def get_cds(ref):
'''
assuming there is one contiguous coding region which might be
split into multiple sub-proteins like HA1 and HA2.
loop over all features, pull out min and max of their union
'''
cds_start, cds_end = np.inf, 0
for feature in ref.features:
if feature.type=='CDS':
if feature.location.start<cds_start:
cds_start=feature.location.start
if feature.location.end>cds_end:
cds_end=feature.location.end
refstr = str(ref.seq).upper()
refCDS = refstr[cds_start:cds_end]
refAA = safe_translate(refstr[cds_start:cds_end])
return refstr, refCDS, refAA, cds_start, cds_end
def codon_align(seq, refstr, refAA, cds_start, cds_end):
seqstr = str(seq.seq).upper()
score, refaln, seqaln = align_pairwise(refstr, seqstr)
if score<0: # did not align
return None
ref_aln_array = np.array(list(refaln))
seq_aln_array = np.array(list(seqaln))
# stip gaps
ungapped = ref_aln_array!='-'
ref_aln_array_ungapped = ref_aln_array[ungapped]
seq_aln_array_ungapped = seq_aln_array[ungapped]
seq5pUTR = "".join(seq_aln_array_ungapped[:cds_start])
seq3pUTR = "".join(seq_aln_array_ungapped[cds_end:])
seqCDS = "".join(seq_aln_array_ungapped[cds_start:cds_end])
seqCDS_ungapped = seqCDS.replace('-', '')
seqAA = safe_translate(seqCDS_ungapped)
scoreAA, refalnAA, seqalnAA = align_pairwise(refAA, seqAA)
if scoreAA<0 or sum(seqAA.count(x) for x in ['*', 'X'])>5 or refalnAA.count('-')>5:
print(seq.id, "didn't translate properly", file=sys.stderr)
return None
seqCDS_aln = seq5pUTR
pos = 0
for aa_ref, aa_seq in zip(refalnAA, seqalnAA):
if aa_seq=='-':
seqCDS_aln += '---'
# if the nucleotide sequence is gapped
# (i.e. because of missing data at the 5p and 3p end, advance pos)
if seqCDS_ungapped[pos:pos+3]=='---':
pos += 3
else:
if len(seqCDS_ungapped)>=pos+3:
seqCDS_aln += seqCDS_ungapped[pos:pos+3]
else:
seqCDS_aln += '---'
pos += 3
return ''.join(seqCDS_aln)+seq3pUTR
for seq in alignment:
if seq.id == ref.id:
continue
# read sequence and all its annotated features
seq_container = tmpNode()
seq_str = str(seq.seq)
seq_container.sequences['nuc'] = {
i: c
for i, c in enumerate(seq_str)
}
for fname, feat in features.items():
if feat.type != 'source':
seq_container.sequences[fname] = {
i: c
for i, c in enumerate(
safe_translate(feat.extract(seq_str)))
}
# for each clade, check whether it matches any of the clade definitions in the tsv
matches = []
for clade_name, clade_alleles in clade_designations.items():
if is_node_in_clade(clade_alleles, seq_container, ref):
matches.append(clade_name)
# print the last match as clade assignment and all others as ancestral clades
# note that this assumes that clades in the tsv are ordered by order of appearence.
# furthermore, this will only work if parent clades don't have definitions that exclude
# child clades, i.e. positions can only be additive for this to work.
if matches:
print(
f"{seq.description}\t{matches[-1]}\t{', '.join(matches[:-1])}",
ref = SeqIO.read(args.reference, 'genbank')
