def _findGeneInSeq(self, record):
"""Extract gene sequence from larger sequence (e.g. genomes)
by searching features."""
if not record.features:
# if there aren't any features, just return the record
return record
for feature in record.features:
feature_names = []
if 'gene' in feature.qualifiers.keys():
feature_names.extend(feature.qualifiers['gene'])
if 'gene_synonym' in feature.qualifiers.keys():
feature_names.extend(feature.qualifiers['gene_synonym'])
if 'product' in feature.qualifiers.keys():
feature_names.extend(feature.qualifiers['product'])
gene_names = [e.lower() for e in self.gene_names]
feature_names = [e.lower() for e in feature_names]
if set(gene_names) & set(feature_names):
try:
extractor = SeqFeature(feature.location)
found_seq = extractor.extract(record)
except ValueError:
# catch value errors raised for sequences
# with "fuzzy" positions
# TODO: what are fuzzy positions and can I use
# them?
return record
else:
return found_seq
return record
def t_write_from_recs(self):
"""Write out GFF3 from SeqRecord inputs.
"""
seq = Seq("GATCGATCGATCGATCGATC")
rec = SeqRecord(seq, "ID1")
qualifiers = {"source": "prediction", "score": 10.0, "other": ["Some", "annotations"],
"ID": "gene1"}
sub_qualifiers = {"source": "prediction"}
top_feature = SeqFeature(FeatureLocation(0, 20), type="gene", strand=1,
qualifiers=qualifiers)
top_feature.sub_features = [SeqFeature(FeatureLocation(0, 5), type="exon", strand=1,
qualifiers=sub_qualifiers),
SeqFeature(FeatureLocation(15, 20), type="exon", strand=1,
qualifiers=sub_qualifiers)]
rec.features = [top_feature]
out_handle = StringIO.StringIO()
GFF.write([rec], out_handle)
wrote_info = out_handle.getvalue().split("\n")
assert wrote_info[0] == "##gff-version 3"
assert wrote_info[1] == "##sequence-region ID1 1 20"
assert wrote_info[2].split("\t") == ['ID1', 'prediction', 'gene', '1',
'20', '10.0', '+', '.',
'other=Some,annotations;ID=gene1']
assert wrote_info[3].split("\t") == ['ID1', 'prediction', 'exon', '1', '5',
'.', '+', '.', 'Parent=gene1']
def mergeRecords(file): #adapted from SeqHandler by NF Alikhan (github.com/happykhan/seqhandler)
#SeqHandler is a script for merging, converting and splitting sequence files (Genbank, EMBL, fasta and others). Please use it to merge multi-Genbank files before running bwast.py
filetype = determineFileType(file) #determine file type
readInMultifasta = open(file, "r")
records = list(SeqIO.parse(readInMultifasta, filetype))
mergingFile = records[0]
from Bio.SeqFeature import SeqFeature, FeatureLocation
contigs = SeqFeature(FeatureLocation(0, len(mergingFile) ), type="fasta_record",\
strand=1)
contigs.qualifiers["note"] = records[0].name #pull out contig number of first contig
mergingFile.features.append(contigs) #append first contig to mergingFile
for nextRecord in records[1:]:
contigs = SeqFeature(FeatureLocation(len(mergingFile), len(mergingFile) + len(nextRecord)), type="fasta_record",\
strand=1)
contigs.qualifiers["note"] = nextRecord.name
mergingFile.features.append(contigs) #append subsequent contigs to mergingFile
mergingFile += nextRecord
mergingFile.name = records[0].name
mergingFile.description = records[0].description
mergingFile.annotations = records[0].annotations
for feature in mergingFile.features:
if feature.type == 'source':
mergingFile.features.remove(feature)
contigs = SeqFeature(FeatureLocation(0, len(mergingFile)), type="source", strand=1)
mergingFile.features.insert(0,contigs)
merged_file = re.sub(r"\.\w+$", r".merged.fa", file)
out_handle = open(merged_file, "w")
SeqIO.write(mergingFile, out_handle, filetype)
return merged_file
def make_protein_feature(feature_name, feature_start, feature_end, feature_type):
''' Returns sequence feature, using start, end, name and type as input
'''
feature = SeqFeature(FeatureLocation(int(feature_start), int(feature_end)), type=feature_type)
if feature_type == "Region":
feature.qualifiers = {'name': [feature_name]}
return feature
def test_translation_checks_cds(self):
"""Test that a CDS feature is subject to respective checks."""
seq = Seq.Seq("GGTTACACTTACCGATAATGTCTCTGATGA", generic_dna)
f = SeqFeature(FeatureLocation(0, 30), type="CDS")
f.qualifiers['transl_table'] = [11]
with self.assertRaises(TranslationError):
f.translate(seq)
def mergeMod(args):
filetype = args.inFormat
# Load file as SeqRecord
int_handle = open(args.input, "r")
recs = list(SeqIO.parse(int_handle, filetype))
# For each SeqRecord, I.e. complete gbk annotation obj in file
fgbk = recs[0]
from Bio.SeqFeature import SeqFeature, FeatureLocation
d = SeqFeature(FeatureLocation(0, len(fgbk)), type="fasta_record", strand=1)
d.qualifiers["note"] = recs[0].name
fgbk.features.append(d)
for l in recs[1:]:
d = SeqFeature(FeatureLocation(len(fgbk), len(fgbk) + len(l)), type="fasta_record", strand=1)
d.qualifiers["note"] = l.name
fgbk.features.append(d)
fgbk += l
fgbk.name = recs[0].name
fgbk.description = recs[0].description
fgbk.annotations = recs[0].annotations
if args.accession != None:
fgbk.name = args.accession
if args.ver != None:
fgbk.id = fgbk.name + "." + args.ver
for f in fgbk.features:
if f.type == "source":
fgbk.features.remove(f)
d = SeqFeature(FeatureLocation(0, len(fgbk)), type="source", strand=1)
fgbk.features.insert(0, d)
outtype = filetype
if args.outFormat != None:
outtype = args.outFormat
out_handle = open(args.output, "w")
SeqIO.write(fgbk, out_handle, outtype)
def get_longest(seq_record, gene2isoforms): l = [] c = 0; chrom = adjust_name(seq_record.name); for gene, isoforms in gene2isoforms.iteritems(): longest = max(isoforms, key = lambda i: sum([len(x) for x in i])) if(args.format == 'bed'): compound_to_bed(longest, chrom, gene) elif(args.format == 'fasta'): if(len(longest) > 1): location = CompoundLocation(longest, operator = "join") else: location = longest[0]; feature = SeqFeature(location=location, type='utr', strand = longest[0].strand) #print longest[0].strand f = feature.extract(seq_record) f.name = gene f.id = gene f.description = gene l.append(f); return l;
def seqBlastToFeatures(self, blastDB, blastExe, seqFile, blastType = "blastn",scoreMin = 1e-3, logFile = None):
'''
Blast sequence file against blast database
parse files into records.
This function may not work as well on very large blast comparisons because
it does a full read of the result for the conversion to features.
