def signup():
check_args(g.args, 'name', 'password')
u = user.new_user(
g.args['name'],
g.args['password'],
g.args)
return {'message': 'signup successful', 'user': u}
def user_login():
"""get a token to use for authentication throughout the rest of the site"""
#NOTE: no permission required for this part because it uses an
#alternative login method (username & password rather than token)
#and declares the user object on its own
#CONSIDER: add a delay for password based login to prevent excessive attempts
check_args('username', 'password')
g.user = user.auth(g.args['username'], g.args['password'],
ip=request.remote_addr)
if not g.user:
raise ex.Unauthorized('Bad username or password.')
return {
'message': 'login successful',
'token': g.user.token,
}
def vote(update, context):
logging.info('vote command called')
context.bot.deleteMessage(chat_id=update.effective_chat.id, message_id=update.message.message_id)
if check_args(update, context, "/vote", ["EVENT", "LOCATION"], operator.lt, len(context.args)):
user = update.message.from_user
message = gen_message.gen_vote(user.first_name, context.args)
if message:
context.bot.send_message(chat_id=update.effective_chat.id, text=message)
def user_login():
"""get a token to use for authentication throughout the rest of the site"""
#NOTE: no permission required for this part because it uses an
#alternative login method (username & password rather than token)
#and declares the user object on its own
#CONSIDER: add a delay for password based login to prevent excessive attempts
check_args('username', 'password')
g.user = user.auth(g.args['username'],
g.args['password'],
ip=request.remote_addr)
if not g.user:
raise ex.Unauthorized('Bad username or password.')
return {
'message': 'login successful',
'token': g.user.token,
}
def get_patients_variants(**kwargs):
'''
change genotype_df's data format into a dictionary, so gnomad
and cadd can be added.
return result
{
patients:{p:[v1,v2]}
variants:{v1:{
gnomad_af:
gnomad_hom_f:
cadd:
}}
}
'''
# define result
result = {'patients': defaultdict(list), 'variants': {}}
compulsory_keys = {
'genotype_df',
'gnomad_freqs',
}
# check args
helper.check_args(compulsory_keys, kwargs, 'get_patients_variants')
# change the data structure to the result format
for col in list(kwargs['genotype_df']):
this = kwargs['genotype_df'][col]
# there might be a way to merge the following two steps into one
# + het
result['patients'][col].extend(this[this == 1].index)
# + hom
result['patients'][col].extend(list(this[this == 2].index) * 2)
result['variants'] = {
i: {
'gnomad_af': kwargs['gnomad_freqs'][i]['gnomad_af'],
'gnomad_an': kwargs['gnomad_freqs'][i]['gnomad_an'],
'gnomad_ac': kwargs['gnomad_freqs'][i]['gnomad_ac'],
'gnomad_hom_c': kwargs['gnomad_freqs'][i]['gnomad_hom_c'],
'gnomad_hom_f': kwargs['gnomad_freqs'][i]['gnomad_hom_f'],
'cadd': None,
}
for i in kwargs['genotype_df'].index
}
return result
def training(update, context):
logging.info('training command called')
context.bot.deleteMessage(chat_id=update.effective_chat.id, message_id=update.message.message_id)
if check_args(update, context, "/training", ["[LOCATION]"], operator.lt, len(context.args)):
user = update.message.from_user
message = gen_message.gen_training(user.first_name, context.args)
keyboard = [[InlineKeyboardButton("Join", callback_data="train" + str(user.first_name))]]
reply_markup = InlineKeyboardMarkup(keyboard)
if message:
context.bot.send_message(chat_id=update.effective_chat.id, text=message, reply_markup=reply_markup)
def notraining(update, context):
logging.info('notraining command called')
context.bot.deleteMessage(chat_id=update.effective_chat.id, message_id=update.message.message_id)
