def run(self):
for ens_id, a in self.annotation_iterator():
if comp_ann_lib.short_cds(a) is True and a.cds_size != 0:
bed_rec = seq_lib.cds_coordinate_to_bed(
a, 0, a.cds_size, self.rgb, self.column)
self.details_dict[ens_id].append(bed_rec)
self.classify_dict[ens_id] = 1
else:
self.classify_dict[ens_id] = 0
self.dump_results_to_disk()
def run(self):
for aug_aln_id, aug_t, t in self.augustus_transcript_transmap_iterator(
):
if t.thick_stop != aug_t.thick_stop:
s = aug_t.cds_size
bed_rec = seq_lib.cds_coordinate_to_bed(
aug_t, s - 3, s, self.rgb, self.column)
self.details_dict[aug_aln_id].append(bed_rec)
self.classify_dict[aug_aln_id] = 1
else:
self.classify_dict[aug_aln_id] = 0
self.dump_results_to_disk()
def BeginStart(transcript_dict, seq_dict):
classify_dict = {}
details_dict = {}
for ens_id, t in transcript_iterator(transcript_dict):
seq = t.get_cds(seq_dict)
if len(seq) <= 75:
continue
if seq[:3] != "ATG":
classify_dict[ens_id] = 1
details_dict[ens_id] = seq_lib.cds_coordinate_to_bed(t, 0, 3, rgb, sys._getframe().f_code.co_name)
else:
classify_dict[ens_id] = 0
return classify_dict, details_dict
def StartOutOfFrame(transcript_dict, seq_dict):
classify_dict = {}
details_dict = {}
for ens_id, t in transcript_iterator(transcript_dict):
if t.getCdsLength() <= 75:
continue
t_frames = [x for x in t.exon_frames if x != -1]
if t.strand is True and t_frames[0] != 0 or t.strand is False and t_frames[-1] != 0:
classify_dict[ens_id] = 1
details_dict[ens_id] = seq_lib.cds_coordinate_to_bed(t, 0, 3, rgb, sys._getframe().f_code.co_name)
continue
classify_dict[ens_id] = 0
return classify_dict, details_dict
def run(self):
self.get_fasta()
for ens_id, a in self.annotation_iterator():
# do not include noncoding transcripts or lift-overs that contain less than short_cds_size
if comp_ann_lib.short_cds(a):
self.classify_dict[ens_id] = 0
elif a.get_cds(self.ref_seq_dict)[:3] != "ATG":
bed_rec = seq_lib.cds_coordinate_to_bed(
a, 0, 3, self.rgb, self.column)
self.details_dict[ens_id].append(bed_rec)
self.classify_dict[ens_id] = 1
else:
self.classify_dict[ens_id] = 0
self.dump_results_to_disk()
def InFrameStop(transcript_dict, seq_dict):
classify_dict = {}
details_dict = defaultdict(list)
for ens_id, t in transcript_iterator(transcript_dict):
cds = t.get_cds(seq_dict)
offset = seq_lib.find_offset(t.exonFrames, t.strand)
for i, codon in seq_lib.read_codons_with_position(cds, offset, skip_last=True):
amino_acid = seq_lib.codon_to_amino_acid(codon)
if amino_acid == "*":
classify_dict[ens_id] = 1
details_dict[ens_id].append(seq_lib.cds_coordinate_to_bed(t, i, i + 3, rgb,
sys._getframe().f_code.co_name))
if ens_id not in classify_dict:
classify_dict[ens_id] = 0
return classify_dict, details_dict
def EndStop(transcript_dict, seq_dict):
stop_codons = ('TAA', 'TGA', 'TAG')
classify_dict = {}
details_dict = defaultdict(list)
for ens_id, t in transcript_iterator(transcript_dict):
seq = t.get_cds(seq_dict)
s = len(seq)
if s <= 75:
continue
if seq[-3:] not in stop_codons:
classify_dict[ens_id] = 1
details_dict[ens_id] = seq_lib.cds_coordinate_to_bed(t, s - 3, s, rgb, sys._getframe().f_code.co_name)
else:
classify_dict[ens_id] = 0
return classify_dict, details_dict
def run(self):