'''
print ">blast %s %s %s %s" % (self.blastDB, self.blastExe, seqFile, blastType)
blastRecords = self.seqBlast(seqFile, blastType = "blastn", scoreMin = 1e-3, logFile = None)
result = []
index = 0
for r in blastRecords:
recordFeatures = []
for alignment in r.alignments:
name = alignment.title
query = r.query
for hsp in alignment.hsps:
if hsp.expect < scoreMin:
(ts,ss) = hsp.frame
strand = ss
start = hsp.sbjct_start
end = hsp.sbjct_end
location = FeatureLocation(start,end)
feature = SeqFeature(id=query,location=location,strand=strand)
aMatch = hsp.query + "\n" + hsp.match + "\n" + hsp.sbjct
feature.qualifiers["query"] = hsp.query
feature.qualifiers["subject"] = hsp.sbjct
feature.qualifiers["alignment"] = aMatch
recordFeatures.append(feature)
result.append(recordFeatures)
index = index + 1
return result
def annotate_dna_reference(ref, protein):
'''Annotate DNA reference with the protein secondary structures'''
from Bio.SeqFeature import SeqFeature, FeatureLocation
annotation_table = parse_secondary_structure(protein)
if protein == 'gagpol':
start_protein = ref.annotation['gag'].location.nofuzzy_start
elif protein == 'vpu':
# Uniprot starts one aa downstream of us
start_protein = ref.annotation[protein].location.nofuzzy_start + 3
else:
start_protein = ref.annotation[protein].location.nofuzzy_start
features = []
for _, datum in annotation_table.iterrows():
start_dna = datum['start'] * 3 + start_protein
end_dna = datum['end'] * 3 + start_protein
# Notice ribosomal slippage site
if protein == 'gagpol':
if start_dna >= slippage_site:
start_dna -= 1
if end_dna >= slippage_site:
end_dna -= 1
anno = SeqFeature(FeatureLocation(start_dna, end_dna), strand=+1)
anno.type = datum['feature']
features.append(anno)
return features
def parse(self):
with open(self._file) as handle:
genbank = SeqRecord(Seq.UnknownSeq(0))
header_pattern = re.compile(r"ref\|(?P<id>.*?)\|:(?P<start>[0-9]+)-(?P<end>[0-9]+)\|(?P<description>.*?)\|\s*\[gene=(?P<gene>\S+)\]\s*\[locus_tag=(?P<locus_tag>\S+)\]\s*")
first = True
for record in SeqIO.parse(handle, "fasta"):
header = record.description
match = header_pattern.match(header)
if not match:
self.errors.append("Invalid header: >" + header)
continue
if first:
first = False
genbank.id = match.group("id")
genbank.name = match.group("id")
feature = SeqFeature(FeatureLocation(int(match.group("start")), int(match.group("end"))), type = "gene")
feature.qualifiers = {"locus_tag": match.group("locus_tag"),
"gene": match.group("gene"),
"note": match.group("description"),
"sequence": record.seq}
genbank.features.append(feature)
return genbank
return None
def to_seqfeature(self):
"""Create a SeqFeature from the ProteinDomain Object."""
feat = SeqFeature(location=FeatureLocation(self.start, self.end),
id=self.value)
if hasattr(self, 'confidence'):
feat.qualifiers['confidence'] = self.confidence
return feat
def test_deletion__overlapping_features(self):
# Example based on intersection of nei and arbB genes in MG1655.
before_overlap = 'GCCCTGGCTGCCAGCA'
overlap = 'CTAG'
after_overlap = 'GCCGACCGCTTCGG'
raw_seq_str = before_overlap + overlap + after_overlap
seq = Seq(raw_seq_str, generic_dna)
seq_record = SeqRecord(seq)
feature_1_loc = FeatureLocation(0,
len(before_overlap) + len(overlap), strand=1)
feature_1 = SeqFeature(feature_1_loc, type='CDS', id='1')
seq_record.features.append(feature_1)
feature_2_loc = FeatureLocation(len(before_overlap), len(raw_seq_str),
strand=1)
feature_2 = SeqFeature(feature_2_loc, type='CDS', id='2')
seq_record.features.append(feature_2)
maker = VCFToGenbankMaker(seq_record, None, None)
maker._update_genome_record_for_variant(
len(before_overlap), overlap, '')
# Assert the sequence is correct.
EXPECTED_SEQ = before_overlap + after_overlap
self.assertEqual(EXPECTED_SEQ, str(seq_record.seq))
# Assert the feature annotations are still correct.
EXPECTED_FEATURE_1_SEQ = before_overlap
self.assertEqual(EXPECTED_FEATURE_1_SEQ,
str(feature_1.extract(seq_record.seq)))
EXPECTED_FEATURE_2_SEQ = after_overlap
self.assertEqual(EXPECTED_FEATURE_2_SEQ,
str(feature_2.extract(seq_record.seq)))
def create_gene_feature(gene_name, feature_location, feature_qualifiers):
"""Creates a minimal SeqFeature to represent a gene.
"""
gene_feature = SeqFeature(feature_location, type='gene')
gene_feature.qualifiers = {'gene': [gene_name]}
gene_feature.qualifiers = dict(gene_feature.qualifiers.items() +
feature_qualifiers.items())
return gene_feature
def _get_feature(self, feature_dict):
"""Retrieve a Biopython feature from our dictionary representation.
"""
location = FeatureLocation(*feature_dict['location'])
new_feature = SeqFeature(location, feature_dict['type'],
id=feature_dict['id'], strand=feature_dict['strand'])
new_feature.qualifiers = feature_dict['quals']
return new_feature
def get_gene_and_201bp_upstream(genefeature,genomeseq):
mystart = genefeature.location.start
myend = genefeature.location.end
mystrand = genefeature.location.strand
if mystrand == 1:
newfeature = SeqFeature(FeatureLocation(mystart-201,myend),strand=mystrand)
elif mystrand == -1:
newfeature = SeqFeature(FeatureLocation(mystart,myend+201),strand=mystrand)
return newfeature.extract(genomeseq)
def makeSeqObjectsForTblastnNeighbors(tblastn_id, clusterrunid, cur, N=200000):
"""
Given a tBBLSATn ID and a dictionary from sanitized contig IDs (which is what will be
present in the TBLASTN id) to non-sanitized IDs (which are what is in the database),
returns a list of seq objects INCLUDING the TBLASTN hit itself (so that we can show that
on the region drawing).
We pick an N large enough to get at least one gene and then pick the closest one and get
all of its neighbors with a call to makeSeqFeaturesForGeneNeighbors() and just tack the TBLASTN
onto it.