user = update.message.from_user
message = ""
if len(context.args) == 0:
message = gen_message.gen_notraining_0(user.first_name)
elif check_args(update, context, "/notraining", ["TIME", "LOCATION"], operator.lt, len(context.args)):
message = gen_message.gen_notraining(user.first_name, context.args)
if message:
context.bot.send_message(chat_id=update.effective_chat.id, text=message)
def rm_vote(update, context):
context.bot.deleteMessage(chat_id=update.effective_chat.id, message_id=update.message.message_id)
logging.info('removevote command called')
user = update.message.from_user
message = ""
if len(context.args) == 0:
message = gen_message.gen_rm_vote_0(user.first_name)
elif check_args(update, context, "/removevote", ["EVENT"], operator.lt, len(context.args)):
message = gen_message.gen_rm_vote(user.first_name, context.args)
if message:
context.bot.send_message(chat_id=update.effective_chat.id, text=message)
def notomorrowtraining(update, context):
logging.info('notomorrowtraining command called')
context.bot.deleteMessage(chat_id=update.effective_chat.id, message_id=update.message.message_id)
user = update.message.from_user
message = ""
if len(context.args) == 0:
message = gen_message.gen_notraining_0(user.first_name,
day=f"{date.today() + timedelta(days=1)}.train", daystring=" tomorrow")
elif check_args(update, context, "/notomorrowtraining", ["TIME", "LOCATION"], operator.lt, len(context.args)):
message = gen_message.gen_notraining(user.first_name, context.args,
day=f"{date.today() + timedelta(days=1)}.train", daystring="tomorrow ")
if message:
context.bot.send_message(chat_id=update.effective_chat.id, text=message)
def tomorrowtraining(update, context):
logging.info('tomorrowtraining command called')
context.bot.deleteMessage(chat_id=update.effective_chat.id, message_id=update.message.message_id)
if check_args(update, context, "/tomorrowtraining", ["TIME", "LOCATION"], operator.lt, len(context.args)):
user = update.message.from_user
reply_markup = None
message = gen_message.gen_training(user.first_name, context.args,
day=f"{date.today() + timedelta(days=1)}.train", daystring="tomorrow ")
if message != "Please specify time in the format HH:MM":
keyboard = [[InlineKeyboardButton("Join", callback_data="train" + str(user.first_name))]]
reply_markup = InlineKeyboardMarkup(keyboard)
if message:
context.bot.send_message(chat_id=update.effective_chat.id, text=message, reply_markup=reply_markup)
def main(**kwargs):
'''
parameters:
genes: optional
N (selecting HPO with at least N Ph. affecting both \
#positive (selecting parental HPO in the positive set \
#and negative set)
vcf file location
gnomad files location
patient_mini, patient_info, both are json files
cadd path
unrelated file used to subset vcf file
v cutoff and p cutoff are to remove variants and patients with \
#low coverage over the gene
returns hpo goodness of fit score, p_g (gnomad_freq
'''
# check args
compulsory_keys = {
'N',
'gnomad_path',
'patient_mini_file',
'patient_info_file',
'unrelated_file',
'gnomad_cutoff',
'gnomad_step',
'gnomad_path',
'hpo_mask',
'cadd_step',
'cadd_min',
}
helper.check_args(compulsory_keys, kwargs, 'main')
# defaults
kwargs.setdefault('gene_inheritance_mode', {})
# get patient_mini and patient_info
patient_info = helper.get_snapshot(kwargs['patient_info_file'])
# get p_h for all hpos
phs = helper.get_phs(patient_info)
# get patient_map
patient_map = PM.main(**kwargs)
if patient_map is None:
return None
modes = 'rd'
# translate patient_map's key
pm = {}
for m in modes:
pm[m] = {}
for k, v in patient_map['patient_map'][m].items():
key = tuple([int(i) for i in k.split(',')])
