def get_approved_evidence_levels(
graphkb_conn: GraphKBConnection) -> List[Ontology]:
filters = []
for source, names in APPROVED_EVIDENCE_LEVELS.items():
filters.append({
'AND': [
{
'source': {
'target': 'Source',
'filters': {
'name': source
}
}
},
{
'name': names,
'operator': 'IN'
},
]
})
return graphkb_conn.query({
'target': 'EvidenceLevel',
'filters': {
'OR': filters
}
})
def get_therapeutic_associated_genes(graphkb_conn: GraphKBConnection) -> Set[str]:
therapeutic_relevance = get_terms_set(graphkb_conn, BASE_THERAPEUTIC_TERMS)
statements = graphkb_conn.query(
{
'target': 'Statement',
'filters': {'relevance': sorted(list(therapeutic_relevance))},
'returnProperties': [
'conditions.@rid',
'conditions.@class',
'conditions.reference1.@class',
'conditions.reference1.@rid',
'conditions.reference2.@class',
'conditions.reference2.@rid',
'reviewStatus',
],
},
)
genes = set()
for statement in statements:
if statement['reviewStatus'] == FAILED_REVIEW_STATUS:
continue
for condition in statement['conditions']:
if condition['@class'] == 'Feature':
genes.add(condition['@rid'])
elif condition['@class'].endswith('Variant'):
if condition['reference1'] and condition['reference1']['@class'] == 'Feature':
genes.add(condition['reference1']['@rid'])
if condition['reference2'] and condition['reference2']['@class'] == 'Feature':
genes.add(condition['reference2']['@rid'])
return genes
def get_statements_from_variants(
graphkb_conn: GraphKBConnection, variants: List[Record]
) -> List[Statement]:
"""
Given a list of variant records from GraphKB, return all the related statements
Args:
graphkb_conn (GraphKBConnection): the graphkb api connection object
variants (list.<dict>): list of variant records
Returns:
list.<dict>: list of Statement records from graphkb
"""
return_props = (
BASE_RETURN_PROPERTIES
+ ['sourceId', 'source.name', 'source.displayName']
+ [f'conditions.{p}' for p in GENERIC_RETURN_PROPERTIES]
+ [f'subject.{p}' for p in GENERIC_RETURN_PROPERTIES]
+ [f'evidence.{p}' for p in GENERIC_RETURN_PROPERTIES]
+ [f'relevance.{p}' for p in GENERIC_RETURN_PROPERTIES]
+ [f'evidenceLevel.{p}' for p in GENERIC_RETURN_PROPERTIES]
+ ['reviewStatus']
)
statements = graphkb_conn.query(
{
'target': 'Statement',
'filters': {'conditions': convert_to_rid_list(variants), 'operator': 'CONTAINSANY'},
'returnProperties': return_props,
},
)
return [s for s in statements if s['reviewStatus'] != FAILED_REVIEW_STATUS]
def get_alternatives(graphkb_conn: GraphKBConnection,
record_id: str) -> List[Dict]:
return graphkb_conn.query({
'target': [record_id],
'queryType': 'similarTo',
'treeEdges': []
})
def get_gene_information(
graphkb_conn: GraphKBConnection, gene_names: Iterable[str]
) -> List[IprGene]:
"""
Create the Gene Info object for upload to IPR with the other report information
Args:
graphkb_conn ([type]): [description]
gene_names ([type]): [description]
"""
logger.info('fetching variant related genes list')
variants = graphkb_conn.query(
{'target': 'Variant', 'returnProperties': ['@class', 'reference1', 'reference2']},
)
gene_flags: Dict[str, Set[str]] = {
'cancerRelated': set(),
'knownFusionPartner': set(),
'knownSmallMutation': set(),
}
for variant in variants:
gene_flags['cancerRelated'].add(variant['reference1'])
if variant['reference2']:
gene_flags['cancerRelated'].add(variant['reference2'])
gene_flags['knownFusionPartner'].add(variant['reference1'])
gene_flags['knownFusionPartner'].add(variant['reference2'])
elif variant['@class'] == 'PositionalVariant':
gene_flags['knownSmallMutation'].add(variant['reference1'])
logger.info('fetching oncogenes list')
gene_flags['oncogene'] = convert_to_rid_set(get_oncokb_oncogenes(graphkb_conn))
logger.info('fetching tumour supressors list')
gene_flags['tumourSuppressor'] = convert_to_rid_set(get_oncokb_tumour_supressors(graphkb_conn))
logger.info('fetching therapeutic associated genes lists')
gene_flags['therapeuticAssociated'] = get_therapeutic_associated_genes(graphkb_conn)
result = []
for gene_name in gene_names:
equivalent = convert_to_rid_set(get_equivalent_features(graphkb_conn, gene_name))
row = IprGene({'name': gene_name})
for flag in gene_flags:
row[flag] = bool(equivalent & gene_flags[flag])
