def get_genes(variant):
"""Get the gene information in the mongoengine format.
Args:
variant (dict): A Variant dictionary
Returns:
mongo_genes (list): A list with mongo engine object that
represents the genes
"""
genes = {}
mongo_genes = []
transcripts = get_transcripts(variant)
# A dictionary with clinical gene descriptions
gene_descriptions = get_gene_descriptions(variant)
# First we get all vep entrys that we find and put them under their
# corresponding gene symbol in 'genes'
for transcript in transcripts:
hgnc_symbol = transcript.hgnc_symbol
transcript_id = transcript.transcript_id
functional_annotations = transcript.functional_annotations
ensembl_id = transcript.ensembl_id
if hgnc_symbol:
if hgnc_symbol in genes:
genes[hgnc_symbol]['transcripts'][transcript_id] = transcript
# Check most severe transcript
for functional_annotation in functional_annotations:
new_rank = SO_TERMS[functional_annotation]['rank']
if new_rank < genes[hgnc_symbol]['best_rank']:
genes[hgnc_symbol]['best_rank'] = new_rank
genes[hgnc_symbol]['most_severe_transcript'] = transcript
genes[hgnc_symbol]['most_severe_function'] = functional_annotation
else:
genes[hgnc_symbol] = {
'transcripts':{
transcript_id: transcript,
},
'most_severe_transcript': transcript,
'omim_gene_id': None,
'phenotypic_terms': [],
'best_rank': 100,
'ensembl_id': ensembl_id,
}
for functional_annotation in functional_annotations:
new_rank = SO_TERMS[functional_annotation]['rank']
if new_rank < genes[hgnc_symbol]['best_rank']:
genes[hgnc_symbol]['best_rank'] = new_rank
genes[hgnc_symbol]['most_severe_function'] = functional_annotation
######################################################################
## There are two types of OMIM terms, one is the OMIM gene entry ##
## and one is for the phenotypic terms. ##
## Each key in the 'omim_terms' dictionary reprecents a gene id. ##
## Values are a dictionary with 'omim_gene_id' = omim_gene_id and ##
## 'phenotypic_terms' = [list of OmimPhenotypeObjects] ##
######################################################################
# Fill the mim ids for the genes:
mim_ids = get_omim_gene_ids(variant)
for hgnc_symbol in mim_ids:
if hgnc_symbol in genes:
mim_id = mim_ids[hgnc_symbol]
logger.debug("Adding mim id {0} to gene {1}".format(
hgnc_symbol, mim_id))
genes[hgnc_symbol]['omim_gene_id'] = mim_id
# Fill the omim phenotype terms:
phenotype_mim_ids = get_omim_phenotype_ids(variant)
for hgnc_symbol in phenotype_mim_ids:
phenotype_terms = phenotype_mim_ids[hgnc_symbol]
if hgnc_symbol in genes:
for term in phenotype_terms:
genes[hgnc_symbol]['phenotypic_terms'].append(term)
reduced_penetrance = set(variant['info_dict'].get('Reduced_penetrance', []))
for hgnc_symbol in genes:
gene_info = genes[hgnc_symbol]
most_severe = gene_info['most_severe_transcript']
# Create a mongo engine gene object for each gene found in the variant
mongo_gene = Gene(hgnc_symbol=hgnc_symbol)
if hgnc_symbol in reduced_penetrance:
mongo_gene.reduced_penetrance = True
mongo_gene.description = gene_descriptions.get(hgnc_symbol)
mongo_gene.ensembl_gene_id = gene_info['ensembl_id']
mongo_gene.omim_gene_entry = gene_info['omim_gene_id']
mongo_gene.omim_phenotypes = gene_info['phenotypic_terms']
# Add a list with the transcripts:
mongo_gene.transcripts = []
for transcript_id in gene_info['transcripts']:
mongo_gene.transcripts.append(gene_info['transcripts'][transcript_id])
try:
mongo_gene.functional_annotation = gene_info['most_severe_function']
except AttributeError:
pass
try:
mongo_gene.region_annotation = SO_TERMS[mongo_gene.functional_annotation]['region']
except AttributeError:
pass
try:
mongo_gene.sift_prediction = most_severe.sift_prediction
except AttributeError:
pass
try:
mongo_gene.polyphen_prediction = most_severe.polyphen_prediction
except AttributeError:
pass
# Add the mongo engine gene to the dictionary
mongo_genes.append(mongo_gene)
return mongo_genes
def get_genes(variant):
"""
Get the transcript information in the mongoengine format.