pm[m][key] = v
phenogenon_cache = {'r': {}, 'd': {}}
# get phenogenon sums on the first gnomad bin.
# get all testable hpos
hpos = [
i for i, v in phs.items()
if v >= kwargs['N'] and i not in kwargs['hpo_mask']
]
for hpo in hpos:
# inheritance mode: r and d
# Note that for each HPO, it only keeps the inheritance mode
# with the higher hgf score
for mode in modes:
args = dict(
hpos=hpo,
mode=mode,
patient_info=patient_info,
patient_map=pm[mode],
)
genon = helper.phenogenon(**args)
phenogenon_cache[mode][hpo] = genon.tolist()
return {
'phenogenon': phenogenon_cache,
'NP': patient_map['NP'],
'patient_map': patient_map['patient_map'],
'patients_variants': patient_map['patients_variants'],
}
def main(**kwargs):
'''
parameters:
genes: optional
N (selecting HPO with at least N Ph. affecting both \
#positive (selecting parental HPO in the positive set \
#and negative set)
vcf file location
gnomad files location
patient_mini, patient_info, both are json files
cadd path
unrelated file used to subset vcf file
v cutoff and p cutoff are to remove variants and patients with \
#low coverage over the gene
returns hpo goodness of fit score, p_g (gnomad_freq
'''
# check args
compulsory_keys = {
'remove_nc',
'vcf_file',
'gnomad_path',
'cadd_file',
'patient_mini_file',
'patient_info_file',
'human_fasta_ref',
'unrelated_file',
'v_cutoff',
'p_cutoff', # not using this since no phasing is done
'gnomad_cutoff',
'gnomad_step',
'gnomad_path',
'cadd_step',
'cadd_min',
'cis_gap',
}
helper.check_args(compulsory_keys, kwargs, 'main')
# defaults
kwargs.setdefault('gene_inheritance_mode', {})
# get patient_mini and patient_info
gnomad_steps = np.arange(0,
kwargs['gnomad_cutoff'] + kwargs['gnomad_step'],
kwargs['gnomad_step'])
cadd_steps = np.arange(kwargs['cadd_min'], 60, kwargs['cadd_step'])
# for each gene, get all valid variants/patients according to p/v_cutoff,
# annotate using gnomad
# then get patients_variants, with variants annotated with
# gnomad freqs and cadd
patients_variants = PV.main(**kwargs)
# get patient_map for recessive and dominant modes
args = dict(
data=patients_variants,
vcf=patients_variants['cover_df'],
gnomad_steps=gnomad_steps,
cadd_steps=cadd_steps,
cis_gap=kwargs['cis_gap'],
)
patient_map = {'r': {}, 'd': {}}
# first get mode if provided. Note that the keys could be id
# or gene name
modes = 'rd'
# get number of patients who carry rare variants when get patient_maps
NP = {}
for mode in modes:
args['mode'] = mode
M = helper.get_patient_map(**args)
NP[mode] = len(
set(
list(
itertools.chain.from_iterable(
[v[0] for k, v in M.items() if k[1] == 0]))))
# change the keys to a string
for k, v in M.items():
patient_map[mode]['{},{}'.format(k[0],
k[1])] = [list(v[0]),
list(v[1])]
return {
'patient_map': patient_map,
'patients_variants': patients_variants,
'NP': NP,
}
def main(**kwargs):
'''
parameters:
genes: optional
N (selecting HPO with at least N Ph. affecting both \
#positive (selecting parental HPO in the positive set \
#and negative set)
vcf file location
gnomad files location
patient_mini, patient_info, both are json files
cadd path
unrelated file used to subset vcf file
v cutoff and p cutoff are to remove variants and patients with \
#low coverage over the gene
returns hpo goodness of fit score, p_g (gnomad_freq
'''
# check args
compulsory_keys = {
'N',
'gnomad_path',
'patient_mini_file',
'patient_info_file',
'unrelated_file',
'gnomad_cutoff',
'gnomad_step',
'gnomad_path',
'hpo_mask',
'cadd_step',
'cadd_min',
'output',
}
helper.check_args(compulsory_keys, kwargs, 'main')
# defaults
kwargs.setdefault('gene_inheritance_mode',{})
