def _format_gene_models(parent_nf_map, child_nf_map):
"""
Genarate GeneObject based on the parsed file contents
parent_map: parent features with source and chromosome information
child_map: transctipt and exon information are encoded
"""
g_cnt = 0
gene = np.zeros((len(parent_nf_map), ), dtype=utils.init_gene_DE())
for pkey, pdet in parent_nf_map.items():
# considering only gene features
if not re.search(r'gene', pdet.get('type', '')):
continue
# infer the gene start and stop if not there in the
if not pdet.get('location', []):
GNS, GNE = [], []
# multiple number of transcripts
for L1 in child_nf_map[pkey]:
GNS.append(L1.get('location', [])[0])
GNE.append(L1.get('location', [])[1])
GNS.sort()
GNE.sort()
pdet['location'] = [GNS[0], GNE[-1]]
orient = pdet.get('strand', '')
gene[g_cnt]['id'] = g_cnt + 1
gene[g_cnt]['chr'] = pkey[0]
gene[g_cnt]['source'] = pkey[1]
gene[g_cnt]['name'] = pkey[-1]
gene[g_cnt]['start'] = pdet.get('location', [])[0]
gene[g_cnt]['stop'] = pdet.get('location', [])[1]
gene[g_cnt]['strand'] = orient
# default value
gene[g_cnt]['is_alt_spliced'] = 0
if len(child_nf_map[pkey]) > 1:
gene[g_cnt]['is_alt_spliced'] = 1
# complete sub-feature for all transcripts
dim = len(child_nf_map[pkey])
TRS = np.zeros((dim, ), dtype=np.object)
EXON = np.zeros((dim, ), dtype=np.object)
# fetching corresponding transcripts
for xq, Lv1 in enumerate(child_nf_map[pkey]):
TID = Lv1.get('ID', '')
TRS[xq] = np.array([TID])
orient = Lv1.get('strand', '')
# fetching different sub-features
child_feat = defaultdict(list)
for Lv2 in child_nf_map[(pkey[0], pkey[1], TID)]:
E_TYP = Lv2.get('type', '')
child_feat[E_TYP].append(Lv2.get('location'))
# make exon coordinate from cds and utr regions
if not child_feat.get('exon'):
if child_feat.get('CDS'):
exon_cod = utils.make_Exon_cod(
orient,
NonetoemptyList(child_feat.get('five_prime_UTR')),
NonetoemptyList(child_feat.get('CDS')),
NonetoemptyList(child_feat.get('three_prime_UTR')))
child_feat['exon'] = exon_cod
else:
# searching through keys to find a pattern describing exon feature
ex_key_pattern = [
k for k in child_feat if k.endswith("exon")
]
child_feat['exon'] = child_feat[ex_key_pattern[0]]
# TODO only UTR's
# make general ascending order of coordinates
if orient == '-':
for etype, excod in child_feat.items():
if len(excod) > 1:
if excod[0][0] > excod[-1][0]:
excod.reverse()
child_feat[etype] = excod
# add sub-feature # make array for export to different out
EXON[xq] = np.array(child_feat.get('exon'), np.float64)
# add sub-features to the parent gene feature
gene[g_cnt]['transcripts'] = TRS
gene[g_cnt]['exons'] = EXON
gene[g_cnt]['gene_info'] = dict(ID=pkey[-1],
Name=pdet.get('name'),
Source=pkey[1])
g_cnt += 1
## deleting empty gene records from the main array
for XP, ens in enumerate(gene):
if ens[0] == 0:
break
XQC = range(XP, len(gene) + 1)
gene = np.delete(gene, XQC)
return gene
def format_gene_models(parent_nf_map, child_nf_map):
"""
Genarate GeneObject based on the parsed file contents
@args parent_nf_map: parent features with source and chromosome information
@type parent_nf_map: collections defaultdict
@args child_nf_map: transctipt and exon information are encoded