# assuming there is one contiguous coding region which might be
# split into multiple sub-proteins like HA1 and HA2.
# loop over all features, pull out min and max of their union
cds_start, cds_end = np.inf, 0
for feature in ref.features:
if feature.type == 'CDS':
if feature.location.start < cds_start:
cds_start = feature.location.start
if feature.location.end > cds_end:
cds_end = feature.location.end
refstr = str(ref.seq).upper()
refCDS = refstr[cds_start:cds_end]
refAA = safe_translate(refstr[cds_start:cds_end])
alignment = []
for seq in aln:
seqstr = str(seq.seq).upper()
score, refaln, seqaln = align_pairwise(refstr, seqstr)
if score < 0: # did not align
continue
ref_aln_array = np.array(list(refaln))
seq_aln_array = np.array(list(seqaln))
# stip gaps
ungapped = ref_aln_array != '-'
ref_aln_array_ungapped = ref_aln_array[ungapped]
seq_aln_array_ungapped = seq_aln_array[ungapped]
ref = SeqIO.read(refname, 'genbank')
features = load_features(refname)
clade_designations = read_in_clade_definitions(
f"config/clades_{args.lineage}_ha.tsv")
# get sequence as string, CDS seq, amino acid sequence, and start/end pos
refstr, refCDS, refAA, cds_start, cds_end = get_cds(ref)
alignment = []
for seq in seqs:
seq_container = tmpNode()
seq_aln = codon_align(seq, refstr, refAA, cds_start, cds_end)
if seq_aln is None:
print(f"{seq.id}\tnot translatable", file=sys.stdout)
continue
seq_container.sequences['nuc'] = {i: c for i, c in enumerate(seq_aln)}
for fname, feat in features.items():
if feat.type != 'source':
seq_container.sequences[fname] = {
i: c
for i, c in enumerate(safe_translate(feat.extract(
seq_aln)))
}
matches = []
for clade_name, clade_alleles in clade_designations.items():
if is_node_in_clade(clade_alleles, seq_container, ref):
matches.append(clade_name)
print(f"{seq.description}\t{', '.join(matches)}", file=sys.stdout)
tmp_str = "".join(sample('ABCDEFGHILKLMOPQRSTUVWXYZ', 20))
ref = SeqIO.read(args.reference_sequence, 'genbank')
# get sequence as string, CDS seq, amino acid sequence, and start/end pos
features_to_translate = load_features(args.reference_sequence, args.genes)
refstr, refCDS, refAA, cds_start, cds_end = get_cds(ref)
alignment = []
for seq in sequences:
seq_aln = codon_align(seq, refstr, refAA, cds_start, cds_end)
if seq_aln:
seq.seq=Seq.Seq(seq_aln)
alignment.append(seq)
print("selected %d for region %s and date interval %f-%f"%(len(sequences), region, time_interval[0], time_interval[1]))
for gene, fname in zip(args.genes, args.output):
if gene not in features_to_translate:
continue
seqs = []
feature = features_to_translate[gene]
for seq in alignment:
try:
translation = SeqRecord.SeqRecord(seq=Seq.Seq(safe_translate(str(feature.extract(seq.seq)))),
id=seq.name, name=seq.name, description='')
seqs.append(translation)
except:
print("WARN:",seq.name,"did not translate")
SeqIO.write(seqs, fname, 'fasta')
for seq in alignment:
if seq.id==ref.id:
continue
if len(seq.seq)!=len(ref.seq):
import ipdb; ipdb.set_trace()
print(f"ERROR: this file doesn't seem aligned to the reference. {seq.id} as length {len(seq.seq)} while the reference has length {len(ref.seq)}.")
sys.exit(1)
# read sequence and all its annotated features
seq_container = tmpNode()
seq_str = str(seq.seq)
seq_container.sequences['nuc'] = {i:c for i,c in enumerate(seq_str)}
for fname, feat in features.items():
if feat.type != 'source':
seq_container.sequences[fname] = {i:c for i,c in enumerate(safe_translate(feat.extract(seq_str)))}
# for each clade, check whether it matches any of the clade definitions in the tsv
matches = []
for clade_name, clade_alleles in clade_designations.items():
if is_node_in_clade(clade_alleles, seq_container, ref):
matches.append(clade_name)
# print the last match as clade assignment and all others as ancestral clades
# note that this assumes that clades in the tsv are ordered by order of appearence.
# furthermore, this will only work if parent clades don't have definitions that exclude
# child clades, i.e. positions can only be additive for this to work.
if matches:
print(f"{seq.description}\t{matches[-1]}\t{', '.join(matches[:-1])}", file=output)
else:
