def test_parse_transcripts_file(transcripts_handle):
"""Test to parse all ensembl transcripts"""
transcripts = parse_transcripts(transcripts_handle)
for transcript_id in transcripts:
transcript = transcripts[transcript_id]
assert transcript['ensembl_transcript_id']
assert transcript['ensembl_gene_id']
def test_parse_transcripts_file(transcripts_handle):
"""Test to parse all ensembl transcripts"""
transcripts = parse_transcripts(transcripts_handle)
for transcript_id in transcripts:
transcript = transcripts[transcript_id]
assert transcript['ensembl_transcript_id']
assert transcript['ensembl_gene_id']
def test_parse_transcripts_data_frame(transcripts_df):
"""Test to parse all ensembl transcripts from data frame"""
## GIVEN a data frame with transcript information
transcripts = parse_transcripts(transcripts_df)
## WHEN parsing the transcripts
i = 0
for i, transcript_id in enumerate(transcripts):
transcript = transcripts[transcript_id]
## THEN assert they all got the mandatory ids
assert transcript['ensembl_transcript_id']
assert transcript['ensembl_gene_id']
assert i > 0
def test_parse_transcripts_data_frame(transcripts_df):
"""Test to parse all ensembl transcripts from data frame"""
## GIVEN a data frame with transcript information
transcripts = parse_transcripts(transcripts_df)
## WHEN parsing the transcripts
i = 0
for i,transcript_id in enumerate(transcripts):
transcript = transcripts[transcript_id]
## THEN assert they all got the mandatory ids
assert transcript['ensembl_transcript_id']
assert transcript['ensembl_gene_id']
assert i > 0
def parsed_transcripts(request, transcripts_handle, ensembl_genes):
"""Get the parsed ensembl transcripts"""
print('')
transcripts = parse_transcripts(transcripts_handle)
for tx_id in transcripts:
tx_info = transcripts[tx_id]
ens_gene_id = tx_info['ensembl_gene_id']
gene_obj = ensembl_genes.get(ens_gene_id)
if not gene_obj:
continue
tx_info['hgnc_id'] = gene_obj['hgnc_id']
tx_info['primary_transcripts'] = set(
gene_obj.get('primary_transcripts', []))
return transcripts
def load_transcripts(adapter,
transcripts_lines=None,
build='37',
ensembl_genes=None):
"""Load all the transcripts
Transcript information is from ensembl.
Args:
adapter(MongoAdapter)
transcripts_lines(iterable): iterable with ensembl transcript lines
build(str)
ensembl_genes(dict): Map from ensembl_id -> HgncGene
Returns:
transcript_objs(list): A list with all transcript objects
"""
# Fetch all genes with ensemblid as keys
ensembl_genes = ensembl_genes or adapter.ensembl_genes(build)
if transcripts_lines is None:
transcripts_lines = fetch_ensembl_transcripts(build=build)
# Map with all transcripts enstid -> parsed transcript
transcripts_dict = parse_transcripts(transcripts_lines)
for ens_tx_id in list(transcripts_dict):
parsed_tx = transcripts_dict[ens_tx_id]
# Get the ens gene id
ens_gene_id = parsed_tx['ensembl_gene_id']
# pp(ens_gene_id)
# Fetch the internal gene object to find out the correct hgnc id
gene_obj = ensembl_genes.get(ens_gene_id)
# If the gene is non existing in scout we skip the transcript
if not gene_obj:
transcripts_dict.pop(ens_tx_id)
LOG.debug("Gene %s does not exist in build %s", ens_gene_id, build)
continue
# Add the correct hgnc id
parsed_tx['hgnc_id'] = gene_obj['hgnc_id']
# Primary transcript information is collected from HGNC
parsed_tx['primary_transcripts'] = set(
gene_obj.get('primary_transcripts', []))
ref_seq_transcripts = 0
nr_primary_transcripts = 0
nr_transcripts = len(transcripts_dict)
transcript_objs = []
with progressbar(transcripts_dict.values(),
label="Building transcripts",
length=nr_transcripts) as bar:
for tx_data in bar:
#################### Get the correct refseq identifier ####################
# We need to decide one refseq identifier for each transcript, if there are any to choose
# from. The algorithm is as follows:
# If these is ONE mrna this is choosen
# If there are several mrna the one that is in 'primary_transcripts' is choosen
# Else one is choosen at random
# The same follows for the other categories where nc_rna has precedense over mrna_predicted
tx_data['is_primary'] = False
primary_transcripts = tx_data['primary_transcripts']
refseq_identifier = None
for category in TRANSCRIPT_CATEGORIES:
identifiers = tx_data[category]
if not identifiers:
continue
intersection = identifiers.intersection(primary_transcripts)
ref_seq_transcripts += 1
if intersection:
refseq_identifier = intersection.pop()
tx_data['is_primary'] = True
nr_primary_transcripts += 1
else:
refseq_identifier = identifiers.pop()
# If there was refseq identifiers we break the loop
break
if refseq_identifier:
tx_data['refseq_id'] = refseq_identifier
#################### #################### ####################
# Build the transcript object
tx_obj = build_transcript(tx_data, build)
transcript_objs.append(tx_obj)
# Load all transcripts
LOG.info("Loading transcripts...")