@type child_nf_map: collections defaultdict
"""
g_cnt = 0
gene = np.zeros((len(parent_nf_map),), dtype = utils.init_gene())
for pkey, pdet in parent_nf_map.items():
# considering only gene features
#if not re.search(r'gene', pdet.get('type', '')):
# continue
# infer the gene start and stop if not there in the
if not pdet.get('location', []):
GNS, GNE = [], []
# multiple number of transcripts
for L1 in child_nf_map[pkey]:
GNS.append(L1.get('location', [])[0])
GNE.append(L1.get('location', [])[1])
GNS.sort()
GNE.sort()
pdet['location'] = [GNS[0], GNE[-1]]
orient = pdet.get('strand', '')
gene[g_cnt]['id'] = g_cnt +1
gene[g_cnt]['chr'] = pkey[0]
gene[g_cnt]['source'] = pkey[1]
gene[g_cnt]['name'] = pkey[-1]
gene[g_cnt]['start'] = pdet.get('location', [])[0]
gene[g_cnt]['stop'] = pdet.get('location', [])[1]
gene[g_cnt]['strand'] = orient
gene[g_cnt]['score'] = pdet.get('score','')
# default value
gene[g_cnt]['is_alt_spliced'] = gene[g_cnt]['is_alt'] = 0
if len(child_nf_map[pkey]) > 1:
gene[g_cnt]['is_alt_spliced'] = gene[g_cnt]['is_alt'] = 1
# complete sub-feature for all transcripts
dim = len(child_nf_map[pkey])
TRS = np.zeros((dim,), dtype=np.object)
TR_TYP = np.zeros((dim,), dtype=np.object)
EXON = np.zeros((dim,), dtype=np.object)
UTR5 = np.zeros((dim,), dtype=np.object)
UTR3 = np.zeros((dim,), dtype=np.object)
CDS = np.zeros((dim,), dtype=np.object)
TISc = np.zeros((dim,), dtype=np.object)
TSSc = np.zeros((dim,), dtype=np.object)
CLV = np.zeros((dim,), dtype=np.object)
CSTOP = np.zeros((dim,), dtype=np.object)
TSTAT = np.zeros((dim,), dtype=np.object)
TSCORE = np.zeros((dim,), dtype=np.object)
# fetching corresponding transcripts
for xq, Lv1 in enumerate(child_nf_map[pkey]):
TID = Lv1.get('ID', '')
TRS[xq]= np.array([TID])
TYPE = Lv1.get('type', '')
TR_TYP[xq] = np.array('')
TR_TYP[xq] = np.array(TYPE) if TYPE else TR_TYP[xq]
orient = Lv1.get('strand', '')
tr_score = Lv1.get('score', '')
# fetching different sub-features
child_feat = defaultdict(list)
for Lv2 in child_nf_map[(pkey[0], pkey[1], TID)]:
E_TYP = Lv2.get('type', '')
child_feat[E_TYP].append(Lv2.get('location'))
# make general ascending order of coordinates
if orient == '-':
for etype, excod in child_feat.items():
if len(excod) > 1:
if excod[0][0] > excod[-1][0]:
excod.reverse()
child_feat[etype] = excod
# make exon coordinate from cds and utr regions
if not child_feat.get('exon'):
if child_feat.get('CDS'):
exon_cod = utils.make_Exon_cod( orient,
NonetoemptyList(child_feat.get('five_prime_UTR')),
NonetoemptyList(child_feat.get('CDS')),
NonetoemptyList(child_feat.get('three_prime_UTR')))
child_feat['exon'] = exon_cod
else:
# TODO only UTR's
# searching through keys to find a pattern describing exon feature
ex_key_pattern = [k for k in child_feat if k.endswith("exon")]
if ex_key_pattern:
child_feat['exon'] = child_feat[ex_key_pattern[0]]
# stop_codon are seperated from CDS, add the coordinates based on strand
if child_feat.get('stop_codon'):
if orient == '+':
if child_feat.get('stop_codon')[0][0] - child_feat.get('CDS')[-1][1] == 1:
child_feat['CDS'][-1] = [child_feat.get('CDS')[-1][0], child_feat.get('stop_codon')[0][1]]
else:
child_feat['CDS'].append(child_feat.get('stop_codon')[0])
elif orient == '-':
if child_feat.get('CDS')[0][0] - child_feat.get('stop_codon')[0][1] == 1:
child_feat['CDS'][0] = [child_feat.get('stop_codon')[0][0], child_feat.get('CDS')[0][1]]
else:
child_feat['CDS'].insert(0, child_feat.get('stop_codon')[0])
# transcript signal sites
TIS, cdsStop, TSS, cleave = [], [], [], []
cds_status, exon_status, utr_status = 0, 0, 0
if child_feat.get('exon'):
TSS = [child_feat.get('exon')[-1][1]]
TSS = [child_feat.get('exon')[0][0]] if orient == '+' else TSS
cleave = [child_feat.get('exon')[0][0]]
cleave = [child_feat.get('exon')[-1][1]] if orient == '+' else cleave
exon_status = 1
if child_feat.get('CDS'):
if orient == '+':
TIS = [child_feat.get('CDS')[0][0]]
cdsStop = [child_feat.get('CDS')[-1][1]-3]
else:
TIS = [child_feat.get('CDS')[-1][1]]
cdsStop = [child_feat.get('CDS')[0][0]+3]
cds_status = 1
# cds phase calculation
child_feat['CDS'] = utils.add_CDS_phase(orient, child_feat.get('CDS'))
# sub-feature status
if child_feat.get('three_prime_UTR') or child_feat.get('five_prime_UTR'):
utr_status =1
if utr_status == cds_status == exon_status == 1:
t_status = 1
else:
t_status = 0
# add sub-feature # make array for export to different out
TSTAT[xq] = t_status
EXON[xq] = np.array(child_feat.get('exon'), np.float64)
UTR5[xq] = np.array(NonetoemptyList(child_feat.get('five_prime_UTR')))
UTR3[xq] = np.array(NonetoemptyList(child_feat.get('three_prime_UTR')))
CDS[xq] = np.array(NonetoemptyList(child_feat.get('CDS')))
TISc[xq] = np.array(TIS)
CSTOP[xq] = np.array(cdsStop)
TSSc[xq] = np.array(TSS)
CLV[xq] = np.array(cleave)
TSCORE[xq] = tr_score
# add sub-features to the parent gene feature
gene[g_cnt]['transcript_status'] = TSTAT
gene[g_cnt]['transcripts'] = TRS
gene[g_cnt]['exons'] = EXON
gene[g_cnt]['utr5_exons'] = UTR5
gene[g_cnt]['cds_exons'] = CDS
gene[g_cnt]['utr3_exons'] = UTR3
gene[g_cnt]['transcript_type'] = TR_TYP
gene[g_cnt]['tis'] = TISc
gene[g_cnt]['cdsStop'] = CSTOP
gene[g_cnt]['tss'] = TSSc
gene[g_cnt]['cleave'] = CLV
gene[g_cnt]['transcript_score'] = TSCORE
gene[g_cnt]['gene_info'] = dict( ID = pkey[-1],
Name = pdet.get('name'),
Source = pkey[1])
# few empty fields // TODO fill this:
gene[g_cnt]['anno_id'] = []
gene[g_cnt]['confgenes_id'] = []
gene[g_cnt]['alias'] = ''
gene[g_cnt]['name2'] = []
gene[g_cnt]['chr_num'] = []
gene[g_cnt]['paralogs'] = []
gene[g_cnt]['transcript_valid'] = []
gene[g_cnt]['exons_confirmed'] = []
gene[g_cnt]['tis_conf'] = []
gene[g_cnt]['tis_info'] = []
gene[g_cnt]['cdsStop_conf'] = []
gene[g_cnt]['cdsStop_info'] = []
gene[g_cnt]['tss_info'] = []
gene[g_cnt]['tss_conf'] = []
gene[g_cnt]['cleave_info'] = []
gene[g_cnt]['cleave_conf'] = []
gene[g_cnt]['polya_info'] = []
gene[g_cnt]['polya_conf'] = []
gene[g_cnt]['is_valid'] = []
gene[g_cnt]['transcript_complete'] = []
gene[g_cnt]['is_complete'] = []
gene[g_cnt]['is_correctly_gff3_referenced'] = ''
gene[g_cnt]['splicegraph'] = []
g_cnt += 1
## deleting empty gene records from the main array
XPFLG=0
for XP, ens in enumerate(gene):
if ens[0]==0:
XPFLG=1
break
if XPFLG==1:
XQC = range(XP, len(gene)+1)
gene = np.delete(gene, XQC)
return gene
def format_gene_models(parent_nf_map, child_nf_map):
"""
Genarate GeneObject based on the parsed file contents
@args parent_nf_map: parent features with source and chromosome information
@type parent_nf_map: collections defaultdict
@args child_nf_map: transctipt and exon information are encoded
@type child_nf_map: collections defaultdict
"""
g_cnt = 0
gene = np.zeros((len(parent_nf_map), ), dtype=utils.init_gene())
for pkey, pdet in parent_nf_map.items():
# considering only gene features
#if not re.search(r'gene', pdet.get('type', '')):
# continue
# infer the gene start and stop if not there in the
if not pdet.get('location', []):
GNS, GNE = [], []
# multiple number of transcripts
for L1 in child_nf_map[pkey]:
GNS.append(L1.get('location', [])[0])
GNE.append(L1.get('location', [])[1])
GNS.sort()
GNE.sort()
pdet['location'] = [GNS[0], GNE[-1]]
orient = pdet.get('strand', '')
gene[g_cnt]['id'] = g_cnt + 1
gene[g_cnt]['chr'] = pkey[0]
gene[g_cnt]['source'] = pkey[1]
gene[g_cnt]['name'] = pkey[-1]
gene[g_cnt]['start'] = pdet.get('location', [])[0]
gene[g_cnt]['stop'] = pdet.get('location', [])[1]
gene[g_cnt]['strand'] = orient
gene[g_cnt]['score'] = pdet.get('score', '')
# default value
gene[g_cnt]['is_alt_spliced'] = gene[g_cnt]['is_alt'] = 0
if len(child_nf_map[pkey]) > 1:
gene[g_cnt]['is_alt_spliced'] = gene[g_cnt]['is_alt'] = 1
# complete sub-feature for all transcripts
dim = len(child_nf_map[pkey])
TRS = np.zeros((dim, ), dtype=np.object)
TR_TYP = np.zeros((dim, ), dtype=np.object)
EXON = np.zeros((dim, ), dtype=np.object)
UTR5 = np.zeros((dim, ), dtype=np.object)
UTR3 = np.zeros((dim, ), dtype=np.object)
CDS = np.zeros((dim, ), dtype=np.object)
TISc = np.zeros((dim, ), dtype=np.object)
TSSc = np.zeros((dim, ), dtype=np.object)
CLV = np.zeros((dim, ), dtype=np.object)
CSTOP = np.zeros((dim, ), dtype=np.object)
TSTAT = np.zeros((dim, ), dtype=np.object)
TSCORE = np.zeros((dim, ), dtype=np.object)
# fetching corresponding transcripts
for xq, Lv1 in enumerate(child_nf_map[pkey]):
TID = Lv1.get('ID', '')
TRS[xq] = np.array([TID])
TYPE = Lv1.get('type', '')
TR_TYP[xq] = np.array('')
TR_TYP[xq] = np.array(TYPE) if TYPE else TR_TYP[xq]
orient = Lv1.get('strand', '')
tr_score = Lv1.get('score', '')
# fetching different sub-features
child_feat = defaultdict(list)
for Lv2 in child_nf_map[(pkey[0], pkey[1], TID)]:
E_TYP = Lv2.get('type', '')
child_feat[E_TYP].append(Lv2.get('location'))
# make general ascending order of coordinates
if orient == '-':
for etype, excod in child_feat.items():
if len(excod) > 1:
if excod[0][0] > excod[-1][0]:
excod.reverse()
child_feat[etype] = excod
# make exon coordinate from cds and utr regions
if not child_feat.get('exon'):
if child_feat.get('CDS'):
exon_cod = utils.make_Exon_cod(
orient,
NonetoemptyList(child_feat.get('five_prime_UTR')),
NonetoemptyList(child_feat.get('CDS')),
NonetoemptyList(child_feat.get('three_prime_UTR')))
child_feat['exon'] = exon_cod
else:
# TODO only UTR's
# searching through keys to find a pattern describing exon feature
ex_key_pattern = [
k for k in child_feat if k.endswith("exon")
]
if ex_key_pattern:
child_feat['exon'] = child_feat[ex_key_pattern[0]]