"""
# Lets first get the contig and start/stop locations (which tell us teh strand) out of
# the TBLASTN id. This returns a ValueError if it fails which the calling function can catch if needed.
sanitizedToNot = getSanitizedContigList(cur)
contig, start, stop = splitTblastn(tblastn_id)
if contig in sanitizedToNot:
contig = sanitizedToNot[contig]
start = int(start)
stop = int(stop)
# Create a seq object for the TBLASTN hit itself
if start < stop:
strand = +1
else:
strand = -1
tblastn_feature = SeqFeature(FeatureLocation(start, stop), strand=strand, id=tblastn_id)
tblastn_feature.qualifiers["cluster_id"] = -1
# Find the neighboring genes.
neighboring_genes = getGenesInRegion(contig, start - N, stop + N, cur)
if len(neighboring_genes) == 0:
sys.stderr.write(
"WARNING: No neighboring genes found for TBLASTN hit %s within %d nucleotides in contig %s\n"
% (tblastn_id, N, contig)
)
return [tblastn_feature]
else:
neighboring_geneinfo = getGeneInfo(neighboring_genes, cur)
# Find the closest gene to ours and get the clusters for those neighbors based on the specific clusterrunid
minlen = N
mingene = None
minstrand = None
for geneinfo in neighboring_geneinfo:
genestart = int(geneinfo[5])
geneend = int(geneinfo[6])
distance = min(abs(genestart - start), abs(geneend - start), abs(genestart - stop), abs(geneend - stop))
if distance < minlen:
mingene = geneinfo[0]
minlen = distance
neighboring_features = makeSeqFeaturesForGeneNeighbors(mingene, clusterrunid, cur)
# Add the TBLASTN itself and return it.
neighboring_features.append(tblastn_feature)
return neighboring_features
def find_cds ():
seq_des = str(record_dict[keys].description).split("|")
for i in seq_des:
if re.match("CDS", i):
feature, cds_start, cds_end = re.split(":|-", i)
cds_feature = SeqFeature(FeatureLocation(int(cds_start)-1,int(cds_end)-1),
type=str(feature))
cds_sequence = cds_feature.extract(record_dict[keys].seq)
print cds_sequence.translate()
return cds_start, cds_end, cds_sequence
def make_seq_feature(start, end, ftype, quals={}):
'''
create a sequence feature from a start, end, and a type. additionally you
may include other fields, like note, label, evidence, citation, as a dict.
'''
seq_feature = SeqFeature(FeatureLocation(start, end), strand= +1, type=ftype)
seq_feature.qualifiers = quals
seq_feature.qualifiers['source'] = ['splicemod']
return seq_feature
def test_update_genome_record_for_variant__overlapping_features(self):
"""Tests handling a record that lands in a region of overlapping
features.
"""
# Example based on intersection of nei and arbB genes in MG1655.
before_overlap = 'GCCCTGGCTGCCAGCA'
overlap = 'CTAG'
after_overlap = 'GCCGACCGCTTCGG'
raw_seq_str = before_overlap + overlap + after_overlap
seq = Seq(raw_seq_str, generic_dna)
seq_record = SeqRecord(seq)
feature_1_loc = FeatureLocation(0,
len(before_overlap) + len(overlap), strand=1)
feature_1 = SeqFeature(feature_1_loc, type='CDS', id='1')
seq_record.features.append(feature_1)
feature_2_loc = FeatureLocation(len(before_overlap), len(raw_seq_str),
strand=1)
feature_2 = SeqFeature(feature_2_loc, type='CDS', id='2')
seq_record.features.append(feature_2)
maker = VCFToGenbankMaker(seq_record, None, None)
overlap_replacement = 'TTAA'
maker._update_genome_record_for_variant(len(before_overlap), overlap,
overlap_replacement)
# Features changed, so requery them.
feature_1 = None
feature_2 = None
for feature in seq_record.features:
if feature.id == '1':
feature_1 = feature
elif feature.id == '2':
feature_2 = feature
assert feature_1
assert feature_2
# Assert the sequence is correct.
EXPECTED_SEQ = before_overlap + overlap_replacement + after_overlap
self.assertEqual(EXPECTED_SEQ, str(seq_record.seq))
# Feature added to represent swap.
# self.assertEqual(3, len(seq_record.features))
# Assert the feature annotations are still correct.
EXPECTED_FEATURE_1_SEQ = before_overlap + overlap_replacement
self.assertEqual(EXPECTED_FEATURE_1_SEQ,
str(feature_1.extract(seq_record.seq)))
EXPECTED_FEATURE_2_SEQ = overlap_replacement + after_overlap
self.assertEqual(EXPECTED_FEATURE_2_SEQ,
str(feature_2.extract(seq_record.seq)))
def attach_features(predictions, seqrecord):
for prediction in predictions[seqrecord.id]:
if prediction.raw_score >= 1.0:
qualifiers = {}
qualifiers['locus_tag'] = [prediction.cds_id]
feature = SeqFeature(
location=prediction.location,
type='CDS',
strand=prediction.strand,
qualifiers=qualifiers,
)
feature.qualifiers = qualifiers
seqrecord.features.append(feature)
def makeSeqFeature(geneid, cur):
'''
Make a BioPython SeqFeature object for a gene with ITEP ID geneid
'''
geneinfo = getGeneInfo( [ geneid ], cur )
geneinfo = geneinfo[0]
start = int(geneinfo[5])
stop = int(geneinfo[6])
strand = int(geneinfo[8])
feature = SeqFeature(FeatureLocation(start, stop), strand=strand, id=geneid)
# This can be overwritten by other functions but we need a placeholder.
feature.qualifiers["cluster_id"] = -1
return feature
def _add_gff_line(self, rec, gff_parts, parents, children):
"""Add details from a GFF line to the given SeqRecord.
"""
gff_parts = [(None if p == '.' else p) for p in gff_parts]
assert rec.id == gff_parts[0], "ID mismatch: %s %s" % (rec.id,
gff_parts[0])
# collect all of the base qualifiers for this item
quals = collections.defaultdict(list)
if gff_parts[1]:
quals["source"].append(gff_parts[1])
if gff_parts[5]:
quals["score"].append(gff_parts[5])
if gff_parts[7]:
quals["phase"].append(gff_parts[7])
for key, val in [a.split('=') for a in gff_parts[8].split(';')]:
quals[key].extend(val.split(','))
quals = dict(quals)
# if we are describing a location, then we are a feature
if gff_parts[3] and gff_parts[4]:
#if quals.has_key('ID') or quals.has_key('Parent'):
# print gff_parts[1:6], quals
location = FeatureLocation(int(gff_parts[3]) - 1, int(gff_parts[4]))
new_feature = SeqFeature(location, gff_parts[2],
id = quals.get('ID', [''])[0],
strand = self._strand_map[gff_parts[6]])
new_feature.qualifiers = quals
# Handle flat features
if not new_feature.id:
rec.features.append(new_feature)
# features that have parents need to link so we can pick up
# the relationship
elif new_feature.qualifiers.has_key('Parent'):
for parent in new_feature.qualifiers['Parent']:
children[parent].append(new_feature)
# top level features
else:
parents[rec.id].append(new_feature)
# otherwise, associate these annotations with the full record
else:
# add these as a list of annotations, checking not to overwrite
# current values
for key, vals in quals:
if rec.annotations.has_key(key):
try:
rec.annotations[key].extend(vals)
except AttributeError:
rec.annotations[key] = [rec.annotations[key]] + vals
else:
rec.annotations[key] = vals
return rec, parents, children
def make_join_feature(f_list, ftype="misc_feature"):
#NOTE - Does NOT reorder the sub-features (which you may
#want to do for reverse strand features...)