Args:
variant : A Variant dictionary
Returns:
mongo_genes: A list with mongo engine object that represents the genes
"""
genes = {}
transcripts = []
mongo_genes = []
# Conversion from ensembl to refseq
# ensembl_to_refseq is a dictionary with ensembl transcript id as keys and
# a list of refseq ids as values
ensembl_to_refseq = {}
for gene_info in variant['info_dict'].get(
'Ensembl_transcript_to_refseq_transcript', []):
splitted_gene = gene_info.split(':')
transcript_info = splitted_gene[1]
for transcript in transcript_info.split('|'):
splitted_transcript = transcript.split('>')
if len(splitted_transcript) > 1:
ensembl_id = splitted_transcript[0]
refseq_ids = splitted_transcript[1].split('/')
ensembl_to_refseq[ensembl_id] = refseq_ids
# A dictionary with clinical gene descriptions
gene_descriptions = {}
for gene_info in variant['info_dict'].get('Gene_description', []):
splitted_gene = gene_info.split(':')
hgnc_symbol = splitted_gene[0]
description = splitted_gene[1]
gene_descriptions[hgnc_symbol] = description
# First we get all vep entrys that we find and put them under their
# corresponding gene symbol in 'genes'
for vep_entry in variant['vep_info'].get(variant['ALT'], []):
transcript = get_transcript(vep_entry, ensembl_to_refseq)
hgnc_symbol = transcript.hgnc_symbol
if hgnc_symbol:
if hgnc_symbol in genes:
genes[hgnc_symbol]['transcripts'][transcript.transcript_id] = transcript
for functional_annotation in transcript.functional_annotations:
new_rank = SO_TERMS[functional_annotation]['rank']
if new_rank < genes[hgnc_symbol]['best_rank']:
genes[hgnc_symbol]['best_rank'] = new_rank
genes[hgnc_symbol]['most_severe_transcript'] = transcript
genes[hgnc_symbol]['most_severe_function'] = functional_annotation
else:
genes[hgnc_symbol] = {}
genes[hgnc_symbol]['transcripts'] = {}
genes[hgnc_symbol]['transcripts'][transcript.transcript_id] = transcript
genes[hgnc_symbol]['most_severe_transcript'] = transcript
genes[hgnc_symbol]['omim_gene_id'] = None
genes[hgnc_symbol]['phenotypic_terms'] = []
genes[hgnc_symbol]['best_rank'] = 40
genes[hgnc_symbol]['ensembl_id'] = transcript.ensembl_id
for functional_annotation in transcript.functional_annotations:
new_rank = SO_TERMS[functional_annotation]['rank']
if new_rank < genes[hgnc_symbol]['best_rank']:
genes[hgnc_symbol]['best_rank'] = new_rank
genes[hgnc_symbol]['most_severe_function'] = functional_annotation
######################################################################
## There are two types of OMIM terms, one is the OMIM gene entry ##
## and one is for the phenotypic terms. ##
## Each key in the 'omim_terms' dictionary reprecents a gene id. ##
## Values are a dictionary with 'omim_gene_id' = omim_gene_id and ##
## 'phenotypic_terms' = [list of OmimPhenotypeObjects] ##
######################################################################
# Fill the omim gene id:s:
for annotation in variant['info_dict'].get('OMIM_morbid', []):
if annotation:
splitted_record = annotation.split(':')
try:
hgnc_symbol = splitted_record[0]
omim_term = splitted_record[1]
genes[hgnc_symbol]['omim_gene_id'] = omim_term
except (ValueError, KeyError):
pass
# Fill the omim phenotype terms:
for gene_annotation in variant['info_dict'].get('Phenotypic_disease_model', []):
if gene_annotation:
splitted_gene = gene_annotation.split(':')