# stop_codon are seperated from CDS, add the coordinates based on strand
if child_feat.get('stop_codon'):
if orient == '+':
if child_feat.get('stop_codon')[0][0] - child_feat.get(
'CDS')[-1][1] == 1:
child_feat['CDS'][-1] = [
child_feat.get('CDS')[-1][0],
child_feat.get('stop_codon')[0][1]
]
else:
child_feat['CDS'].append(
child_feat.get('stop_codon')[0])
elif orient == '-':
if child_feat.get('CDS')[0][0] - child_feat.get(
'stop_codon')[0][1] == 1:
child_feat['CDS'][0] = [
child_feat.get('stop_codon')[0][0],
child_feat.get('CDS')[0][1]
]
else:
child_feat['CDS'].insert(
0,
child_feat.get('stop_codon')[0])
# transcript signal sites
TIS, cdsStop, TSS, cleave = [], [], [], []
cds_status, exon_status, utr_status = 0, 0, 0
if child_feat.get('exon'):
TSS = [child_feat.get('exon')[-1][1]]
TSS = [child_feat.get('exon')[0][0]] if orient == '+' else TSS
cleave = [child_feat.get('exon')[0][0]]
cleave = [child_feat.get('exon')[-1][1]
] if orient == '+' else cleave
exon_status = 1
if child_feat.get('CDS'):
if orient == '+':
TIS = [child_feat.get('CDS')[0][0]]
cdsStop = [child_feat.get('CDS')[-1][1] - 3]
else:
TIS = [child_feat.get('CDS')[-1][1]]
cdsStop = [child_feat.get('CDS')[0][0] + 3]
cds_status = 1
# cds phase calculation
child_feat['CDS'] = utils.add_CDS_phase(
orient, child_feat.get('CDS'))
# sub-feature status
if child_feat.get('three_prime_UTR') or child_feat.get(
'five_prime_UTR'):
utr_status = 1
if utr_status == cds_status == exon_status == 1:
t_status = 1
else:
t_status = 0
# add sub-feature # make array for export to different out
TSTAT[xq] = t_status
EXON[xq] = np.array(child_feat.get('exon'), np.float64)
UTR5[xq] = np.array(
NonetoemptyList(child_feat.get('five_prime_UTR')))
UTR3[xq] = np.array(
NonetoemptyList(child_feat.get('three_prime_UTR')))
CDS[xq] = np.array(NonetoemptyList(child_feat.get('CDS')))
TISc[xq] = np.array(TIS)
CSTOP[xq] = np.array(cdsStop)
TSSc[xq] = np.array(TSS)
CLV[xq] = np.array(cleave)
TSCORE[xq] = tr_score
# add sub-features to the parent gene feature
gene[g_cnt]['transcript_status'] = TSTAT
gene[g_cnt]['transcripts'] = TRS
gene[g_cnt]['exons'] = EXON
gene[g_cnt]['utr5_exons'] = UTR5
gene[g_cnt]['cds_exons'] = CDS
gene[g_cnt]['utr3_exons'] = UTR3
gene[g_cnt]['transcript_type'] = TR_TYP
gene[g_cnt]['tis'] = TISc
gene[g_cnt]['cdsStop'] = CSTOP
gene[g_cnt]['tss'] = TSSc
gene[g_cnt]['cleave'] = CLV
gene[g_cnt]['transcript_score'] = TSCORE
gene[g_cnt]['gene_info'] = dict(ID=pkey[-1],
Name=pdet.get('name'),
Source=pkey[1])
# few empty fields // TODO fill this:
gene[g_cnt]['anno_id'] = []
gene[g_cnt]['confgenes_id'] = []
gene[g_cnt]['alias'] = ''
gene[g_cnt]['name2'] = []
gene[g_cnt]['chr_num'] = []
gene[g_cnt]['paralogs'] = []
gene[g_cnt]['transcript_valid'] = []
gene[g_cnt]['exons_confirmed'] = []
gene[g_cnt]['tis_conf'] = []
gene[g_cnt]['tis_info'] = []
gene[g_cnt]['cdsStop_conf'] = []
gene[g_cnt]['cdsStop_info'] = []
gene[g_cnt]['tss_info'] = []
gene[g_cnt]['tss_conf'] = []
gene[g_cnt]['cleave_info'] = []
gene[g_cnt]['cleave_conf'] = []
gene[g_cnt]['polya_info'] = []
gene[g_cnt]['polya_conf'] = []
gene[g_cnt]['is_valid'] = []
gene[g_cnt]['transcript_complete'] = []
gene[g_cnt]['is_complete'] = []
gene[g_cnt]['is_correctly_gff3_referenced'] = ''