self.get_fasta()
for ens_id, a in self.annotation_iterator():
cds = a.get_cds(self.ref_seq_dict)
offset = seq_lib.find_offset(a.exon_frames, a.strand)
for i, codon in seq_lib.read_codons_with_position(cds,
offset,
skip_last=True):
amino_acid = seq_lib.codon_to_amino_acid(codon)
if amino_acid == "*":
bed_rec = seq_lib.cds_coordinate_to_bed(
a, i, i + 3, self.rgb, self.column)
self.details_dict[ens_id].append(bed_rec)
self.classify_dict[ens_id] = len(self.details_dict[ens_id])
self.dump_results_to_disk()
def run(self):
for ens_id, a in self.annotation_iterator():
# do not include noncoding transcripts or lift-overs that contain less than short_cds_size
if comp_ann_lib.short_cds(a):
self.classify_dict[ens_id] = 0
continue
# remove all -1 frames because those are UTR exons
a_frames = [x for x in a.exon_frames if x != -1]
if a.strand is True and a_frames[
0] != 0 or a.strand is False and a_frames[-1] != 0:
self.classify_dict[ens_id] = 1
self.details_dict[ens_id].append(
seq_lib.cds_coordinate_to_bed(a, 0, 3, self.rgb,
self.column))
else:
self.classify_dict[ens_id] = 0
self.dump_results_to_disk()
def unknown_base(transcript_dict, seq_dict, r, cds):
classify_dict = {}
details_dict = {}
for ens_id, t in transcript_iterator(transcript_dict):
if cds is True:
s = t.get_cds(seq_dict)
tmp = [seq_lib.cds_coordinate_to_bed(t, m.start(), m.end(), rgb, sys._getframe().f_code.co_name) for m in
re.finditer(r, s)]
else:
s = t.get_mrna(seq_dict)
tmp = [seq_lib.transcript_coordinate_to_bed(t, m.start(), m.end(), rgb, sys._getframe().f_code.co_name)
for m in re.finditer(r, s)]
if len(tmp) > 0:
details_dict[ens_id] = tmp
classify_dict[ens_id] = 1
else:
classify_dict[ens_id] = 0
return classify_dict, details_dict
def run(self, equality_test=lambda target, query: target != query):
self.get_fasta()
for aln_id, aln, t, a in self.alignment_transcript_annotation_iterator(
):
# do not include noncoding transcripts or lift-overs that contain less than short_cds_size
if comp_ann_lib.short_cds(t) or comp_ann_lib.short_cds(a):
self.classify_dict[aln_id] = 0
continue
for i, target_codon, query_codon in comp_ann_lib.codon_pair_iterator(
a, t, aln, self.seq_dict, self.ref_seq_dict):
target_aa = seq_lib.codon_to_amino_acid(target_codon)
query_aa = seq_lib.codon_to_amino_acid(query_codon)
if target_codon != query_codon and equality_test(
target_aa, query_aa) is True:
bed_rec = seq_lib.cds_coordinate_to_bed(
t, i, i + 3, self.rgb, self.column)
self.details_dict[aln_id].append(bed_rec)
self.classify_dict[aln_id] = len(self.details_dict[aln_id])
self.dump_results_to_disk()
def run(self):
for aln_id, aln, t, a in self.alignment_transcript_annotation_iterator(
):
# do not include noncoding transcripts or lift-overs that contain less than short_cds_size
if comp_ann_lib.short_cds(t) or comp_ann_lib.short_cds(a):
self.classify_dict[aln_id] = 0
continue
cds_positions = [
t.chromosome_coordinate_to_cds(
aln.query_coordinate_to_target(
a.cds_coordinate_to_transcript(i))) for i in xrange(3)
]
if None in cds_positions:
self.details_dict[aln_id].append(
seq_lib.cds_coordinate_to_bed(t, 0, 3, self.rgb,
self.column))
self.classify_dict[aln_id] = 1
else:
self.classify_dict[aln_id] = 0
self.dump_results_to_disk()