flags = [c for c in row.keys() if c != 'name']
if any(row[c] for c in flags):
result.append(row)
# make smaller JSON to upload since all default to false already
for flag in flags:
if not row[flag]:
del row[flag]
return result
def get_preferred_drug_representation(graphkb_conn: GraphKBConnection,
drug_record_id: str) -> Dict:
"""
Given a Drug record, follow its linked records to find the preferred
representation by following alias, deprecating, and cross reference links
"""
source_preference = {
r['@rid']: r['sort']
for r in graphkb_conn.query({
'target': 'Source',
'returnProperties': ['sort', '@rid']
})
}
drugs = sorted(
get_alternatives(graphkb_conn, drug_record_id),
key=lambda rec: generate_ontology_preference_key(
rec, source_preference),
)
return drugs[0]
def get_preferred_gene_name(graphkb_conn: GraphKBConnection,
record_id: str) -> str:
"""
Given some Feature record ID return the preferred gene name
"""
record = graphkb_conn.get_record_by_id(record_id)
biotype = record.get('biotype', '')
genes = []
expanded = graphkb_conn.query({'target': [record_id], 'neighbors': 3})[0]
if biotype != 'gene':
for edge in expanded.get('out_ElementOf', []):
target = edge['in']
if target.get('biotype') == 'gene':
genes.append(target)
for edge_type in [
'out_AliasOf',
'in_AliasOf',
'in_DeprecatedBy',
'out_CrossReferenceOf',
'in_CrossReferenceOf',
]:
target_name = 'out' if edge_type.startswith('in') else 'in'
for edge in expanded.get(edge_type, []):
target = edge[target_name]
if target.get('biotype') == 'gene':
genes.append(target)
genes = sorted(
genes,
key=lambda gene: (
gene['deprecated'],
bool(gene['dependency']),
'_' in gene['name'],
gene['name'].startswith('ens'),
),
)
if genes:
return genes[0]['displayName']
# fallback to the input displayName
return record['displayName']
for match in variant_matches:
print(variant_name, 'will match', match['displayName'])
# return properties should be customized to the users needs
return_props = (BASE_RETURN_PROPERTIES +
['sourceId', 'source.name', 'source.displayName'] +
[f'conditions.{p}' for p in GENERIC_RETURN_PROPERTIES] +
[f'subject.{p}' for p in GENERIC_RETURN_PROPERTIES] +
[f'evidence.{p}' for p in GENERIC_RETURN_PROPERTIES] +
[f'relevance.{p}' for p in GENERIC_RETURN_PROPERTIES] +
[f'evidenceLevel.{p}' for p in GENERIC_RETURN_PROPERTIES])
statements = graphkb_conn.query({
'target': 'Statement',
'filters': {
'conditions': convert_to_rid_list(variant_matches),
'operator': 'CONTAINSANY'
},
'returnProperties': return_props,
})
for statement in statements[:5]:
print(
statement['relevance']['displayName'],
statement['subject']['displayName'],
statement['source']['displayName'] if statement['source'] else '',
)
BASE_THERAPEUTIC_TERMS = 'therapeutic efficacy'
therapeutic_terms = get_term_tree(graphkb_conn,
BASE_THERAPEUTIC_TERMS,
def create_section_html(
graphkb_conn: GraphKBConnection,
gene_name: str,
sentences_by_statement_id: Dict[str, str],
statements: Dict[str, Statement],
exp_variants: List[IprVariant],
) -> str:
"""
Generate HTML for a gene section of the comments
"""
output = [f'<h2>{gene_name}</h2>']
sentence_categories: Dict[str, str] = {}
for statement_id, sentence in sentences_by_statement_id.items():
relevance = statements[statement_id]['relevance']['@rid']
category = categorize_relevance(
graphkb_conn, relevance,
RELEVANCE_BASE_TERMS + [('resistance', ['no sensitivity'])])
sentence_categories[sentence] = category
# get the entrez gene description
genes = sorted(
graphkb_conn.query(
{
'target': 'Feature',
'filters': {
'AND': [
{
'source': {
'target': 'Source',
'filters': {
'name': 'entrez gene'
}
}
},
{
'name': gene_name
},
{
'biotype': 'gene'
},
]
},
}, ),
key=generate_ontology_preference_key,
)
variants_text = display_variants(gene_name, exp_variants)
if not variants_text:
# exclude sections where they are not linked to an experimental variant. this can occur when there are co-occurent statements collected
return ''
if genes and genes[0].get('description', ''):
description = '. '.join(genes[0]['description'].split('. ')[:2])
sourceId = genes[0]['sourceId']
output.append(f'''
<blockquote class="entrez_description" cite="{ENTREZ_GENE_URL}/{sourceId}">
{description}.