if len(transcript_objs) > 0:
adapter.load_transcript_bulk(transcript_objs)
LOG.info('Number of transcripts in build %s: %s', build, nr_transcripts)
LOG.info('Number of transcripts with refseq identifier: %s',
ref_seq_transcripts)
LOG.info('Number of primary transcripts: %s', nr_primary_transcripts)
return transcript_objs
def load_transcripts(adapter, transcripts_lines=None, build='37', ensembl_genes=None):
"""Load all the transcripts
Transcript information is from ensembl.
Args:
adapter(MongoAdapter)
transcripts_lines(iterable): iterable with ensembl transcript lines
build(str)
ensembl_genes(dict): Map from ensembl_id -> HgncGene
Returns:
transcript_objs(list): A list with all transcript objects
"""
# Fetch all genes with ensemblid as keys
ensembl_genes = ensembl_genes or adapter.ensembl_genes(build)
if transcripts_lines is None:
transcripts_lines = fetch_ensembl_transcripts(build=build)
# Map with all transcripts enstid -> parsed transcript
transcripts_dict = parse_transcripts(transcripts_lines)
for ens_tx_id in list(transcripts_dict):
parsed_tx = transcripts_dict[ens_tx_id]
# Get the ens gene id
ens_gene_id = parsed_tx['ensembl_gene_id']
# Fetch the internal gene object to find out the correct hgnc id
gene_obj = ensembl_genes.get(ens_gene_id)
# If the gene is non existing in scout we skip the transcript
if not gene_obj:
transcripts_dict.pop(ens_tx_id)
LOG.debug("Gene %s does not exist in build %s", ens_gene_id, build)
continue
# Add the correct hgnc id
parsed_tx['hgnc_id'] = gene_obj['hgnc_id']
# Primary transcript information is collected from HGNC
parsed_tx['primary_transcripts'] = set(gene_obj.get('primary_transcripts', []))
ref_seq_transcripts = 0
nr_primary_transcripts = 0
nr_transcripts = len(transcripts_dict)
transcript_objs = []
with progressbar(transcripts_dict.values(), label="Building transcripts", length=nr_transcripts) as bar:
for tx_data in bar:
#################### Get the correct refseq identifier ####################
# We need to decide one refseq identifier for each transcript, if there are any to
# choose from. The algorithm is as follows:
# If there is ONE mrna this is choosen
# If there are several mrna the one that is in 'primary_transcripts' is choosen
# Else one is choosen at random
# The same follows for the other categories where nc_rna has precedense over mrna_predicted
# We will store all refseq identifiers in a "refseq_identifiers" list as well
tx_data['is_primary'] = False
primary_transcripts = tx_data['primary_transcripts']
refseq_identifier = None
refseq_identifiers = []
for category in TRANSCRIPT_CATEGORIES:
identifiers = tx_data[category]
if not identifiers:
continue
for refseq_id in identifiers:
# Add all refseq identifiers to refseq_identifiers
refseq_identifiers.append(refseq_id)
ref_seq_transcripts += 1
if refseq_id in primary_transcripts:
refseq_identifier = refseq_id
tx_data['is_primary'] = True
nr_primary_transcripts += 1
if not refseq_identifier:
refseq_identifier = refseq_id
if refseq_identifier:
tx_data['refseq_id'] = refseq_identifier
if refseq_identifiers:
tx_data['refseq_identifiers'] = refseq_identifiers
#################### #################### ####################
# Build the transcript object
tx_obj = build_transcript(tx_data, build)
transcript_objs.append(tx_obj)
# Load all transcripts
LOG.info("Loading transcripts...")
if len(transcript_objs) > 0:
adapter.load_transcript_bulk(transcript_objs)
LOG.info('Number of transcripts in build %s: %s', build, nr_transcripts)
LOG.info('Number of transcripts with refseq identifier: %s', ref_seq_transcripts)
LOG.info('Number of primary transcripts: %s', nr_primary_transcripts)
return transcript_objs
