def case(store, institute_obj, case_obj):
"""Preprocess a single case."""
case_obj['individual_ids'] = []
for individual in case_obj['individuals']:
try:
sex = int(individual.get('sex', 0))
except ValueError as err:
sex = 0
individual['sex_human'] = SEX_MAP[sex]
individual['phenotype_human'] = PHENOTYPE_MAP.get(
individual['phenotype'])
case_obj['individual_ids'].append(individual['individual_id'])
case_obj['assignees'] = [
store.user(user_email) for user_email in case_obj.get('assignees', [])
]
suspects = [
store.variant(variant_id) or variant_id
for variant_id in case_obj.get('suspects', [])
]
causatives = [
store.variant(variant_id) or variant_id
for variant_id in case_obj.get('causatives', [])
]
distinct_genes = set()
case_obj['panel_names'] = []
for panel_info in case_obj.get('panels', []):
if panel_info.get('is_default'):
panel_obj = store.gene_panel(panel_info['panel_name'],
version=panel_info.get('version'))
distinct_genes.update(
[gene['hgnc_id'] for gene in panel_obj.get('genes', [])])
full_name = "{} ({})".format(panel_obj['display_name'],
panel_obj['version'])
case_obj['panel_names'].append(full_name)
case_obj['default_genes'] = list(distinct_genes)
for hpo_term in itertools.chain(case_obj.get('phenotype_groups', []),
case_obj.get('phenotype_terms', [])):
hpo_term['hpo_link'] = (
"http://compbio.charite.de/hpoweb/showterm?id={}".format(
hpo_term['phenotype_id']))
# other collaborators than the owner of the case
o_collaborators = [
store.institute(collab_id) for collab_id in case_obj['collaborators']
if collab_id != case_obj['owner']
]
case_obj['o_collaborators'] = [(collab_obj['_id'],
collab_obj['display_name'])
for collab_obj in o_collaborators]
irrelevant_ids = ('cust000', institute_obj['_id'])
collab_ids = [(collab['_id'], collab['display_name'])
for collab in store.institutes()
if (collab['_id'] not in irrelevant_ids) and (
collab['_id'] not in case_obj['collaborators'])]
events = list(store.events(institute_obj, case=case_obj))
def case_report_content(store, institute_obj, case_obj):
"""Gather contents to be visualized in a case report
Args:
store(adapter.MongoAdapter)
institute_obj(models.Institute)
case_obj(models.Case)
Returns:
data(dict)
"""
variant_types = {
"causatives_detailed": "causatives",
"partial_causatives_detailed": "partial_causatives",
"suspects_detailed": "suspects",
"classified_detailed": "acmg_classification",
"tagged_detailed": "manual_rank",
"tier_detailed": "cancer_tier",
"dismissed_detailed": "dismiss_variant",
"commented_detailed": "is_commented",
}
data = case_obj
for individual in data["individuals"]:
try:
sex = int(individual.get("sex", 0))
except ValueError as err:
sex = 0
individual["sex_human"] = SEX_MAP[sex]
individual["phenotype_human"] = PHENOTYPE_MAP.get(individual["phenotype"])
dismiss_options = DISMISS_VARIANT_OPTIONS
if case_obj.get("track") == "cancer":
dismiss_options = {
**DISMISS_VARIANT_OPTIONS,
**CANCER_SPECIFIC_VARIANT_DISMISS_OPTIONS,
}
# Add the case comments
data["comments"] = store.events(institute_obj, case=case_obj, comments=True)
data["manual_rank_options"] = MANUAL_RANK_OPTIONS
data["cancer_tier_options"] = CANCER_TIER_OPTIONS
data["dismissed_options"] = dismiss_options
data["genetic_models"] = dict(GENETIC_MODELS)
data["report_created_at"] = datetime.datetime.now().strftime("%Y-%m-%d %H:%M")
evaluated_variants = {vt: [] for vt in variant_types}
# We collect all causatives (including the partial ones) and suspected variants
# These are handeled in separate since they are on case level
for var_type in ["causatives", "suspects", "partial_causatives"]:
# These include references to variants
vt = "_".join([var_type, "detailed"])
for var_id in case_obj.get(var_type, []):
variant_obj = store.variant(var_id)
if not variant_obj:
continue
if var_type == "partial_causatives": # Collect associated phenotypes
variant_obj["phenotypes"] = [
value for key, value in case_obj["partial_causatives"].items() if key == var_id
][0]
evaluated_variants[vt].append(variant_obj)