hgnc_symbol = splitted_gene[0]
for omim_entry in splitted_gene[1].split('|'):
splitted_record = omim_entry.split('>')
phenotype_id = splitted_record[0]
inheritance_patterns = []
if len(splitted_record) > 1:
inheritance_patterns = splitted_record[1].split('/')
disease_model = PhenotypeTerm(
phenotype_id=phenotype_id,
disease_models=inheritance_patterns
)
genes[hgnc_symbol]['phenotypic_terms'].append(disease_model)
for hgnc_symbol in genes:
gene_info = genes[hgnc_symbol]
most_severe = gene_info['most_severe_transcript']
# Create a mongo engine gene object for each gene found in the variant
mongo_gene = Gene(hgnc_symbol=hgnc_symbol)
mongo_gene.description = gene_descriptions.get(hgnc_symbol)
mongo_gene.ensembl_gene_id = gene_info.get('ensembl_id', None)
mongo_gene.omim_gene_entry = gene_info.get(
'omim_gene_id',
None
)
mongo_gene.omim_phenotypes = gene_info.get(
'phenotypic_terms',
[]
)
# Add a list with the transcripts:
mongo_gene.transcripts = []
for transcript_id in gene_info['transcripts']:
mongo_gene.transcripts.append(gene_info['transcripts'][transcript_id])
try:
mongo_gene.functional_annotation = gene_info['most_severe_function']
except AttributeError:
pass
try:
mongo_gene.region_annotation = SO_TERMS[mongo_gene.functional_annotation]['region']
except AttributeError:
pass
try:
mongo_gene.sift_prediction = most_severe.sift_prediction
except AttributeError:
pass
try:
mongo_gene.polyphen_prediction = most_severe.polyphen_prediction
except AttributeError:
pass
# Add the mongo engine gene to the dictionary
mongo_genes.append(mongo_gene)
return mongo_genes
def get_genes(variant):
"""
Get the transcript information in the mongoengine format.
Args:
variant : A Variant dictionary
Returns:
mongo_genes: A list with mongo engine object that represents the genes
"""
genes = {}
transcripts = []
mongo_genes = []
# Conversion from ensembl to refseq
# ensembl_to_refseq is a dictionary with ensembl transcript id as keys and
# a list of refseq ids as values
ensembl_to_refseq = {}
for gene_info in variant['info_dict'].get(
'Ensembl_transcript_to_refseq_transcript', []):
splitted_gene = gene_info.split(':')
transcript_info = splitted_gene[1]
for transcript in transcript_info.split('|'):
splitted_transcript = transcript.split('>')
if len(splitted_transcript) > 1:
ensembl_id = splitted_transcript[0]
refseq_ids = splitted_transcript[1].split('/')
ensembl_to_refseq[ensembl_id] = refseq_ids
# A dictionary with clinical gene descriptions
gene_descriptions = {}
for gene_info in variant['info_dict'].get('Gene_description', []):
splitted_gene = gene_info.split(':')
hgnc_symbol = splitted_gene[0]
description = splitted_gene[1]
gene_descriptions[hgnc_symbol] = description
# First we get all vep entrys that we find and put them under their
# corresponding gene symbol in 'genes'
for vep_entry in variant['vep_info'].get(variant['ALT'], []):
transcript = get_transcript(vep_entry, ensembl_to_refseq)
hgnc_symbol = transcript.hgnc_symbol
if hgnc_symbol:
if hgnc_symbol in genes:
genes[hgnc_symbol]['transcripts'][transcript.transcript_id] = transcript
for functional_annotation in transcript.functional_annotations:
new_rank = SO_TERMS[functional_annotation]['rank']
if new_rank < genes[hgnc_symbol]['best_rank']:
genes[hgnc_symbol]['best_rank'] = new_rank