# output already exist?
if os.path.isfile(kwargs['output']):
print('already done')
return None
# get patient_mini and patient_info
patient_info = helper.get_snapshot(kwargs['patient_info_file'])
patient_mini = helper.get_snapshot(kwargs['patient_mini_file'])
# get p_h for all hpos
phs = helper.get_phs(patient_info)
# add cohort info into patient_mini
all_p = MONGO['patient_db'].patients.find({'external_id':{'$in':patient_mini.keys()}},{'external_id':1,'contact':1})
for i in all_p:
# !!!! this belongs to UCLex's problem!!! remove if publish
# JingYu and BLACK to UKIRDC, KELSELL to DavidKelsell
contactdict = dict(
JingYu = 'UKIRDC',
Black = 'UKIRDC',
KELSELL = 'DavidKelsell',
TonySegal = 'SEGAL',
SanjaySisodiya = 'SISODIYA',
)
contact = i['contact']['user_id']
contact = contactdict.get(contact,contact)
patient_mini[i['external_id']] = {'hpo': patient_mini[i['external_id']],
'contact': contact}
# get hpodb from json
hpo_db = get_hpo_from_json('../tests/data/new-hpo-hpo.json')
# get genes, if not provided. get all gene_ids from mongodb, \
#if provided, convert to gene_id
fields = {
'gene_id':1,
'gene_name':1,
'_id':0,
'chrom':1,
'start':1,
'stop':1,
'xstart':1,
'xstop':1,
}
this = {}
gene_ranges = get_chrom_genes(kwargs['chrom'], fields, MONGO['phenopolis_db'])
# get gnomad and cadd steps
gnomad_steps = np.arange(
0,
kwargs['gnomad_cutoff']+kwargs['gnomad_step'],
kwargs['gnomad_step']
)
cadd_steps = np.arange(kwargs['cadd_min'], 60, kwargs['cadd_step'])
# get patient_maps
with open(
os.path.join(
kwargs['patient_maps_path'],
'{}.json'.format(kwargs['chrom'])
),
'r',
) as inf:
patient_maps = json.load(inf)
# for each gene, get all valid variants/patients according to p/v_cutoff,
# annotate using gnomad
result = {}
number_processed = 0
coding_variants = None
outf = open(kwargs['output'], 'w')
outf.write('{')
for gene_range in gene_ranges:
# get patient_map
patient_map = patient_maps.pop(gene_range['gene_id'], None)
if patient_map is None:
continue
# print progress
number_processed += 1
if not number_processed % 100:
print('===processed {} genes==='.format(number_processed))
print('processing {}'.format(gene_range['gene_name']))
modes = kwargs['gene_inheritance_mode'].get(
gene_range['gene_name'],
kwargs['gene_inheritance_mode'].get(
gene_range['gene_id'],
'rd'
)
)
# translate patient_map's key
pm = {}
for m in modes:
pm[m] = {}
for k,v in patient_map['patient_map'][m].items():
key = tuple([int(i) for i in k.split(',')])
