def main(gff_file, fasta_file, prefix, min_len=50):
gff = gt.parser(gff_file=gff_file, fasta_file=fasta_file)
scaffolds = sorted(gff.seq.items(), key=lambda x: len(x[1]), reverse=True)
gene_counter = 0
with open('namemap.tsv', 'w') as namemap:
for seqid, seq in scaffolds:
genes = gff.getitems(featuretype='gene', seqid=seqid)
sorted_genes = sorted(genes, key=lambda sub: sub.get_start())
for gene in sorted_genes:
transcript = list(gff.get_children(gene,
featuretype='mRNA'))[0]
if len(transcript.pep) < min_len:
continue
gene_counter += 1
gene_id = format_id(prefix, gene_counter)
namemap.write('{0}\t{1}\n'.format(gene.ID, gene_id))
gene.ID = gene_id
gene.source = 'BRAKER'
print '\t'.join(gene.gff_fields)
transcript_id = '{0}.1'.format(gene_id)
transcript.ID = transcript_id
transcript.parents = [gene_id]
transcript.source = 'BRAKER'
print '\t'.join(transcript.gff_fields)
cds_counter = 0
for cds in gff.get_children(transcript, featuretype='CDS'):
cds_counter += 1
cds_id = '{0}.CDS{1}'.format(gene_id, cds_counter)
cds.ID = cds_id
cds.source = 'BRAKER'
cds.parents = [transcript_id]
print '\t'.join(cds.gff_fields)
def main(gff_file, fasta_file, prefix, min_len = 50):
gff = gt.parser(gff_file = gff_file, fasta_file = fasta_file,
limit = dict(featuretype = ['gene','mRNA','CDS']))
scaffolds = sorted(gff.seq.items(), key = lambda x: len(x[1]), reverse = True)
gene_counter = 0
for seqid,seq in scaffolds:
genes = gff.getitems(featuretype = 'gene', seqid = seqid)
sorted_genes = sorted(genes, key = lambda sub: sub.get_start())
for gene in sorted_genes:
transcript1 = list(gff.get_children(gene, featuretype = 'mRNA'))[0]
if len(transcript1.pep) < min_len:
continue
gene_counter += 1
gene_id = format_id(prefix, gene_counter)
gene.ID = gene_id
gene.source = 'BRAKER'
print '\t'.join(gene.gff_fields)
mRNA_counter = 0
for transcript in gff.get_children(gene, featuretype = 'mRNA'):
mRNA_counter += 1
transcript_id = '{0}.{1}'.format(gene_id, mRNA_counter)
transcript.ID = transcript_id
transcript.parents = [gene_id]
transcript.source = 'BRAKER'
print '\t'.join(transcript.gff_fields)
cds_counter = 0
for cds in gff.get_children(transcript, featuretype = 'CDS'):
cds_counter += 1
cds_id = '{0}.CDS{1}'.format(gene_id, cds_counter)
cds.ID = cds_id
cds.source = 'BRAKER'
cds.parents = [transcript_id]
print '\t'.join(cds.gff_fields)
def init(gff_file,fasta_file,gene_collection,track_collection):
print 'parsing gff'
assembly = fasta_file.split('/')[-1]
track = gff_file.split('/')[-1]
track_name = '.'.join(track.split('.')[:-1])
gff = gt.parser(gff_file,fasta_file=fasta_file)
gff_md5 = get_md5(gff_file)
bulk = gene_collection.initialize_unordered_bulk_op()
print 'initializing upload'
meta = {'track':track,'assembly':assembly,'md5':gff_md5}
for gene in gff.getitems(featuretype='gene'):
gene_dic = model_gene_feature(gene)
gene_dic['track'] = track
gene_dic['assembly'] = assembly
bulk.insert(gene_dic)
meta.setdefault('gene',0)
meta['gene'] += 1
print 'uploading to mongodb'
print '...'