</blockquote>
<p>
{variants_text}
</p>
''')
sentences_used: Set[str] = set()
for section in [
{s
for (s, v) in sentence_categories.items() if v == 'diagnostic'},
{s
for (s, v) in sentence_categories.items() if v == 'biological'},
{
s
for (s, v) in sentence_categories.items()
if v in ['therapeutic', 'prognostic']
},
{
s
for (s, v) in sentence_categories.items() if v not in [
'diagnostic', 'biological', 'therapeutic', 'prognostic',
'resistance'
]
},
{s
for (s, v) in sentence_categories.items() if v == 'resistance'},
]:
content = '. '.join(sorted(list(section - sentences_used)))
sentences_used.update(section)
output.append(f'<p>{content}</p>')
return '\n'.join(output)
def annotate_variant(graphkb_conn: GraphKBConnection,
raw_variant_name: str,
include_unmatched: bool = False) -> List[Dict[str, str]]:
results = []
variant_name = convert_aa_3to1(raw_variant_name)
if 'c.*' in variant_name:
results.append({
'variant':
raw_variant_name,
'error':
f'skipping unsupported notation: {variant_name}'
})
return results
print(f'processing: {variant_name}')
try:
variant_matches = match_positional_variant(graphkb_conn, variant_name)
except FeatureNotFoundError:
if include_unmatched:
results.append({'variant': raw_variant_name})
return results
except Exception as err:
results.append({'variant': raw_variant_name, 'error': str(err)})
return results
if variant_matches:
print(f'{variant_name} matches {len(variant_matches)} variant records')
# return properties should be customized to the users needs
return_props = (BASE_RETURN_PROPERTIES +
['sourceId', 'source.name', 'source.displayName'] +
[f'conditions.{p}' for p in GENERIC_RETURN_PROPERTIES] +
[f'subject.{p}' for p in GENERIC_RETURN_PROPERTIES] +
[f'evidence.{p}' for p in GENERIC_RETURN_PROPERTIES] +
[f'relevance.{p}' for p in GENERIC_RETURN_PROPERTIES] +
[f'evidenceLevel.{p}'
for p in GENERIC_RETURN_PROPERTIES] + ['reviewStatus'])
statements = typing.cast(
Statement,
graphkb_conn.query({
'target': 'Statement',
'filters': {
'conditions': convert_to_rid_list(variant_matches),
'operator': 'CONTAINSANY',
},
'returnProperties': return_props,
}),
)
if not statements:
if include_unmatched:
results.append({
'variant_matches':
';'.join(sorted([v['displayName'] for v in variant_matches])),
'variant':
raw_variant_name,
})
return results
print(f'{variant_name} matches {len(statements)} statements')
for statement in statements:
row = {
'variant_matches':
';'.join(sorted([v['displayName'] for v in variant_matches])),
'variant':
raw_variant_name,
'statement.relevance':
statement['relevance']['displayName'],
'statement.@rid':
statement['@rid'],
'statement.subject':
statement['subject']['displayName'],
'statement.source':
statement['source']['displayName'] if statement['source'] else '',
'statement.evidence':
';'.join(sorted([e['displayName']
for e in statement['evidence']])),
'statement.conditions':
';'.join(
sorted([e['displayName'] for e in statement['conditions']])),
'statement.evidence_level':
';'.join(
sorted([
e['displayName']
for e in (statement['evidenceLevel'] or [])
])),
'statement.review_status':
statement['reviewStatus'],
'is_therapeutic':
bool(statement['relevance']['@rid'] in therapeutic_terms),
}
results.append(row)
return results