pm[m][key] = v
phenogenon_cache = {'r':{},'d':{}}
# get phenogenon sums on the first gnomad bin.
# get all testable hpos
hpos_to_test = None
if kwargs['hpos_to_test']:
hpos_to_test = [i for i in kwargs['hpos_to_test']
if i not in kwargs['hpo_mask']]
else:
hpos_to_test = [i for i,v in phs.items()
if v >= kwargs['N']
and i not in kwargs['hpo_mask']]
for hpos in hpos_to_test:
# inheritance mode: r and d
# Note that for each HPO, it only keeps the inheritance mode
# with the higher hgf score
for mode in modes:
args = dict(
hpos = hpos,
mode = mode,
patient_info = patient_info,
patient_map = pm[mode],
)
genon = helper.phenogenon(**args)
phenogenon_cache[mode][hpos] = genon.tolist()
output = json.dumps({
gene_range['gene_id']:{
'symbol': gene_range['gene_name'],
'phenogenon': phenogenon_cache,
'NP': patient_map['NP'],
}
})
# strip off the braces
output = output[1:-1]
if number_processed != 1:
# meaning not the first record. add a comma
output = ',' + output
outf.write(output)
# close cursor
gene_ranges.close()
outf.write('}')
outf.close()
def main(**kwargs):
'''
parameters:
genes: optional
N (selecting HPO with at least N Ph. affecting both \
#positive (selecting parental HPO in the positive set \
#and negative set)
vcf file location
gnomad files location
patient_mini, patient_info, both are json files
cadd path
unrelated file used to subset vcf file
v cutoff and p cutoff are to remove variants and patients with \
#low coverage over the gene
returns hpo goodness of fit score, p_g (gnomad_freq
'''
# check args
compulsory_keys = {
'remove_nc',
'vcf_file',
'gnomad_path',
'cadd_file',
'patient_mini_file',
'patient_info_file',
'human_fasta_ref',
'unrelated_file',
'v_cutoff',
'p_cutoff', # not using this since no phasing is done
'gnomad_cutoff',
'gnomad_step',
'gnomad_path',
'cadd_step',
'cadd_min',
'genon_sum_cutoff_coefficient',
'cis_gap',
'output',
}
helper.check_args(compulsory_keys, kwargs, 'main')
# defaults
kwargs.setdefault('gene_inheritance_mode', {})
# get patient_mini and patient_info
patient_info = helper.get_snapshot(kwargs['patient_info_file'])
patient_mini = helper.get_snapshot(kwargs['patient_mini_file'])
# get p_h for all hpos
phs = helper.get_phs(patient_info)
# add cohort info into patient_mini
all_p = MONGO['patient_db'].patients.find(
{'external_id': {
'$in': patient_mini.keys()
}}, {
'external_id': 1,
'contact': 1
})
for i in all_p:
# !!!! this belongs to UCLex's problem!!! remove if publish
# JingYu and BLACK to UKIRDC, KELSELL to DavidKelsell
contactdict = dict(
JingYu='UKIRDC',
Black='UKIRDC',
KELSELL='DavidKelsell',
TonySegal='SEGAL',
SanjaySisodiya='SISODIYA',
)
contact = i['contact']['user_id']
contact = contactdict.get(contact, contact)
patient_mini[i['external_id']] = {
'hpo': patient_mini[i['external_id']],
'contact': contact
}
# get genes, if not provided. get all gene_ids from mongodb, \
#if provided, convert to gene_id
fields = {
'gene_id': 1,
'gene_name': 1,
'_id': 0,
'chrom': 1,
'start': 1,
'stop': 1,
'xstart': 1,
'xstop': 1,
}
this = {}
if kwargs.get('genes', None) is not None:
genes = phenopolis_utils.symbols_to_ids(kwargs['genes'],
MONGO['phenopolis_db'])
this = {'gene_id': {'$in': genes}}
# sometimes the cursor times out.
# but do remember to close it
if kwargs.get('chrom', None) is not None:
gene_ranges = get_chrom_genes(kwargs['chrom'], fields,
MONGO['phenopolis_db'])
#gene_ranges = get_chrom_genes_with_jq(kwargs['chrom'],kwargs['uclex_genes_json'])
else:
gene_ranges = MONGO['phenopolis_db'].genes.find(this,
fields,
no_cursor_timeout=True)