bulk.execute()
print 'uploaded {0} genes'.format(meta['gene'])
print 'indexing'
gene_collection.create_index('ID')
gene_collection.create_index('type')
gene_collection.create_index([('seqid',pymongo.TEXT),('start',pymongo.ASCENDING),('end',pymongo.ASCENDING)])
gene_collection.create_index('subfeatures.ID')
print 'setting metadata'
track_collection.insert_one(meta)
def main(gff_file, namemap):
gff = gt.parser(gff_file=gff_file)
with open('namemap.tsv', 'r') as namemap:
genes = gff.getitems(featuretype='gene')
sorted_genes = sorted(genes, key=lambda sub: sub.get_start())
genefind = namemap.read()
for gene in sorted_genes:
number = genefind.find(gene.ID)
print "%s : %s " % (gene.ID, number)
def test1():
for gff_file in (gff_success,gff_fail):
gff = gt.parser(gff_file,fasta_file=fasta_file)
for transcript in gff.getitems(featuretype='mRNA'):
if transcript.pep[0] != 'M':
print gff_file
print transcript.pep
print transcript
raise Exception('Wrong start')
if transcript.pep[-1] != '*':
print gff_file
print transcript.pep
print transcript
raise Exception('Wrong stop')
def main(gff_file, fasta_file):
gff = gt.parser(gff_file, fasta_file=fasta_file)
cds_file = '{0}.CDS.fasta'.format(gff_file)
pep_file = '{0}.PEP.fasta'.format(gff_file)
transcripts = gff.getitems(featuretype='mRNA')
sorted_transcripts = sorted(transcripts, key=lambda t: t.ID)
with open(cds_file, 'w') as cds_handle, open(pep_file, 'w') as pep_handle:
for transcript in sorted_transcripts:
if len(transcript.seq) == 0:
continue
name = transcript.attributes.get('Name', [''])[0]
header = '>{0} {1}\n'.format(transcript.ID, name)
seq = splitter(transcript.seq, 60)
pep = splitter(transcript.pep, 60)
cds_handle.write(header)
for s in seq:
cds_handle.write(s + '\n')
pep_handle.write(header)
for p in pep:
pep_handle.write(p + '\n')
def main(infile=None):
client_ip = get_client()
client = MongoClient(client_ip)
db = client.meteor
collection = db.interpro
ipr_hierarchy = get_ipr_hierarchy()
upload(collection,ipr_hierarchy)
#for ipr in ipr_hierarchy:
# print ipr
quit()
ipr_combinations = get_ipr_combinations()
gff = gt.parser(infile)
names = {}
for polypeptide in gff.getitems(featuretype='polypeptide'):
domains = set()
for protein_match in gff.get_children(polypeptide,featuretype='protein_match'):
interpro = get_interpro(protein_match)
if not interpro or interpro not in ipr_hierarchy:
continue
name = ipr_hierarchy[interpro].name
if 'DUF' in name or 'unknown' in name:
continue
domains.add(protein_match)
if not domains:
name = ['None']
else:
reduced_domains = reduce_domains(domains,ipr_hierarchy)
if len(reduced_domains) == 1:
name = [reduced_domains[0].name]
else:
name = find_similar_names(reduced_domains)
#print name
if len(name) != 1:
name = [ipr_combinations.get(name,name[0])]
name = name[0]
if name.endswith('domain') or name.endswith('fold'):
name += ' containing protein'
print '\t'.join([polypeptide.ID,name])
'''
def upload_ips(gff_file,gene_collection,interpro_collection):
print 'parsing gff'
gff = gt.parser(gff_file)
counter = 0
all_interpro = set()
print 'uploading to mongodb'
print 'adding to genes'
for polypeptide in gff.getitems(featuretype='polypeptide'):
counter += 1
for protein_match in gff.get_children(polypeptide,featuretype='protein_match'):
protein_match_ID = re.sub('\.','.',protein_match.ID)
gene_key = {'subfeatures.ID':polypeptide.ID}
gene_update = {'$set': {
'subfeatures.$.interproscan.'+protein_match_ID: {
'start' : protein_match.start,
'end' : protein_match.end,
'score' : protein_match.score,
'source' : protein_match.source,
'signature_desc' : ','.join(protein_match.attributes.get('signature_desc',[''])),
'dbxref' : ','.join(protein_match.attributes.get('Dbxref',[''])),
'name' : ','.join(protein_match.attributes.get('Name',['']))
}
}
}
dbxref = protein_match.attributes.get('Dbxref',None)
if dbxref:
dbxref_dict = {'domains.'+kv[0]:kv[1] for kv in [d.split(':') for d in dbxref]}
gene_update['$addToSet'] = dbxref_dict
interpro = dbxref_dict.get('domains.InterPro',None)
if interpro:
all_interpro.add(interpro)
gene_collection.update(gene_key,gene_update)
print 'fetching additional interpro data'
all_interpro = list(all_interpro)
for domains in (all_interpro[i:i+100] for i in xrange(0,len(all_interpro),100)):
url = 'http://www.ebi.ac.uk/Tools/dbfetch/dbfetch/interpro/{0}/tab'.format(','.join(domains))
response = urlopen(url)
for line in response.readlines():
line = line.strip()
if not line or line[0] == '#':
continue
parts = line.split('\t')
rand_color = randomcolor.RandomColor(seed=parts[0])
interpro_key = {'ID':parts[0]}
interpro_update = {'$set': {
'type':parts[1],
'short_name':parts[2],
'description':parts[3],
'color':rand_color.generate(format_='rgb')[0]
}}
domains.remove(parts[0])
interpro_collection.update(interpro_key,interpro_update,upsert=True)
if domains:
for domain in domains:
interpro_key = {'ID':domain}
interpro_update = {'$set': {
'type':'ERROR',
'short_name':'ERROR',
'description':'This domain was found with interproscan, but could not be found on the interpro site',
'color':'black'
}}
interpro_collection.update(interpro_key,interpro_update,upsert=True)