if len(set(f.strand for f in f_list))==1:
strand = f_list[0].strand
else:
strand = None
for f in f_list:
f.type=ftype
f.location_operator="join"
jf = SeqFeature(FeatureLocation(f_list[0].location.start,
f_list[-1].location.end),
type=ftype, strand=strand, location_operator="join")
jf.sub_features = f_list
return jf
def nrpsSmash(dnaSeq):
options = Namespace()
options.outputfoldername = "/tmp/nrpspks_predictions_txt"
options.record_idx = "" # used in NRPSPredictor2.nrpscodepred, check later what to set it to
options.eukaryotic = 0
tstFeature = SeqFeature(FeatureLocation(0, len(dnaSeq)), type="CDS", strand=1)
tstFeature.qualifiers = {'gene':['gene']}
sequenceRecord = SeqRecord(Seq(dnaSeq, IUPAC.unambiguous_dna),
id = "seq_id",
name = "seq_name",
description = "seq_description")
sequenceRecord.features = [tstFeature]
analysis = specific_analysis(sequenceRecord, options)
shutil.rmtree(options.raw_predictions_outputfolder)
return analysis
def retrieveCompositeSequence(seq_record,seqList) :
# true seq
listePosition = list()
for node in seqList :
seq,coord = node.split(":")
start,end = coord.split("..")
listePosition.append(int(float(start)))
listePosition.append(int(float(end)))
start = min(listePosition)
end = max(listePosition)
f = SeqFeature(FeatureLocation(start,end))
seq = f.extract(seq_record)
seqId = seq_record.id+"|"+str(start)+"_"+str(end)
return SeqRecord(seq=seq.seq,id=seqId,description="")
def __repr__(self):
"It prints representing the seqfeature"
toprint = BioSeqFeature.__repr__(self)
toprint = toprint[:-1]
toprint += ", qualifiers=%s " % repr(self.qualifiers)
toprint += ")"
return toprint
def convertReportToFeatures(self,report,conversionMap,startTag,endTag,strandTag,qualTag="gene"):
'''
parse 2d hash into feature list
'''
result = []
for rowName in report.returnRowNames():
start = float(report.getElement(rowName,startTag))
end = float(report.getElement(rowName,endTag))
location = FeatureLocation(start,end)
strand = self._parseConversion(report.getElement(rowName,strandTag),conversionMap)
feature = SeqFeature(id = rowName,location=location,strand=strand)
feature.qualifiers[qualTag] = [rowName]
for colName in [rowName,report.returnColumnNames()]:
if colName in conversionMap.keys():
newName = conversionMap[colName]
value = report[colName]
feature.qualifiers[newName] = [value]
result.append(feature)
return result
def parse_smart_domains(file_name):
in_handle = open(file_name, "rU")
lines = in_handle.readlines()
domains = []
domain_status = False
domain_type = False
is_domain = False
for line in lines:
line = line.rstrip()
if len(line) > 1:
pairs = line.split("=")
is_domain = True
if len(pairs) == 2:
if pairs[0] == "DOMAIN":
domain_name = pairs[1]
elif pairs[0] == "START":
domain_start = int(pairs[1])
elif pairs[0] == "END":
domain_end = int(pairs[1])
elif pairs[0] == "TYPE":
if pairs[1] != "PFAM":
domain_type = True
else:
domain_type = False
elif pairs[0] == "STATUS":
if pairs[1] == "visible|OK":
domain_status = True
#False
else: domain_status = True
else:
is_domain = False
else:
if is_domain & domain_type & domain_status:
d = SeqFeature(FeatureLocation(domain_start, domain_end), type="Region")
d.qualifiers = {'region_name': [domain_name]}
if domain_name != 'low_complexity_region':
domains.append(d)
is_domain = False
domain_type = False
domain_status = False
in_handle.close()
return domains
def main():
genome_record = SeqIO.read(INPUT_GENOME, 'genbank')
with open(SNP_CSV_DATA_FILE) as input_fh:
reader = csv.DictReader(input_fh, SNP_FIELD_NAMES)
reader.next() # Ignore header.
for row in reader:
feature_ref = row['ref']
feature_alt = row['alt'].replace('[', '').replace(']', '')
feature_start = int(row['position']) - 1 # pythonic
feature_end = feature_start + len(feature_alt)
feature_location = FeatureLocation(feature_start, feature_end)
feature = SeqFeature(location=feature_location,
type=REALIGNED_SNP_TYPE,
strand=1)
feature.qualifiers['ref'] = row['ref']
feature.qualifiers['alt'] = row['alt']
genome_record.features.append(feature)
with open(OUTPUT_GENOME, 'w') as output_fh:
SeqIO.write(genome_record, output_fh, 'genbank')
def extract_sequences_one_sample(args):
'''
Function for extracting protein sequences given annotated regions and produce fasta files that can be used for clustering
'''
(fasta_path,annotation_path,sample, output_dir)=args
domainInfo=load_annotation_pfam(annotation_path)
print "Generating domain fasta sequences for "+sample+" ..."
from Bio import SeqIO
from Bio.SeqFeature import SeqFeature, FeatureLocation
from Bio.SeqRecord import SeqRecord
(annot,start,stop,strand,evalue)=domainInfo
record_dict=index_fasta(fasta_path)
recordlist=[]
outfilename=output_dir +'/forClustering/'+ sample +'.fasta'
outhandle=open(outfilename,'w')
for domainID in annot.keys():
for i in range(len(annot[domainID])):
domain=annot[domainID][i]
try:
seq=record_dict[domain]
except KeyError:
print "Error: " + domain + " not in fasta file.\n"
break
a=start[domainID][i]
b=stop[domainID][i]
seq_strand=strand[domainID][i]
seq_evalue=evalue[domainID][i]
if seq_strand in '+':
domain_feature = SeqFeature(FeatureLocation(a-1, b-1), type="domain", strand=1)
elif seq_strand in '-':
domain_feature = SeqFeature(FeatureLocation(a-1, b-1), type="domain", strand=-1)
feature_seq = domain_feature.extract(seq)
feature_seq.id=feature_seq.id+' '+domainID+' '+seq_evalue
recordlist.append(feature_seq)
SeqIO.write(recordlist, outhandle, "fasta")
outhandle.close()
print "Done"
def add_track_with_sigils(self, **kwargs):
"""Add track with sigils."""
self.gdt_features = self.gdd.new_track(1, greytrack=False)
self.gds_features = self.gdt_features.new_set()
for i in range(18):
start = int((400 * i) / 18.0)
end = start + 17
if i % 3 == 0:
strand = None
name = "Strandless"
color = colors.orange
elif i % 3 == 1:
strand = +1
name = "Forward"
color = colors.red
else:
strand = -1
name = "Reverse"
color = colors.blue
feature = SeqFeature(FeatureLocation(start, end), strand=strand)
self.gds_features.add_feature(feature, name=name,
color=color, label=True, **kwargs)
def match_sequence_numbering(self):
""" SequenceData.match_sequence_numbering
Assign canonical sequence numbering to structural fragments
"""
if not hasattr(self, 'has_canonical'):
return False
for ch_id in self.data:
if ch_id not in self.has_canonical or not self.has_canonical[ch_id]:
continue
for mod_id in self.data[ch_id]['pdb']:
frgs = self.data[ch_id]['pdb'][mod_id]['frgs']
self.data[ch_id]['pdb'][mod_id]['match_numbering'] = True
for nfrag in range(0, len(frgs)):
inic = self.data[ch_id]['can'].seq.find(frgs[nfrag].seq) + 1
fin = inic + len(frgs[nfrag].seq) - 1
self.data[ch_id]['pdb'][mod_id]['frgs'][nfrag].features.append(
SeqFeature(FeatureLocation(inic, fin))
)
if inic != frgs[nfrag].features[0].location.start or\
fin != frgs[nfrag].features[0].location.end:
self.data[ch_id]['pdb'][mod_id]['match_numbering'] = False
return True
def add_point_feature(self, resnum, feat_type=None, feat_id=None):
"""Add a feature to the features list describing a single residue.
