def annotate_novel_coding(assembled_gtf, ref_gtf, ref_fasta, data, out_file=None):
if not out_file:
out_file = os.path.splitext(assembled_gtf)[0] + ".annotated.gtf"
if file_exists(out_file):
return out_file
classification = cpat.classify_with_cpat(assembled_gtf, ref_gtf,
ref_fasta, data)
if not classification:
logger.info("Protein coding classification of %s was skipped because "
"CPAT was not found." % assembled_gtf)
return assembled_gtf
ref_db = gtf.get_gtf_db(ref_gtf)
known_transcript = {feature['transcript_id'][0]: feature.source for feature in
gtf.complete_features(ref_db)}
assembled_db = gtf.get_gtf_db(assembled_gtf)
with file_transaction(out_file) as tx_out_file:
with open(tx_out_file, 'w') as out_handle:
for feature in gtf.complete_features(assembled_db):
transcript_id = feature['transcript_id'][0]
if transcript_id not in known_transcript:
feature.source = classification[transcript_id]
else:
feature.source = known_transcript[transcript_id]
out_handle.write(str(feature) + "\n")
return out_file
def annotate_novel_coding(assembled_gtf, ref_gtf, ref_fasta, out_file=None):
if not out_file:
out_file = os.path.splitext(assembled_gtf)[0] + ".annotated.gtf"
if file_exists(out_file):
return out_file
classification = cpat.classify_with_cpat(assembled_gtf, ref_gtf, ref_fasta)
ref_db = gtf.get_gtf_db(ref_gtf)
known_transcript = {feature['transcript_id'][0]: feature.source for feature in
gtf.complete_features(ref_db)}
assembled_db = gtf.get_gtf_db(assembled_gtf)
with file_transaction(out_file) as tx_out_file:
with open(tx_out_file, 'w') as out_handle:
for feature in gtf.complete_features(assembled_db):
transcript_id = feature['transcript_id'][0]
if transcript_id not in known_transcript:
feature.source = classification[transcript_id]
else:
feature.source = known_transcript[transcript_id]
out_handle.write(str(feature) + "\n")
return out_file
def cleanup_transcripts(assembled_gtf, ref_gtf, ref_fasta, out_file=None):
"""
Clean up a GTF file of assembled transcripts
1) if a known gene is known to code for a protein, remove any *novel*
isoforms of the that do not also code for a protein.
2) if a new gene has been annotated and none of its isoforms are protein
coding and it is > 200 bp, mark it as a lincRNA. < 200 bp mark it as ncRNA
"""
if not out_file:
out_file = os.path.splitext(assembled_gtf)[0] + ".cleaned.gtf"
if file_exists(out_file):
return out_file
ref_db = gtf.get_gtf_db(ref_gtf)
known_transcript = {
feature['transcript_id'][0]: feature.source
for feature in gtf.complete_features(ref_db)
}
ref_gene_to_source = gtf.get_gene_source_set(ref_gtf)
assembled_db = gtf.get_gtf_db(assembled_gtf)
assembled_fasta = gtf.gtf_to_fasta(assembled_gtf, ref_fasta)
lengths = fasta.sequence_length(assembled_fasta)
with file_transaction(out_file) as tx_out_file:
with open(tx_out_file, 'w') as out_handle:
for feature in gtf.complete_features(assembled_db):
transcript_id = feature['transcript_id'][0]
gene_id = feature['gene_id'][0]
if transcript_id in known_transcript:
out_handle.write(str(feature) + "\n")
continue
known_coding = "protein_coding" in ref_gene_to_source.get(
gene_id, [None])
if known_coding and feature.source != "protein_coding":
continue
if feature.source != "protein_coding":
if lengths[transcript_id] > 200:
feature.source = "lincRNA"
else:
feature.source = "ncRNA"
out_handle.write(str(feature) + "\n")
return out_file
def annotate_novel_coding(assembled_gtf, ref_gtf, ref_fasta, data, out_file=None):
if not out_file:
out_file = os.path.splitext(assembled_gtf)[0] + ".annotated.gtf"
if file_exists(out_file):
return out_file
classification = cpat.classify_with_cpat(assembled_gtf, ref_gtf, ref_fasta, data)
if not classification:
logger.info("Protein coding classification of %s was skipped because " "CPAT was not found." % assembled_gtf)
return assembled_gtf
ref_db = gtf.get_gtf_db(ref_gtf)
known_transcript = {feature["transcript_id"][0]: feature.source for feature in gtf.complete_features(ref_db)}
assembled_db = gtf.get_gtf_db(assembled_gtf)
with file_transaction(out_file) as tx_out_file:
with open(tx_out_file, "w") as out_handle:
for feature in gtf.complete_features(assembled_db):
transcript_id = feature["transcript_id"][0]
if transcript_id not in known_transcript:
feature.source = classification[transcript_id]
else:
feature.source = known_transcript[transcript_id]
out_handle.write(str(feature) + "\n")
return out_file
def cleanup_transcripts(assembled_gtf, ref_gtf, ref_fasta, out_file=None):
"""
Clean up a GTF file of assembled transcripts
1) if a known gene is known to code for a protein, remove any *novel*
isoforms of the that do not also code for a protein.
2) if a new gene has been annotated and none of its isoforms are protein
coding and it is > 200 bp, mark it as a lincRNA. < 200 bp mark it as ncRNA
"""
if not out_file:
out_file = os.path.splitext(assembled_gtf)[0] + ".cleaned.gtf"
if file_exists(out_file):
return out_file
ref_db = gtf.get_gtf_db(ref_gtf)
known_transcript = {feature['transcript_id'][0]: feature.source for feature
in gtf.complete_features(ref_db)}
ref_gene_to_source = gtf.get_gene_source_set(ref_gtf)
assembled_db = gtf.get_gtf_db(assembled_gtf)
assembled_fasta = gtf.gtf_to_fasta(assembled_gtf, ref_fasta)
lengths = fasta.sequence_length(assembled_fasta)
with file_transaction(out_file) as tx_out_file:
with open(tx_out_file, 'w') as out_handle:
for feature in gtf.complete_features(assembled_db):
transcript_id = feature['transcript_id'][0]
gene_id = feature['gene_id'][0]
if transcript_id in known_transcript:
out_handle.write(str(feature) + "\n")
continue
known_coding = "protein_coding" in ref_gene_to_source.get(gene_id, [None])
if known_coding and feature.source != "protein_coding":
continue
if feature.source != "protein_coding":
if lengths[transcript_id] > 200:
feature.source = "lincRNA"
else:
feature.source = "ncRNA"
out_handle.write(str(feature) + "\n")
return out_file