## get variants for this case that are either classified, commented, tagged or dismissed.
for var_obj in store.evaluated_variants(case_id=case_obj["_id"]):
# Check which category it belongs to
for vt in variant_types:
keyword = variant_types[vt]
# When found we add it to the categpry
# Eac variant can belong to multiple categories
if keyword not in var_obj:
continue
evaluated_variants[vt].append(var_obj)
for var_type in evaluated_variants:
decorated_variants = []
for var_obj in evaluated_variants[var_type]:
# We decorate the variant with some extra information
decorated_info = variant_decorator(
store=store,
institute_id=institute_obj["_id"],
case_name=case_obj["display_name"],
variant_id=None,
variant_obj=var_obj,
add_case=False,
add_other=False,
get_overlapping=False,
add_compounds=False,
variant_type=var_obj["category"],
institute_obj=institute_obj,
case_obj=case_obj,
)
decorated_variants.append(decorated_info["variant"])
# Add the decorated variants to the case
data[var_type] = decorated_variants
return data
def case(store, institute_obj, case_obj):
"""Preprocess a single case.
Prepare the case to be displayed in the case view.
Args:
store(adapter.MongoAdapter)
institute_obj(models.Institute)
case_obj(models.Case)
Returns:
data(dict): includes the cases, how many there are and the limit.
"""
# Convert individual information to more readable format
case_obj['individual_ids'] = []
for individual in case_obj['individuals']:
try:
sex = int(individual.get('sex', 0))
except ValueError as err:
sex = 0
individual['sex_human'] = SEX_MAP[sex]
individual['phenotype_human'] = PHENOTYPE_MAP.get(
individual['phenotype'])
case_obj['individual_ids'].append(individual['individual_id'])
case_obj['assignees'] = [
store.user(user_email) for user_email in case_obj.get('assignees', [])
]
# Fetch the variant objects for suspects and causatives
suspects = [
store.variant(variant_id) or variant_id
for variant_id in case_obj.get('suspects', [])
]
causatives = [
store.variant(variant_id) or variant_id
for variant_id in case_obj.get('causatives', [])
]
# Set of all unique genes in the default gene panels
distinct_genes = set()
case_obj['panel_names'] = []
for panel_info in case_obj.get('panels', []):
if not panel_info.get('is_default'):
continue
panel_obj = store.gene_panel(panel_info['panel_name'],
version=panel_info.get('version'))
distinct_genes.update(
[gene['hgnc_id'] for gene in panel_obj.get('genes', [])])
full_name = "{} ({})".format(panel_obj['display_name'],
panel_obj['version'])
case_obj['panel_names'].append(full_name)
case_obj['default_genes'] = list(distinct_genes)
for hpo_term in itertools.chain(case_obj.get('phenotype_groups', []),
case_obj.get('phenotype_terms', [])):
hpo_term['hpo_link'] = (
"http://compbio.charite.de/hpoweb/showterm?id={}".format(
hpo_term['phenotype_id']))
# other collaborators than the owner of the case
o_collaborators = [
store.institute(collab_id) for collab_id in case_obj['collaborators']
if collab_id != case_obj['owner']
]
case_obj['o_collaborators'] = [(collab_obj['_id'],
collab_obj['display_name'])
for collab_obj in o_collaborators]
irrelevant_ids = ('cust000', institute_obj['_id'])
collab_ids = [(collab['_id'], collab['display_name'])
for collab in store.institutes()
if (collab['_id'] not in irrelevant_ids) and (
collab['_id'] not in case_obj['collaborators'])]
events = list(store.events(institute_obj, case=case_obj))
def case_report_content(store, institute_obj, case_obj):
"""Gather contents to be visualized in a case report
Args:
store(adapter.MongoAdapter)
institute_obj(models.Institute)
case_obj(models.Case)
Returns:
data(dict)
"""
variant_types = {
'causatives_detailed': 'causatives',
'suspects_detailed': 'suspects',
'classified_detailed': 'acmg_classification',
'tagged_detailed': 'manual_rank',
'dismissed_detailed': 'dismiss_variant',
'commented_detailed': 'is_commented',
}
data = case_obj
for individual in data['individuals']:
try:
sex = int(individual.get('sex', 0))
except ValueError as err:
sex = 0
individual['sex_human'] = SEX_MAP[sex]
individual['phenotype_human'] = PHENOTYPE_MAP.get(
individual['phenotype'])
# Add the case comments
data['comments'] = store.events(institute_obj,
case=case_obj,
comments=True)
data['manual_rank_options'] = MANUAL_RANK_OPTIONS
data['dismissed_options'] = DISMISS_VARIANT_OPTIONS
data['genetic_models'] = dict(GENETIC_MODELS)
data['report_created_at'] = datetime.datetime.now().strftime(
"%Y-%m-%d %H:%M")
evaluated_variants = {}
for vt in variant_types:
evaluated_variants[vt] = []
# We collect all causatives and suspected variants
# These are handeled in separate since they are on case level
for var_type in ['causatives', 'suspects']:
#These include references to variants
vt = '_'.join([var_type, 'detailed'])
for var_id in case_obj.get(var_type, []):
variant_obj = store.variant(var_id)
if not variant_obj:
continue
# If the variant exists we add it to the evaluated variants
evaluated_variants[vt].append(variant_obj)