Args:
resnum (int): Protein sequence residue number
feat_type (str, optional): Optional description of the feature type (ie. 'catalytic residue')
feat_id (str, optional): Optional ID of the feature type (ie. 'TM1')
"""
if self.feature_file:
raise ValueError(
'Feature file associated with sequence, please remove file association to append '
'additional features.')
if not feat_type:
feat_type = 'Manually added protein sequence single residue feature'
newfeat = SeqFeature(location=FeatureLocation(
ExactPosition(resnum - 1), ExactPosition(resnum)),
type=feat_type,
id=feat_id)
self.features.append(newfeat)
def t_write_seqrecord(self):
"""Write single SeqRecords.
"""
seq = Seq("GATCGATCGATCGATCGATC")
rec = SeqRecord(seq, "ID1")
qualifiers = {
"source": "prediction",
"score": 10.0,
"other": ["Some", "annotations"],
"ID": "gene1"
}
rec.features = [
SeqFeature(FeatureLocation(0, 20),
type="gene",
strand=1,
qualifiers=qualifiers)
]
out_handle = StringIO()
GFF.write([rec], out_handle, include_fasta=True)
wrote_info = out_handle.getvalue().split("\n")
gff_line = wrote_info[2]
assert gff_line.split("\t")[0] == "ID1"
def long_sigils(self, glyph):
"""Check feature sigils within bounding box."""
# Add a track of features, bigger height to emphasise any sigil errors
self.gdt_features = self.gdd.new_track(1, greytrack=True, height=3)
# We'll just use one feature set for these features if strand specific
self.gds_features = self.gdt_features.new_set()
if glyph in ["BIGARROW"]:
# These straddle the axis, so don't want to draw them on top of each other
feature = SeqFeature(FeatureLocation(25, 375), strand=None)
self.gds_features.add_feature(feature, color="lightblue")
feature = SeqFeature(FeatureLocation(25, 375), strand=+1)
else:
feature = SeqFeature(FeatureLocation(25, 375), strand=+1)
self.gds_features.add_feature(feature, color="lightblue")
self.gds_features.add_feature(
feature, name="Forward", sigil=glyph, color="blue", arrowhead_length=2.0
)
if glyph in ["BIGARROW"]:
# These straddle the axis, so don't want to draw them on top of each other
self.gdt_features = self.gdd.new_track(1, greytrack=True, height=3)
self.gds_features = self.gdt_features.new_set()
feature = SeqFeature(FeatureLocation(25, 375), strand=None)
self.gds_features.add_feature(feature, color="pink")
feature = SeqFeature(FeatureLocation(25, 375), strand=-1)
else:
feature = SeqFeature(FeatureLocation(25, 375), strand=-1)
self.gds_features.add_feature(feature, color="pink")
self.gds_features.add_feature(
feature, name="Reverse", sigil=glyph, color="red", arrowhead_length=2.0
)
# Add another track of features, bigger height to emphasise any sigil errors
self.gdt_features = self.gdd.new_track(1, greytrack=True, height=3)
# We'll just use one feature set for these features,
self.gds_features = self.gdt_features.new_set()
feature = SeqFeature(FeatureLocation(25, 375), strand=None)
self.gds_features.add_feature(feature, color="lightgreen")
self.gds_features.add_feature(
feature, name="Standless", sigil=glyph, color="green", arrowhead_length=2.0
)
self.finish(f"GD_sigil_long_{glyph}")
def check_sub(feature, sequence):
new_features = []
loc_list = []
qual_list = {}
topop = []
for sub in feature.sub_features:
if sub.sub_features: # If there are sub_features, go deeper
new_features.extend(check_sub(sub, sequence))
elif sub.type == 'CDS':
loc = [sub.location.start.real, sub.location.end.real]
loc_list.append(FeatureLocation(loc[0], loc[1], strand=sub.strand))
# For split features (CDSs), the final feature will have the same qualifiers as the children ONLY if
# they're the same, i.e.: all children have the same "protein_ID" (key and value).
for qual in sub.qualifiers.keys():
if qual not in qual_list:
qual_list[qual] = sub.qualifiers[qual]
if qual in qual_list and not qual_list[qual] == sub.qualifiers[
qual]:
topop.append(qual)
for n in topop: # Pop mismatching qualifers over split features
qual_list.pop(n, None)
qual_list.pop('Parent', None) # Pop parent.
# Only works in tip of the tree, when there's no new_feature built yet. If there is,
# it means the script just came out of a check_sub and it's ready to return.
if not new_features:
if len(loc_list) > 1:
loc_list = sorted(loc_list, key=lambda x: x.start.real)
if loc_list[0].strand == 1:
new_loc = CompoundLocation(loc_list)
else:
new_loc = CompoundLocation(list(reversed(loc_list)))
elif len(loc_list) == 0:
return new_features
else:
new_loc = loc_list[0]
new_feature = SeqFeature(new_loc)
new_feature.qualifiers = qual_list
new_feature.type = 'CDS'
trans = new_feature.extract(sequence.seq).translate(stop_symbol='')
new_feature.qualifiers['translation'] = [str(trans)]
new_features.append(new_feature)
return new_features
def setUp(self):
random.seed(1)
self.sf1 = SeqFeature(FeatureLocation(ExactPosition(100),
ExactPosition(500),
strand=-1),
type='exon',
id='exon_1')
self.sf2 = SeqFeature(FeatureLocation(ExactPosition(800),
ExactPosition(1000),
strand=-1),
type='exon',
id='exon_2')
self.sf3 = SeqFeature(FeatureLocation(ExactPosition(1500),
ExactPosition(2000),
strand=-1),
type='exon',
id='exon_3')
self.sf4 = SeqFeature(FeatureLocation(ExactPosition(10000),
ExactPosition(15000),
strand=-1),
type='exon',
id='exon_4')
self.sf5 = SeqFeature(FeatureLocation(ExactPosition(17000),
ExactPosition(20000),
strand=-1),
type='exon',
id='exon_5')
self.sf6 = SeqFeature(FeatureLocation(ExactPosition(22000),
ExactPosition(25000),
strand=-1),
type='exon',
id='exon_6')
self.exon1 = FusionExon(self.sf1)
self.exon2 = FusionExon(self.sf2)
self.exon3 = FusionExon(self.sf3)
self.exon4 = FusionExon(self.sf4)
self.exon5 = FusionExon(self.sf5)
self.exon6 = FusionExon(self.sf6)
self.sfs1 = [self.sf1, self.sf2, self.sf3]
self.sfs2 = [self.sf4, self.sf5, self.sf6]
self.exons1 = [self.exon1, self.exon2, self.exon3]
self.exons2 = [self.exon4, self.exon5, self.exon6]
self.FE1 = FusionEvent(self.sfs1, self.sfs2, "+", "-")
self.FE2 = FusionEvent(self.sfs1, self.sfs2, "-", "+")
def mergeMod(args):
filetype = args.inFormat
# Load file as SeqRecord
int_handle = open(args.input, "r")
recs = list(SeqIO.parse(int_handle, filetype))
# For each SeqRecord, I.e. complete gbk annotation obj in file
fgbk = recs[0]
from Bio.SeqFeature import SeqFeature, FeatureLocation
d = SeqFeature(FeatureLocation(0, len(fgbk) ), type="fasta_record",\
strand=1)
d.qualifiers["note"] = recs[0].name
fgbk.features.append(d)
for l in recs[1:]:
d = SeqFeature(FeatureLocation(len(fgbk), len(fgbk) + len(l)), type="fasta_record",\
strand=1)
d.qualifiers["note"] = l.name
fgbk.features.append(d)
fgbk += l
fgbk.name = recs[0].name