# get gnomad and cadd steps
gnomad_steps = np.arange(0,
kwargs['gnomad_cutoff'] + kwargs['gnomad_step'],
kwargs['gnomad_step'])
cadd_steps = np.arange(kwargs['cadd_min'], 60, kwargs['cadd_step'])
# for each gene, get all valid variants/patients according to p/v_cutoff,
# annotate using gnomad
result = {}
number_processed = 0
last_chrom = None
coding_variants = None
for gene_range in gene_ranges:
# print progress
number_processed += 1
if not number_processed % 100:
print('===processed {} genes==='.format(number_processed))
print('processing {}'.format(gene_range['gene_name']))
# first parse vcf file to get genotype and coverage for each variant
vcf_file = kwargs['vcf_file'].format(gene_range['chrom'])
args = dict(
vcf_file=vcf_file,
chrom=gene_range['chrom'],
start=gene_range['start'],
stop=gene_range['stop'],
unrelated_file=kwargs['unrelated_file'],
human_fasta_ref=kwargs['human_fasta_ref'],
v_cutoff=kwargs['v_cutoff'],
p_cutoff=kwargs['p_cutoff'],
gnomad_path=kwargs['gnomad_path'],
gnomad_cutoff=kwargs['gnomad_cutoff'],
patient_mini=patient_mini,
)
vcf_dfs = get_vcf_df(**args)
if vcf_dfs is None:
# no variants, continue
continue
# get coding variants if last_chrom != this_chrom
if kwargs['remove_nc'] and gene_range['chrom'] != last_chrom:
coding_variant_file = '/cluster/project8/vyp/JingYu/git/phenopolis_analysis/data/public/vcf/chr{}.coding.tsv'.format(
gene_range['chrom'])
coding_variants = get_coding_variants(coding_variant_file)
last_chrom = gene_range['chrom']
'genon_sum_cutoff_coefficient',
genotype_df, cover_df, gnomad_freqs = vcf_dfs
# then get patients_variants, with variants annotated with
# gnomad freqs and cadd
args = dict(
gnomad_freqs=gnomad_freqs,
genotype_df=genotype_df,
)
patients_variants = get_patients_variants(**args)
# remove noncoding?
if kwargs['remove_nc']:
print(len(patients_variants['variants']))
helper.remove_noncoding(gene_range['gene_id'], patients_variants,
coding_variants)
print(len(patients_variants['variants']))
# if no variants left, skip
if not patients_variants['variants']: continue
# for each gene, remove batch-specific variants
args = dict(
data=patients_variants,
patient_mini=patient_mini,
)
batch_specific_variants = helper.get_batch_artefacts(**args)
patients_variants = helper.remove_batch_artefacts(
patients_variants,
batch_specific_variants,
patient_mini,
)
# add cadd
args = dict(
variants=patients_variants['variants'],
chrom=gene_range['chrom'],
start=gene_range['start'],
stop=gene_range['stop'],
cadd_file=kwargs['cadd_file'],
)
cadds = helper.add_cadd(**args)
for k, v in cadds.items():
patients_variants['variants'][k]['cadd'] = v
# when two variants are in cis and both appear in one patient,
# discard the variant with lower cadd in that patient
# example SDK1 ('7-3990565-C-T','7-4014039-C-T')
# for now, only focus on variants with hom_f < 0.00025
remove_cis(patients_variants, genotype_df)
# get patient_map for recessive and dominant modes
args = dict(
data=patients_variants,
vcf=cover_df,
gnomad_steps=gnomad_steps,
cadd_steps=cadd_steps,
cis_gap=kwargs['cis_gap'],
)
patient_map = {'r': {}, 'd': {}}
# first get mode if provided. Note that the keys could be id
# or gene name
modes = kwargs['gene_inheritance_mode'].get(
gene_range['gene_name'],
kwargs['gene_inheritance_mode'].get(gene_range['gene_id'], 'rd'))
# get number of patients who carry rare variants when get patient_maps
NP = {}
for mode in modes:
args['mode'] = mode
M = helper.get_patient_map(**args)
NP[mode] = len(
set(
list(
itertools.chain.from_iterable(
[v[0] for k, v in M.items() if k[1] == 0]))))
