTTGGGACAGCCCTTTTCGTG'
'TACTTGCGACCCAGCTCCAGCTACTTGCTAGGCAGGGACAAGGTGGTGTCTGTTTTCTATTCATTG'
'GTGATCCCCATGCTGAACCCTCTCATTTACAGTTTGAGGAACAAAGAGATCAAGGATGCCCTGTGG'
'AAGGTGTTGGAAAGGAAGAAAGTGTTTTCTTAGGTCATGCGTAGAAACTTATTTATCCAAACTGCT'
'GGAGAATTAAACAATCCAAGCCTTCACCTCCACCTCTGCCTCAGG'))
def test_get_cds_seq_for_transcript(self):
""" check that get_cds_seq_for_transcript() works correctly
"""
seq = self.ensembl.get_cds_seq_for_transcript("ENST00000302030")
self.assertEqual(seq, 'ATGTCCATAACCAAAGCCTGGAACAGCTCATCAGTGACCATGTTCATC'
'CTCCTGGGATTCACAGACCATCCAGAACTCCAGGCCCTCCTCTTTGTGACCTTCCTGGGCATCTAT'
'CTTACCACCCTGGCCTGGAACCTGGCCCTCATTTTTCTGATCAGAGGTGACACCCATCTGCACACA'
'CCCATGTACTTCTTCCTAAGCAACTTATCTTTCATTGACATCTGCTACTCTTCTGCTGTGGCTCCC'
'AATATGCTCACTGACTTCTTCTGGGAGCAGAAGACCATATCATTTGTGGGCTGTGCTGCTCAGTTT'
'TTTTTCTTTGTCGGCATGGGTCTGTCTGAGTGCCTCCTCCTGACTGCTATGGCATACGACCGATAT'
'GCAGCCATCTCCAGCCCCCTTCTCTACCCCACTATCATGACCCAGGGCCTCTGTACACGCATGGTG'
'GTTGGGGCATATGTTGGTGGCTTCCTGAGCTCCCTGATCCAGGCCAGCTCCATATTTAGGCTTCAC'
'TTTTGCGGACCCAACATCATCAACCACTTCTTCTGCGACCTCCCACCAGTCCTGGCTCTGTCTTGC'
'TCTGACACCTTCCTCAGTCAAGTGGTGAATTTCCTCGTGGTGGTCACTGTCGGAGGAACATCGTTC'
'CTCCAACTCCTTATCTCCTATGGTTACATAGTGTCTGCGGTCCTGAAGATCCCTTCAGCAGAGGGC'
'CGATGGAAAGCCTGCAACACGTGTGCCTCGCATCTGATGGTGGTGACTCTGCTGTTTGGGACAGCC'
'CTTTTCGTGTACTTGCGACCCAGCTCCAGCTACTTGCTAGGCAGGGACAAGGTGGTGTCTGTTTTC'
'TATTCATTGGTGATCCCCATGCTGAACCCTCTCATTTACAGTTTGAGGAACAAAGAGATCAAGGAT'
'GCCCTGTGGAAGGTGTTGGAAAGGAAGAAAGTGTTTTCTTAG')
def test_get_protein_seq_for_transcript(self):
""" test that get_protein_seq_for_transcript() works correctly
"""
seq = self.ensembl.get_protein_seq_for_transcript("ENST00000302030")
self.assertEqual(seq, 'MSITKAWNSSSVTMFILLGFTDHPELQALLFVTFLGIYLTTLAWNLAL'
'IFLIRGDTHLHTPMYFFLSNLSFIDICYSSAVAPNMLTDFFWEQKTISFVGCAAQFFFFVGMGLSE'
'CLLLTAMAYDRYAAISSPLLYPTIMTQGLCTRMVVGAYVGGFLSSLIQASSIFRLHFCGPNIINHF'
'FCDLPPVLALSCSDTFLSQVVNFLVVVTVGGTSFLQLLISYGYIVSAVLKIPSAEGRWKACNTCAS'
'HLMVVTLLFGTALFVYLRPSSSYLLGRDKVVSVFYSLVIPMLNPLIYSLRNKEIKDALWKVLERKK'
'VFS')
def test_get_genomic_seq_for_region(self):
""" test that get_genomic_seq_for_region() works correctly
"""
# not that this test uses GRCh37 coordinates
seq = self.ensembl.get_genomic_seq_for_region('11', 59210617, 59210637)
self.assertEqual(seq, 'CTTGTCCTTGTGGTCCACGGG')
def test_get_chrom_for_transcript(self):
""" test that get_chrom_for_transcript() works correctly
"""
chrom = self.ensembl.get_chrom_for_transcript("ENST00000534358", "KMT2A")
self.assertEqual(chrom, "11")
def test_get_exon_ranges_for_transcript(self):
""" test that get_exon_ranges_for_transcript() works correctly
"""
exons = self.ensembl.get_exon_ranges_for_transcript("ENST00000534358")
self.assertEqual(exons, [(118307205, 118307659), (118339490, 118339559),
(118342377, 118345030), (118347520, 118347697), (118348682, 118348916),
(118350889, 118350953), (118352430, 118352807), (118353137, 118353210),
(118354898, 118355029), (118355577, 118355690), (118359329, 118359475),
(118360507, 118360602), (118360844, 118360964), (118361911, 118362033),
(118362459, 118362643), (118363772, 118363945), (118365003, 118365113),
(118365409, 118365482), (118366415, 118366608), (118366976, 118367082),
(118368651, 118368788), (118369085, 118369243), (118370018, 118370135),
(118370550, 118370628), (118371702, 118371862), (118372387, 118372572),
(118373113, 118377361), (118378244, 118378324), (118379851, 118379915),
(118380663, 118380833), (118382666, 118382740), (118390333, 118390507),
(118390672, 118390779), (118391517, 118391600), (118392003, 118392132),
(118392612, 118397539)])
def test_get_cds_ranges_for_transcript(self):
""" tets that get_cds_ranges_for_transcript() works correctly