fgbk.description = recs[0].description
fgbk.annotations = recs[0].annotations
if args.accession != None:
fgbk.name = args.accession
if args.ver != None:
fgbk.id = fgbk.name +'.' + args.ver
for f in fgbk.features:
if f.type == 'source':
fgbk.features.remove(f)
d = SeqFeature(FeatureLocation(0, len(fgbk)), type="source", strand=1)
fgbk.features.insert(0,d)
outtype = filetype
if args.outFormat != None:
outtype = args.outFormat
out_handle = open( args.output,"w")
SeqIO.write(fgbk, out_handle, outtype)
def test_translate(self):
s = SeqRecord(
Seq("ATGGTGTAA"),
id="TestID",
name="TestName",
description="TestDescription",
dbxrefs=["TestDbxrefs"],
features=[SeqFeature(FeatureLocation(0, 3), type="Site")],
annotations={"organism": "bombyx"},
letter_annotations={"test": "abcdefghi"},
)
t = s.translate()
self.assertEqual(t.seq, "MV*")
self.assertEqual(t.id, "<unknown id>")
self.assertEqual(t.name, "<unknown name>")
self.assertEqual(t.description, "<unknown description>")
self.assertFalse(t.dbxrefs)
self.assertFalse(t.features)
self.assertFalse(t.annotations)
self.assertFalse(t.letter_annotations)
t = s.translate(
cds=True,
id=True,
name=True,
description=True,
dbxrefs=True,
annotations=True,
)
self.assertEqual(t.seq, "MV")
self.assertEqual(t.id, "TestID")
self.assertEqual(t.name, "TestName")
self.assertEqual(t.description, "TestDescription")
self.assertEqual(t.dbxrefs, ["TestDbxrefs"])
self.assertFalse(t.features)
self.assertEqual(t.annotations, {"organism": "bombyx"})
self.assertFalse(t.letter_annotations)
def execute(self, params: dict):
records: List[SeqRecord] = []
assembly_list: AssemblyResultSet = params[0]
for assembly_idx, assembly in enumerate(assembly_list):
features = []
part_start_pos = 0
assembly_sequence = assembly.get_sequence()
for part in assembly.get_parts():
#assembly_sequence += str(part.source_part.sequence.seq)
# Add annotation
feature = SeqFeature(
id=part.identifier,
qualifiers={'name': part.identifier},
#location=FeatureLocation(
# start=part_start_pos,
# end=new_start_pos
#),
type='part')
features.append(feature)
record = SeqRecord(
assembly_sequence.seq,
id='123456789', # random accession number
name='Example',
description='An example GenBank file generated by SuperGSL')
for feature in features:
record.features.append(feature)
records.append(record)
output_file = open('example.gb', 'w')
SeqIO.write(records, output_file, 'genbank')
def _easy_seqrec(
str_seq: str,
id,
annotation_type: str = "misc_feature",
start=0,
end=None,
**qualifiers: list,
) -> SeqRecord:
"""Return an annotated SeqRecord from a string and id.
Args:
str_seq: sequence of SeqRecord.
id : Identifier for new part.
annotation_type (optional): Equivalent to Bio.SeqFeature type e.g. CDS,
Defaults to "misc_feature".
start (optional): start of the annotation, Defaults to 0.
end (optional): end of the annotation, if None defaults to len(str_seq),
Defaults to 'None'.
**qualifiers: equivalent to Bio.SeqFeature.qualifiers for annotation e.g. standard_name=["LMP"].
Return:
SeqRecord: An annotated SeqRecord.
"""
if not end:
end = len(str_seq)
seqrec = SeqRecord(
Seq(str_seq),
id=id,
features=[
SeqFeature(
type=annotation_type,
location=FeatureLocation(start=start, end=end, strand=+1),
qualifiers={item[0]: item[1]
for item in qualifiers.items()},
)
],
)
return seqrec
def to_record(self, record=None, record_id=None):
"""Return a Biopython seqrecord of the quote.
>>> record = to_record(solution)
>>> # Let's plot with DnaVu:
>>> from dnavu import create_record_plot
>>> from bokeh.io import output_file, show
>>> output_file("view.html")
>>> plot = create_record_plot(record)
>>> show(plot)
"""
if record_id is None:
record_id = self.id
if record is None:
if has_dna_alphabet: # Biopython <1.78
record = SeqRecord(Seq(self.sequence, DNAAlphabet()),
id=record_id)
else:
record = SeqRecord(Seq(self.sequence), id=record_id)
record.annotations["molecule_type"] = "DNA"
else:
record = deepcopy(record)
if self.assembly_plan is not None:
features = [
SeqFeature(
FeatureLocation(segment[0], segment[1], 1),
type="Feature",
qualifiers={
"name": quote.id,
"source": quote.source,
"price": quote.price,
"lead_time": quote.lead_time,
},
) for segment, quote in self.assembly_plan.items()
]
record.features = features + record.features
return record
def test_replacer__TGA_codon_mid_seq(self):
"""Tests that TGA codon mis-sequence is not replaced,
as it codes for Selenocysteine.
"""
CODONS_TO_REMOVE = ['TGA']
# Simple table for testing.
AA_TO_CODON_LIST_DICT = {
'*': {'TGA': {}, 'TAG': {}},
}
CODON_USAGE_MEMEX = CodonUsageMemex(AA_TO_CODON_LIST_DICT)
feature_1_seq = 'TTG' + 'GCT' + 'TGA' + 'TTG' + 'TGA'
whole_seq = feature_1_seq
seq = Seq(whole_seq, generic_dna)
seq_record = SeqRecord(seq)
feature_1_loc = FeatureLocation(0, len(feature_1_seq), strand=1)
feature_1 = SeqFeature(feature_1_loc, type='CDS', id='1')
seq_record.features.append(feature_1)
codon_replacer = GraphSearchCodonReplacer(
CODONS_TO_REMOVE, CODON_USAGE_MEMEX)
# Perform replacement.
replace_result = codon_replacer.replace_codons_in_feature(
feature_1.id, seq_record)
# Assert successful fix.
self.assertTrue(replace_result['is_success'])
# Assert the new sequence has only the TTG that is not a start codon
# removed.
EXPECTED_NEW_FEATURE_SEQUENCE = 'TTG' + 'GCT' + 'TGA' + 'TTG' + 'TAG'
self.assertEqual(
EXPECTED_NEW_FEATURE_SEQUENCE,
str(replace_result['new_feature_seq']))
def gb_create(sequence, nb_strain, inp, path, name_directory):
# initialization of the DNA sequence
DNA_seq = ''
# loop in sequence list to generate the string corresponding to the DNA sequence of the device.
for seq in sequence:
DNA_seq += seq[1]
# creation of the formated DNA sequence for the genbank file
seq_final = Seq(DNA_seq, IUPAC.unambiguous_dna)
record = SeqRecord(
seq_final,
id='NA', # random accession number
name='Strain' + nb_strain + '_' + inp,
description='Sequence of the computational device in the strain' +
nb_strain + ' to implement the Boolean function required')
# initialization of the variable len_seq
len_seq = 0
# loop in sequence list to generate the genbank feature of the sequence
for feat in sequence:
feature = SeqFeature(FeatureLocation(start=len_seq,
end=len_seq + len(feat[1])),
id=feat[0],
type=feat[0],
strand=feat[2])
record.features.append(feature)
len_seq += len(feat[1])
# Save as GenBank file
output_file = open(
path + '/' + name_directory + '_Strain' + nb_strain + '.gb', 'w')
SeqIO.write(record, output_file, 'genbank')
output_file.close()
def bed2SeqFeature( bedlist,window,expcount,cnvlimit ):
"""Generate feature data."""