# change the keys to a string
for k, v in M.items():
patient_map[mode]['{},{}'.format(
k[0], k[1])] = [list(v[0]), list(v[1])]
result[gene_range['gene_id']] = {
'symbol': gene_range['gene_name'],
'patient_map': patient_map,
'NP': NP,
}
# close cursor
gene_ranges.close()
# write everything to output
with open(kwargs['output'], 'wb') as outf:
json.dump(result, outf)
def get_vcf_df(**kwargs):
'''
use bcf tools to subset variants and patients. then according to
p/v_cutoff to get bad_vs, bad_ps to remove
'''
compulsory_keys = {
'vcf_file',
'chrom',
'start',
'stop',
'unrelated_file',
'human_fasta_ref',
'v_cutoff',
'gnomad_cutoff',
'p_cutoff',
'patient_mini',
}
# check args
helper.check_args(compulsory_keys, kwargs, 'get_vcf_df')
position = '{chrom}:{start}-{stop}'.format(**kwargs)
ps1 = subprocess.Popen(('tabix', '-h', kwargs['vcf_file'], position),
stdout=subprocess.PIPE)
# subset on unrelated samples, and normalise
ps2 = subprocess.Popen(('bcftools', 'view', '-Ou', '-S',
kwargs['unrelated_file'], '-f', 'PASS'),
stdin=ps1.stdout,
stdout=subprocess.PIPE)
ps3 = subprocess.Popen(('bcftools', 'norm', '-Ou', '-m', '-any'),
stdin=ps2.stdout,
stdout=subprocess.PIPE)
normed_vcf = subprocess.check_output(
['bcftools', 'norm', '-Ov', '-f', kwargs['human_fasta_ref']],
stdin=ps3.stdout)
# get vcf df. genotype -1 = missing, 0 = wildtype, 1 = het, 2 = hom
genotype_df = read_vcf(normed_vcf)
# empty vcf? early return
if genotype_df.empty:
return None
# get poorly covered variants and individuals
# change df to cover_df
cover_df = genotype_df.copy()
cover_df[cover_df >= 0] = 1
cover_df[cover_df == -1] = 0
pm = cover_df.mean()
# rid of patients not in patient_mini
bad_ps = set(pm[pm < kwargs['p_cutoff']].index)
bad_ps.update(set(pm.index) - set(kwargs['patient_mini'].keys()))
vm = cover_df.T.mean()
bad_vs = set(vm[vm < kwargs['v_cutoff']].index)
# annotate vs with gnomad
vs = (i for i in vm.index if i not in bad_vs)
gnomad_freqs = gnomad_utils.overall_freqs(vs, kwargs['gnomad_path'])
# remove variants with 'SEGDUP' filter. This gives a lot of noise for recessive
# analysis. For example IGHV3-38 - ENST00000390618, 14-106866588-T-C
bad_vs.update([
i for i, v in gnomad_freqs.items() if v['filters']['exome'] is not None
and 'SEGDUP' in v['filters']['exome'] or v['filters']['genome']
is not None and 'SEGDUP' in v['filters']['genome']
])
# in fact, many variants have very high af, but 0 hom_f, such as
# 6-32548641-A-T, which has no 'SEGDUP' filter. Remove those
# hard filtering for the time being. There might be better ways
bad_vs.update([
i for i, v in gnomad_freqs.items()
if v['gnomad_af'] > 0.01 and v['gnomad_hom_f'] == 0.0
])
# add to bad_vs gnomad_hom_af >= gnomad_cutoff,
# and those not covered by gnomad_path
# Note that if gnomad_hom_af >= gnomad_cutoff, then gnomad_af >= gnomad_cutoff
# but not vice versa
#this = [i for i,v in gnomad_freqs.items()
# if v['gnomad_af'] is None or v['gnomad_hom_f'] >= kwargs['gnomad_cutoff']]
bad_vs.update([
i for i, v in gnomad_freqs.items() if v['gnomad_af'] is None
or v['gnomad_hom_f'] >= kwargs['gnomad_cutoff']
])
vs_count = np.sum(genotype_df[genotype_df > 0], axis=1)
bad_vs.update([
i for i in gnomad_freqs
if vs_count[i] > 3 and gnomad_freqs[i]['pop_filter']
])
# then drop bad_ps and bad_vs
genotype_df.drop(bad_vs, inplace=True)
genotype_df.drop(bad_ps, inplace=True, axis=1)
return (genotype_df, cover_df, gnomad_freqs)
def main(**kwargs):
'''
parameters:
genes: optional
N (selecting HPO with at least N Ph. affecting both \
#positive (selecting parental HPO in the positive set \