## get variants for this case that are either classified, commented, tagged or dismissed.
for var_obj in store.evaluated_variants(case_id=case_obj['_id']):
# Check which category it belongs to
for vt in variant_types:
keyword = variant_types[vt]
# When found we add it to the categpry
# Eac variant can belong to multiple categories
if keyword in var_obj:
evaluated_variants[vt].append(var_obj)
for var_type in evaluated_variants:
decorated_variants = []
for var_obj in evaluated_variants[var_type]:
# We decorate the variant with some extra information
if var_obj['category'] == 'snv':
decorated_info = variant_decorator(store=store,
institute_obj=institute_obj,
case_obj=case_obj,
variant_id=None,
variant_obj=var_obj,
add_case=False,
add_other=False,
get_overlapping=False)
else:
decorated_info = sv_variant(store=store,
institute_id=institute_obj['_id'],
case_name=case_obj['display_name'],
variant_obj=var_obj,
add_case=False,
get_overlapping=False)
decorated_variants.append(decorated_info['variant'])
# Add the decorated variants to the case
data[var_type] = decorated_variants
return data
def case_report_content(store, institute_obj, case_obj):
"""Gather contents to be visualized in a case report
Args:
store(adapter.MongoAdapter)
institute_obj(models.Institute)
case_obj(models.Case)
Returns:
data(dict)
"""
variant_types = {
'causatives_detailed': 'causatives',
'suspects_detailed': 'suspects',
'classified_detailed': 'acmg_classification',
'tagged_detailed': 'manual_rank',
'dismissed_detailed': 'dismiss_variant',
'commented_detailed': 'is_commented',
}
data = case_obj
for individual in data['individuals']:
try:
sex = int(individual.get('sex', 0))
except ValueError as err:
sex = 0
individual['sex_human'] = SEX_MAP[sex]
individual['phenotype_human'] = PHENOTYPE_MAP.get(individual['phenotype'])
# Add the case comments
data['comments'] = store.events(institute_obj, case=case_obj, comments=True)
data['manual_rank_options'] = MANUAL_RANK_OPTIONS
data['dismissed_options'] = DISMISS_VARIANT_OPTIONS
data['genetic_models'] = dict(GENETIC_MODELS)
data['report_created_at'] = datetime.datetime.now().strftime("%Y-%m-%d %H:%M")
evaluated_variants = {}
for vt in variant_types:
evaluated_variants[vt] = []
# We collect all causatives and suspected variants
# These are handeled in separate since they are on case level
for var_type in ['causatives', 'suspects']:
#These include references to variants
vt = '_'.join([var_type, 'detailed'])
for var_id in case_obj.get(var_type,[]):
variant_obj = store.variant(var_id)
if not variant_obj:
continue
# If the variant exists we add it to the evaluated variants
evaluated_variants[vt].append(variant_obj)
## get variants for this case that are either classified, commented, tagged or dismissed.
for var_obj in store.evaluated_variants(case_id=case_obj['_id']):
# Check which category it belongs to
for vt in variant_types:
keyword = variant_types[vt]
# When found we add it to the categpry
# Eac variant can belong to multiple categories
if keyword in var_obj:
evaluated_variants[vt].append(var_obj)
for var_type in evaluated_variants:
decorated_variants = []
for var_obj in evaluated_variants[var_type]:
# We decorate the variant with some extra information
if var_obj['category'] == 'snv':
decorated_info = variant_decorator(
store=store,
institute_obj=institute_obj,
case_obj=case_obj,
variant_id=None,
variant_obj=var_obj,
add_case=False,
add_other=False,
get_overlapping=False
)
else:
decorated_info = sv_variant(
store=store,
institute_id=institute_obj['_id'],
case_name=case_obj['display_name'],
variant_obj=var_obj,
add_case=False,
get_overlapping=False
)
decorated_variants.append(decorated_info['variant'])
# Add the decorated variants to the case
data[var_type] = decorated_variants
return data