def causatives(institute_id):
institute_obj = institute_and_case(store, institute_id)
query = request.args.get('query', '')
hgnc_id = None
if '|' in query:
# filter accepts an array of IDs. Provide an array with one ID element
try:
hgnc_id = [int(query.split(' | ', 1)[0])]
except ValueError:
flash('Provided gene info could not be parsed!', 'warning')
variants = store.check_causatives(institute_obj=institute_obj,
limit_genes=hgnc_id)
if variants:
variants.sort("hgnc_symbols", pymongo.ASCENDING)
all_variants = {}
all_cases = {}
for variant_obj in variants:
if variant_obj['case_id'] not in all_cases:
case_obj = store.case(variant_obj['case_id'])
all_cases[variant_obj['case_id']] = case_obj
else:
case_obj = all_cases[variant_obj['case_id']]
if variant_obj['variant_id'] not in all_variants:
# capture ACMG classification for this variant
if isinstance(variant_obj.get('acmg_classification'), int):
acmg_code = ACMG_MAP[variant_obj['acmg_classification']]
variant_obj['acmg_classification'] = ACMG_COMPLETE_MAP[
acmg_code]
all_variants[variant_obj['variant_id']] = []
all_variants[variant_obj['variant_id']].append((case_obj, variant_obj))
return dict(institute=institute_obj, variant_groups=all_variants)
def causatives(institute_obj, request):
"""Create content to be displayed on institute causatives page
Args:
institute_obj(dict) An institute object
request(flask.request) request sent by user's browser
Returns:
data(dict)
"""
# Retrieve variants grouped by case
query = request.args.get("query", "")
hgnc_id = None
if "|" in query:
# filter accepts an array of IDs. Provide an array with one ID element
try:
hgnc_id = [int(query.split(" | ", 1)[0])]
except ValueError:
flash("Provided gene info could not be parsed!", "warning")
variants = list(
store.check_causatives(institute_obj=institute_obj,
limit_genes=hgnc_id))
if variants:
variants = sorted(
variants,
key=lambda k: k.get("hgnc_symbols", [None])[0] or k.get("str_repid"
) or "",
)
all_variants = {}
all_cases = {}
for variant_obj in variants:
if variant_obj["category"] in ["snv", "cancer"]:
update_representative_gene(variant_obj, variant_obj.get(
"genes", [])) # required to display cDNA and protein change
if variant_obj["case_id"] not in all_cases:
case_obj = store.case(variant_obj["case_id"])
all_cases[variant_obj["case_id"]] = case_obj
else:
case_obj = all_cases[variant_obj["case_id"]]
if variant_obj["variant_id"] not in all_variants:
all_variants[variant_obj["variant_id"]] = []
all_variants[variant_obj["variant_id"]].append((case_obj, variant_obj))
data = dict(
institute=institute_obj,
variant_groups=all_variants,
acmg_map={
key: ACMG_COMPLETE_MAP[value]
for key, value in ACMG_MAP.items()
},
)
return data
def causatives(institute_id):
institute_obj = institute_and_case(store, institute_id)
variants = store.check_causatives(institute_obj=institute_obj)
all_variants = {}
all_cases = {}
for variant_obj in variants:
if variant_obj['case_id'] not in all_cases:
case_obj = store.case(variant_obj['case_id'])
all_cases[variant_obj['case_id']] = case_obj
else:
case_obj = all_cases[variant_obj['case_id']]
if variant_obj['variant_id'] not in all_variants:
all_variants[variant_obj['variant_id']] = []
all_variants[variant_obj['variant_id']].append((case_obj, variant_obj))
return dict(institute=institute_obj, variant_groups=all_variants)
def causatives(institute_id):
institute_obj = institute_and_case(store, institute_id)
variants = store.check_causatives(institute_obj=institute_obj)
all_variants = {}
all_cases = {}
for variant_obj in variants:
if variant_obj['case_id'] not in all_cases:
case_obj = store.case(variant_obj['case_id'])
all_cases[variant_obj['case_id']] = case_obj
else:
case_obj = all_cases[variant_obj['case_id']]
if variant_obj['variant_id'] not in all_variants:
all_variants[variant_obj['variant_id']] = []
all_variants[variant_obj['variant_id']].append((case_obj, variant_obj))
return dict(institute=institute_obj, variant_groups=all_variants)
def causatives(institute_id):
institute_obj = institute_and_case(store, institute_id)
query = request.args.get("query", "")
hgnc_id = None
if "|" in query:
# filter accepts an array of IDs. Provide an array with one ID element
try:
hgnc_id = [int(query.split(" | ", 1)[0])]
except ValueError:
flash("Provided gene info could not be parsed!", "warning")
variants = store.check_causatives(institute_obj=institute_obj,
limit_genes=hgnc_id)
if variants:
variants.sort("hgnc_symbols", pymongo.ASCENDING)
all_variants = {}
all_cases = {}
for variant_obj in variants:
if variant_obj["case_id"] not in all_cases:
case_obj = store.case(variant_obj["case_id"])
all_cases[variant_obj["case_id"]] = case_obj
else:
case_obj = all_cases[variant_obj["case_id"]]
if variant_obj["variant_id"] not in all_variants:
all_variants[variant_obj["variant_id"]] = []
all_variants[variant_obj["variant_id"]].append((case_obj, variant_obj))
acmg_map = {
key: ACMG_COMPLETE_MAP[value]
for key, value in ACMG_MAP.items()
}
return dict(institute=institute_obj,
variant_groups=all_variants,
acmg_map=acmg_map)
for term in store.case_omim_diagnoses(case_obj.get("diagnosis_phenotypes"))
}
if case_obj.get("custom_images"):
# re-encode images as base64
if "case" in case_obj.get("custom_images"):
case_obj["custom_images"] = case_obj["custom_images"]["case"]
for img_section in case_obj["custom_images"].keys():
for img in case_obj["custom_images"][img_section]:
img["data"] = b64encode(img["data"]).decode("utf-8")
data = {
"institute": institute_obj,
"case": case_obj,
"status_class": STATUS_MAP.get(case_obj["status"]),
"other_causatives": [var for var in store.check_causatives(case_obj=case_obj)],
"managed_variants": [var for var in store.check_managed(case_obj=case_obj)],
"comments": store.events(institute_obj, case=case_obj, comments=True),
"hpo_groups": pheno_groups,
"case_groups": case_groups,
"case_group_label": case_group_label,
"events": events,
"suspects": suspects,
"causatives": causatives,
"evaluated_variants": evaluated_variants,
"partial_causatives": partial_causatives,
"collaborators": collab_ids,
"cohort_tags": institute_obj.get("cohorts", []),
"omim_terms": omim_terms,
"manual_rank_options": MANUAL_RANK_OPTIONS,
"cancer_tier_options": CANCER_TIER_OPTIONS,