#and negative set)
vcf file location
gnomad files location
patient_mini, patient_info, both are json files
cadd path
unrelated file used to subset vcf file
v cutoff and p cutoff are to remove variants and patients with \
#low coverage over the gene
returns hpo goodness of fit score, p_g (gnomad_freq
'''
# check args
compulsory_keys = {
'remove_nc',
'vcf_file',
'gnomad_path',
'cadd_file',
'patient_mini_file',
'patient_info_file',
'human_fasta_ref',
'unrelated_file',
'v_cutoff',
'p_cutoff', # not using this since no phasing is done
'gnomad_cutoff',
'gnomad_step',
'gnomad_path',
'cadd_step',
'cadd_min',
'cis_gap',
}
helper.check_args(compulsory_keys, kwargs, 'main')
# defaults
kwargs.setdefault('gene_inheritance_mode', {})
# get patient_mini and patient_info
patient_info = helper.get_snapshot(kwargs['patient_info_file'])
patient_mini = helper.get_snapshot(kwargs['patient_mini_file'])
# get p_h for all hpos
phs = helper.get_phs(patient_info)
# get genes, if not provided. get all gene_ids from mongodb, \
#if provided, convert to gene_id
fields = {
'gene_id': 1,
'gene_name': 1,
'_id': 0,
'chrom': 1,
'start': 1,
'stop': 1,
'xstart': 1,
'xstop': 1,
}
this = {}
# get gnomad and cadd steps
gnomad_steps = np.arange(0,
kwargs['gnomad_cutoff'] + kwargs['gnomad_step'],
kwargs['gnomad_step'])
cadd_steps = np.arange(kwargs['cadd_min'], 60, kwargs['cadd_step'])
# for each gene, get all valid variants/patients according to p/v_cutoff,
# annotate using gnomad
# use PV to record patients_variants
chrom, crange = kwargs['range'].split(':')
start, stop = crange.split('-')
# first parse vcf file to get genotype and coverage for each variant
vcf_file = kwargs['vcf_file'].format(chrom)
args = dict(
vcf_file=vcf_file,
chrom=chrom,
start=start,
stop=stop,
unrelated_file=kwargs['unrelated_file'],
human_fasta_ref=kwargs['human_fasta_ref'],
v_cutoff=kwargs['v_cutoff'],
p_cutoff=kwargs['p_cutoff'],
gnomad_path=kwargs['gnomad_path'],
gnomad_cutoff=kwargs['gnomad_cutoff'],
patient_mini=patient_mini,
)
vcf_dfs = get_vcf_df(**args)
if vcf_dfs is None:
# no variants, continue
return None
genotype_df, cover_df, gnomad_freqs = vcf_dfs
# then get patients_variants, with variants annotated with
# gnomad freqs and cadd
args = dict(
gnomad_freqs=gnomad_freqs,
genotype_df=genotype_df,
)
patients_variants = get_patients_variants(**args)
# remove noncoding?
if kwargs['remove_nc']:
helper.remove_noncoding(patients_variants, kwargs)
# if no variants left, skip
if not patients_variants['variants']:
return None
# for each gene, remove batch-specific variants
args = dict(
data=patients_variants,
patient_mini=patient_mini,
)
batch_specific_variants = helper.get_batch_artefacts(**args)
patients_variants = helper.remove_batch_artefacts(
patients_variants,
batch_specific_variants,
patient_mini,
)
# add cadd
args = dict(
variants=patients_variants['variants'],
chrom=chrom,
start=start,
stop=stop,
cadd_file=kwargs['cadd_file'],
)
cadds = helper.add_cadd(**args)
for k, v in cadds.items():
patients_variants['variants'][k]['cadd'] = v
# when two variants are in cis and both appear in one patient,
# discard the variant with lower cadd in that patient
# example SDK1 ('7-3990565-C-T','7-4014039-C-T')
# for now, only focus on variants with hom_f < 0.00025
remove_cis(patients_variants, genotype_df)
patients_variants['cover_df'] = cover_df
# output patients_variants
return patients_variants
def signup():
check_args(g.args, 'name', 'password')
u = user.new_user(g.args['name'], g.args['password'], g.args)
return {'message': 'signup successful', 'user': u}