gene[g_cnt]['splicegraph'] = []
g_cnt += 1
## deleting empty gene records from the main array
XPFLG = 0
for XP, ens in enumerate(gene):
if ens[0] == 0:
XPFLG = 1
break
if XPFLG == 1:
XQC = range(XP, len(gene) + 1)
gene = np.delete(gene, XQC)
return gene
def _format_gene_models(parent_nf_map, child_nf_map):
"""
Genarate GeneObject based on the parsed file contents
parent_map: parent features with source and chromosome information
child_map: transctipt and exon information are encoded
"""
g_cnt = 0
gene = np.zeros((len(parent_nf_map),), dtype = utils.init_gene_DE())
for pkey, pdet in parent_nf_map.items():
# considering only gene features
if not re.search(r'gene', pdet.get('type', '')):
continue
# infer the gene start and stop if not there in the
if not pdet.get('location', []):
GNS, GNE = [], []
# multiple number of transcripts
for L1 in child_nf_map[pkey]:
GNS.append(L1.get('location', [])[0])
GNE.append(L1.get('location', [])[1])
GNS.sort()
GNE.sort()
pdet['location'] = [GNS[0], GNE[-1]]
orient = pdet.get('strand', '')
gene[g_cnt]['id'] = g_cnt +1
gene[g_cnt]['chr'] = pkey[0]
gene[g_cnt]['source'] = pkey[1]
gene[g_cnt]['name'] = pkey[-1]
gene[g_cnt]['start'] = pdet.get('location', [])[0]
gene[g_cnt]['stop'] = pdet.get('location', [])[1]
gene[g_cnt]['strand'] = orient
# default value
gene[g_cnt]['is_alt_spliced'] = 0
if len(child_nf_map[pkey]) > 1:
gene[g_cnt]['is_alt_spliced'] = 1
# complete sub-feature for all transcripts
dim = len(child_nf_map[pkey])
TRS = np.zeros((dim,), dtype=np.object)
EXON = np.zeros((dim,), dtype=np.object)
# fetching corresponding transcripts
for xq, Lv1 in enumerate(child_nf_map[pkey]):
TID = Lv1.get('ID', '')
TRS[xq]= np.array([TID])
orient = Lv1.get('strand', '')
# fetching different sub-features
child_feat = defaultdict(list)
for Lv2 in child_nf_map[(pkey[0], pkey[1], TID)]:
E_TYP = Lv2.get('type', '')
child_feat[E_TYP].append(Lv2.get('location'))
# make exon coordinate from cds and utr regions
if not child_feat.get('exon'):
if child_feat.get('CDS'):
exon_cod = utils.make_Exon_cod( orient,
NonetoemptyList(child_feat.get('five_prime_UTR')),
NonetoemptyList(child_feat.get('CDS')),
NonetoemptyList(child_feat.get('three_prime_UTR')))
child_feat['exon'] = exon_cod
else:
# searching through keys to find a pattern describing exon feature
ex_key_pattern = [k for k in child_feat if k.endswith("exon")]
child_feat['exon'] = child_feat[ex_key_pattern[0]]
# TODO only UTR's
# make general ascending order of coordinates
if orient == '-':
for etype, excod in child_feat.items():
if len(excod) > 1:
if excod[0][0] > excod[-1][0]:
excod.reverse()
child_feat[etype] = excod
# add sub-feature # make array for export to different out
EXON[xq] = np.array(child_feat.get('exon'), np.float64)
# add sub-features to the parent gene feature
gene[g_cnt]['transcripts'] = TRS
gene[g_cnt]['exons'] = EXON
gene[g_cnt]['gene_info'] = dict( ID = pkey[-1],
Name = pdet.get('name'),
Source = pkey[1])
g_cnt += 1
## deleting empty gene records from the main array
XPFLG=0
for XP, ens in enumerate(gene):
if ens[0]==0:
XPFLG=1
break
if XPFLG==1:
XQC = range(XP, len(gene)+1)
gene = np.delete(gene, XQC)
return gene