gdata = []
for s,e,c in bedlist:
#assume not reads and dels
log2 = float('-inf')
#get log2 only if any reads
if c:
#get exp count
expcountlocal = expcount
if e-s < window:
#if region shorter than window, normalize expcount accordingly
expcountlocal = 1.0 * (e-s) / window * expcount
log2 = log( c / expcountlocal,2 )
#store dels and dups
sf = SeqFeature(FeatureLocation(s,e))
color = 0
'''
if log2 <= -cnvlimit:
color = (0,0,_get_color(-log2))
elif log2 >= cnvlimit:
color = (_get_color(log2),0,0) #rgb
'''
if log2 <= -3*cnvlimit:
color = colors.darkblue
elif log2 <= -2*cnvlimit:
color = colors.blue
elif log2 <= -1*cnvlimit:
color = colors.lightblue
elif log2 >= 3*cnvlimit:
color = colors.darkred
elif log2 >= 2*cnvlimit:
color = colors.red
elif log2 >= 1*cnvlimit:
color = colors.lightsalmon #'''
if color:
gdata.append((sf,color))
return gdata
def plan_step_to_record(plan_step, record=None, record_id=None):
"""Return a Biopython seqrecord of the quote.
>>> record = to_SeqRecord(solution)
>>> # Let's plot with DnaVu:
>>> from dnavu import create_record_plot
>>> from bokeh.io import output_file, show
>>> output_file("view.html")
>>> plot = create_record_plot(record)
>>> show(plot)
"""
if record_id is None:
record_id = plan_step.id
if record is None:
if has_dna_alphabet: # Biopython <1.78
record = SeqRecord(Seq(plan_step.sequence, DNAAlphabet()),
id=record_id)
else:
record = SeqRecord(Seq(plan_step.sequence), id=record_id)
record.annotations["molecule_type"] = "DNA"
else:
record = deepcopy(record)
if plan_step.assembly_plan is not None:
features = [
SeqFeature(
FeatureLocation(q.segment_start, q.segment_end, 1),
type="misc_feature",
qualifiers={
"label": "%s - From %s" % (q.id, q.source),
"name": q.id,
"source": q.source,
"price": q.price,
"lead_time": q.lead_time,
},
) for q in plan_step.assembly_plan
]
record.features = features + record.features
return record
def locations_to_biopython_features(
self,
feature_type="misc_feature",
color="red",
label_prefix="",
merge_overlapping=False,
):
"""Return a list of locations (of breach/suboptimality) as annotations.
Parameters
----------
feature_type
Genbank type of the annotations
color
Color property attached to the annotations
label_prefix
The locations will be labelled of the form
"prefix NameOfSpecification()"
merge_overlapping
If true, then overlapping locations (0-5, 2-9) will be merged into a
single one (0-9).
"""
locations = self.locations
if merge_overlapping:
locations = Location.merge_overlapping_locations(locations)
return [
SeqFeature(
location.to_biopython_location(),
type=feature_type,
qualifiers=dict(
label=label_prefix + " " + str(self.specification),
color=color,
),
) for location in locations
]
def annotate_record(
seqrecord,
location="full",
feature_type="misc_feature",
margin=0,
**qualifiers
):
"""Add a feature to a Biopython SeqRecord.
Parameters
----------
seqrecord
The biopython seqrecord to be annotated.
location
Either (start, end) or (start, end, strand). (strand defaults to +1)
feature_type
The type associated with the feature
margin
Number of extra bases added on each side of the given location.
qualifiers
Dictionnary that will be the Biopython feature's `qualifiers` attribute.
"""
if location == "full":
location = (margin, len(seqrecord) - margin)
strand = location[2] if len(location) == 3 else 1
seqrecord.features.append(
SeqFeature(
FeatureLocation(location[0], location[1], strand),
qualifiers=qualifiers,
type=feature_type,
)
)
def convert_feature(feature,
qualifier_transformers=DEFAULT_QUALIFIER_TRANSFORMERS):
"""
:param SeqFeature feature:
:param qualifier_transformers: a set of transformation functions used to clean up the qualifiers.
:return: a :class:`SeqFeature` with valid GenBank qualifiers and a feature type that is a Sequence Ontology term.
"""
type_ = convert_feature_type(feature)
before, after = feature.qualifiers, feature.qualifiers
for transformer in qualifier_transformers:
before = after = transformer(before, dict(after))
# finally, remove all qualifiers that do not belong to a certain feature key
qualifiers = remove_qualifiers_inappropriate_for_feature(
before, dict(after), genbank_feature_key(type_))
return SeqFeature(location=feature.location,
type=type_,
id=feature.id,
qualifiers=qualifiers)
def get_genome(inputfile):
with open(inputfile) as f:
lines = f.readlines()
sepline = lines.index("##FASTA\n")
genome_id = lines[sepline+1][1:].strip()
genome_features = []
for i in range(2,sepline):
splitline = lines[i].split()
startloc = int(splitline[3])
endloc = int(splitline[4])
featstrand = stranddict[splitline[6]]
locustag = splitline[8].split('=')[1]
thisfeature = SeqFeature(FeatureLocation(start=startloc, end=endloc, strand = featstrand),
type=splitline[2],
qualifiers = {'locus_tag':[locustag]})
genome_features.append(thisfeature)
genome_seq = ""
for i in range(sepline+2, len(lines)):
genome_seq = genome_seq + lines[i].strip()
return(genome_id, genome_seq, genome_features)
def gb_create(DNA_seq, list_feat, name, directory):
# creation of the formated DNA sequence for the genbank file
seq_final = Seq(DNA_seq, IUPAC.unambiguous_dna)
record = SeqRecord(
seq_final,
id='NA', # random accession number
name='',
description='Synthetic sequence')
# loop in sequence list to generate the genbank feature of the sequence
for feat in list_feat:
feature = SeqFeature(FeatureLocation(start=feat[0], end=feat[1]),
id=feat[2],
type=feat[2],
strand=feat[3])
record.features.append(feature)
# Save as GenBank file
output_file = open(directory + '/' + name + '.gb', 'w')
SeqIO.write(record, output_file, 'genbank')
output_file.close()
def test_find_features_starting_at(self):
before = 'TACTAGTCGAT'
feature_1_seq = 'ATGAAAGGGATG'
after = 'CTATCTAGCTAGCT'
whole_seq = Seq(before + feature_1_seq + after, generic_dna)
seq_record = SeqRecord(whole_seq)
feature_1_loc = FeatureLocation(len(before),
len(before) + len(feature_1_seq),
strand=1)
feature_1 = SeqFeature(feature_1_loc, type='CDS', id='1')
seq_record.features.append(feature_1)