"""
cds = self.ensembl.get_cds_ranges_for_transcript("ENST00000534358")
self.assertEqual(cds, [(118307228, 118307659), (118339490, 118339559),
(118342377, 118345030), (118347520, 118347697), (118348682, 118348916),
(118350889, 118350953), (118352430, 118352807), (118353137, 118353210),
(118354898, 118355029), (118355577, 118355690), (118359329, 118359475),
(118360507, 118360602), (118360844, 118360964), (118361911, 118362033),
(118362459, 118362643), (118363772, 118363945), (118365003, 118365113),
(118365409, 118365482), (118366415, 118366608), (118366976, 118367082),
(118368651, 118368788), (118369085, 118369243), (118370018, 118370135),
(118370550, 118370628), (118371702, 118371862), (118372387, 118372572),
(118373113, 118377361), (118378244, 118378324), (118379851, 118379915),
(118380663, 118380833), (118382666, 118382740), (118390333, 118390507),
(118390672, 118390779), (118391517, 118391600), (118392003, 118392132),
(118392612, 118392887)])
def test_rate_limit_ensembl_requests(self):
""" test that rate_limit_ensembl_requests() works correctly
"""
current_time = time.time()
self.ensembl.prior_time = current_time
self.ensembl.rate_limit_ensembl_requests()
delta = self.ensembl.prior_time - current_time
self.assertTrue(delta >= self.ensembl.rate_limit)
def cluster_de_novos(symbol,
de_novos,
iterations=1000000,
ensembl=None,
mut_dict=None):
""" analysis proximity cluster of de novos in a single gene
Args:
symbol: HGNC symbol for a gene
de_novos: dictionary of de novo positions for the HGNC gene,
indexed by functional type
iterations: number of simulations to run
ensembl: EnsemblRequest object, for obtaing info from ensembl
mut_dict: dictionary of mutation rates, indexed by trinuclotide sequence
Returns:
a dictionary containing P values, and distances for missense, nonsense,
and synonymous de novos events. Missing data is represented by "NA".
"""
if ensembl is None:
ensembl = EnsemblRequest('cache', 'grch37')
if mut_dict is None:
mut_dict = load_mutation_rates()
missense = de_novos["missense"]
nonsense = de_novos["nonsense"]
# load the set of transcripts that are the minimum set of transcripts
# required to contain all the de novos, unless we can't find any coding
# transcripts that contain the de novos.
try:
transcripts = load_gene(ensembl, symbol, missense + nonsense)
except IndexError as e:
print(e)
return None
probs = {"miss_prob": [], "nons_prob": []}
dists = {"miss_dist": [], "nons_dist": []}
for transcript in transcripts:
missense_events = get_de_novos_in_transcript(transcript, missense)
nonsense_events = get_de_novos_in_transcript(transcript, nonsense)
rates = SiteRates(transcript, mut_dict)
(miss_dist, miss_prob) = get_p_value(transcript, rates, iterations,
"missense", missense_events)
(nons_dist, nons_prob) = get_p_value(transcript, rates, iterations,
"lof", nonsense_events)
dists["miss_dist"].append(miss_dist)
dists["nons_dist"].append(nons_dist)
probs["miss_prob"].append(miss_prob)
probs["nons_prob"].append(nons_prob)
# remove the de novos analysed in the current transcript, so that
# analysis of subsequent transcripts uses independent events. NOTE THAT
# THIS MIGHT MISS SOME CLUSTERING ACROSS MUTUALLY EXCLUSIVE TRANSCRIPTS
# IF THE DE NOVO EVENTS ARE NEAR THE TRANSCRIPT DIVERGENCE.
missense = [x for x in missense if x not in missense_events]
nonsense = [x for x in nonsense if x not in nonsense_events]
for key in dists:
dists[key] = ",".join([str(x) for x in dists[key]])
probs = combine_p_values(probs)
probs.update(dists)
return probs
def setUpClass(cls):
cls.temp_dir = tempfile.mkdtemp()
cls.ensembl = EnsemblRequest(cls.temp_dir, 'grch37')
cls.mut_dict = load_mutation_rates()