genes[hgnc_symbol]['most_severe_transcript'] = transcript
genes[hgnc_symbol]['most_severe_function'] = functional_annotation
else:
genes[hgnc_symbol] = {}
genes[hgnc_symbol]['transcripts'] = {}
genes[hgnc_symbol]['transcripts'][transcript.transcript_id] = transcript
genes[hgnc_symbol]['most_severe_transcript'] = transcript
genes[hgnc_symbol]['omim_gene_id'] = None
genes[hgnc_symbol]['phenotypic_terms'] = []
genes[hgnc_symbol]['best_rank'] = 40
genes[hgnc_symbol]['ensembl_id'] = transcript.ensembl_id
for functional_annotation in transcript.functional_annotations:
new_rank = SO_TERMS[functional_annotation]['rank']
if new_rank < genes[hgnc_symbol]['best_rank']:
genes[hgnc_symbol]['best_rank'] = new_rank
genes[hgnc_symbol]['most_severe_function'] = functional_annotation
######################################################################
## There are two types of OMIM terms, one is the OMIM gene entry ##
## and one is for the phenotypic terms. ##
## Each key in the 'omim_terms' dictionary reprecents a gene id. ##
## Values are a dictionary with 'omim_gene_id' = omim_gene_id and ##
## 'phenotypic_terms' = [list of OmimPhenotypeObjects] ##
######################################################################
# Fill the omim gene id:s:
for annotation in variant['info_dict'].get('OMIM_morbid', []):
if annotation:
splitted_record = annotation.split(':')
try:
hgnc_symbol = splitted_record[0]
omim_term = splitted_record[1]
genes[hgnc_symbol]['omim_gene_id'] = omim_term
except (ValueError, KeyError):
pass
# Fill the omim phenotype terms:
for gene_annotation in variant['info_dict'].get('Phenotypic_disease_model', []):
if gene_annotation:
splitted_gene = gene_annotation.split(':')
hgnc_symbol = splitted_gene[0]
for omim_entry in splitted_gene[1].split('|'):
splitted_record = omim_entry.split('>')
phenotype_id = splitted_record[0]
inheritance_patterns = []
if len(splitted_record) > 1:
inheritance_patterns = splitted_record[1].split('/')
disease_model = PhenotypeTerm(
phenotype_id=phenotype_id,
disease_models=inheritance_patterns
)
genes[hgnc_symbol]['phenotypic_terms'].append(disease_model)
for hgnc_symbol in genes:
gene_info = genes[hgnc_symbol]
most_severe = gene_info['most_severe_transcript']
# Create a mongo engine gene object for each gene found in the variant
mongo_gene = Gene(hgnc_symbol=hgnc_symbol)
mongo_gene.description = gene_descriptions.get(hgnc_symbol)
mongo_gene.ensembl_gene_id = gene_info.get('ensembl_id', None)
mongo_gene.omim_gene_entry = gene_info.get(
'omim_gene_id',
None
)
mongo_gene.omim_phenotypes = gene_info.get(
'phenotypic_terms',
[]
)
# Add a list with the transcripts:
mongo_gene.transcripts = []
for transcript_id in gene_info['transcripts']:
mongo_gene.transcripts.append(gene_info['transcripts'][transcript_id])
try:
mongo_gene.functional_annotation = gene_info['most_severe_function']
except AttributeError:
pass
try:
mongo_gene.region_annotation = SO_TERMS[mongo_gene.functional_annotation]['region']
except AttributeError:
pass
try:
mongo_gene.sift_prediction = most_severe.sift_prediction
except AttributeError:
pass
try:
mongo_gene.polyphen_prediction = most_severe.polyphen_prediction
except AttributeError:
pass
# Add the mongo engine gene to the dictionary
mongo_genes.append(mongo_gene)
return mongo_genes