self.assertEquals(set([]),
set(find_features_starting_at(0, seq_record)))
self.assertEquals(
set([feature_1]),
set(find_features_starting_at(feature_1_loc.start, seq_record)))
self.assertEquals(
set(),
set(
find_features_starting_at(feature_1_loc.end, seq_record,
['misc'])))
def bed2SeqFeature(bed1, expcount1, bed2, expcount2, window):
"""Generate feature data:
- mark heterozygous SNP-rich with orange
- mark homozygous SNP-rich with darkgrey
"""
gdata = []
bed1.sort()
bed2.sort()
for (s, e, c1), (s2, e2, c2) in zip(bed1, bed2):
# hapA
color = "darkgrey" #0
#mark heterozygous SNP-rich with orange # hetero
if get_log2(s, e, c2, expcount2, window) > 0:
color = 0 #"orange"
#mark homozygous SNP-rich with darkgrey # hapB
elif get_log2(s, e, c1, expcount1, window) > 0:
color = "orange" #"darkgrey"
#store dels and dups
if color:
sf = SeqFeature(FeatureLocation(s, e))
gdata.append((sf, color))
return gdata
def extract_target(self):
inter_df = self.inter_df
i = 0
for index, row in inter_df.iterrows():
i += 1
print(i)
# if i > 20:
# break
chr_id = row['chr']
chr = self.get_genome_by_id(chr_id)
strand = row['strand']
assert strand == '+' or strand == '-', "strand value incorrect {}".format(
strand)
strand = 1 if strand == '+' else -1
start = row[
'start'] - 1 #Note that the start and end location numbering follow Python's scheme
stop = row['end']
target_feature = SeqFeature(
FeatureLocation(start, stop, strand=strand))
target = target_feature.location.extract(chr)
inter_df.loc[index, 'target'] = str(target.seq)
#print (str(target.seq))
self.inter_df = inter_df.copy()
def _translate_feature(fi, rec, table):
f = rec.features[fi]
srec = f.extract(rec)
try:
tsec = srec.seq.translate(table)
if tsec[-1] == '*': tsec = tsec[:-1]
try:
fid = f.qualifiers['locus_tag'][0]
except KeyError:
fid = '%s_f%d' % (rec.id, fi)
trec = SeqRecord(tsec,
id=fid,
name=rec.name,
description=rec.description,
annotations=rec.annotations)
pf = SeqFeature(FeatureLocation(0, len(trec)),
id=f.id,
type='CDS',
qualifiers=f.qualifiers)
trec.features.append(pf)
except Exception, e:
print e
raise RuntimeError('Unable to translate: %s' % str(srec.seq))
def make_gbk_from_gff(infile, outfile):
g_id, g_seq, g_features = get_genome(infile)
myseq = Seq.Seq(g_seq, IUPAC.unambiguous_dna)
myrecord = SeqRecord(myseq, id = g_id, name = g_id)
f1 = SeqFeature(FeatureLocation(0, len(myseq)), type="source")
myrecord.features.append(f1)
for feature in g_features:
feature.qualifiers['transl_table']=[11]
if feature.location.strand == 1:
aaseq = myseq[feature.location.start-1:feature.location.end-1].translate()
else:
aaseq = myseq[feature.location.start-1:feature.location.end-1].reverse_complement().translate()
feature.qualifiers['translation']=[str(aaseq)]
feature.type="CDS"
feature.qualifiers['product'] = feature.qualifiers['locus_tag']
genefeature = copy.deepcopy(feature)
genefeature.type = 'gene'
genefeature.qualifiers = {'locus_tag':feature.qualifiers['locus_tag']}
myrecord.features.append(genefeature)
myrecord.features.append(feature)
SeqIO.write(myrecord, outfile, 'genbank')
def load_annotation(self, gff):
print("Loading annotation...")
try:
with open(gff, 'r') as g:
for feature in g:
if 'gene' in feature:
scaffold, source, gfftype, start, end, score, strand, phase, attributes = feature.rstrip(
).split('\t')
if scaffold not in self.sequences:
continue
featurestrand = None
if strand == '+':
featurestrand = 1
elif strand == '-':
featurestrand = -1
location = FeatureLocation(ExactPosition(start),
ExactPosition(end),
strand=featurestrand)
feature = SeqFeature(location, type=gfftype)
self.sequences[scaffold].features.append(feature)
except IOError:
print("Can't load annotation from file {}!".format(gff))
sys.exit()
def add_jaggies(contig_seq, offset, gd_contig_features):
"""Add JAGGY features for any run of NNNN or XXXX in sequence."""
if not MIN_GAP_JAGGY:
#Skip the jaggies
return
contig_seq = contig_seq.upper().replace("X", "N")
i = 0
j = 0
NNN = "N" * MIN_GAP_JAGGY
while i < len(contig_seq):
i = contig_seq.find(NNN, i)
if i == -1:
return
j = i
while j < len(contig_seq) and contig_seq[j] == "N":
j += 1
#print("Adding jaggy")
gd_contig_features.add_feature(SeqFeature(
FeatureLocation(offset + i, offset + j)),
sigil="JAGGY",
color=colors.slategrey,
border=colors.black)
i = j + 1
def to_biopython_feature(self,
feature_type="misc_feature",
role="constraint",
colors_dict=None,
**qualifiers):
"""Return a Biopython feature representing the specification.
The feature label is a string representation of the specification,
and its location indicates the specification's scope.
"""
if colors_dict is None:
colors_dict = {"constraint": "#355c87", "objective": "#f9cd60"}
qualifiers["role"] = role
if "label" not in qualifiers:
qualifiers['label'] = self.label(role=role,
with_location=False,
assignment=':')
if "color" not in qualifiers:
qualifiers['color'] = colors_dict[role]
return SeqFeature(self.location.to_biopython_location(),
type=feature_type,
qualifiers=qualifiers)
def organism_iterator(self, organism, seq_map=None):
for dbcontig in Contig.objects(organism=organism).no_cache():
if seq_map:
seq = str(seq_map[dbcontig.name].seq)
else:
seq = dbcontig.seq
contig = SeqRecord(id=dbcontig.name, seq=Seq(seq))
for dbfeature in dbcontig.features:
qualifiers = {"locus_tag": [dbfeature.locus_tag]}
p = list(
Protein.objects(organism=organism,
gene=dbfeature.identifier))
if p:
p = p[0]
qualifiers["description"] = [p.description]
qualifiers["gene_symbol"] = p.gene
qualifiers["Note"] = [p.description]
ecs = [
x.upper() for x in p.ontologies if x.startswith("ec:")
]
gos = [
x.upper() for x in p.ontologies if x.startswith("go:")
]
if ecs:
qualifiers["EC"] = ecs
if gos:
qualifiers["GO"] = gos
feature = SeqFeature(id=dbfeature.identifier,
type=dbfeature.type,
qualifiers=qualifiers,
location=FeatureLocation(
start=dbfeature.location.start,
end=dbfeature.location.end,
strand=dbfeature.location.strand))
contig.features.append(feature)
yield contig