def standardCassette(PromoterName, TerminatorName, orfName, orfSeq):
#first, the promoter
print(
"I'm going to build a standard cassette in which promoter is 600nt, terminator 250nt."
)
print("First, which PROMOTER do you want to use, e.g., TDH3")
PromoterGeneRec = fetchGene(PromoterName)
PromoterRec = fetchNeighbor(PromoterGeneRec, "upstream", 600)
PromoterRec.id = PromoterRec.id + "ps"
#second, the terminator
print("Which TERMINATOR do you want to use, e.g., ADH1")
TerminatorGeneRec = fetchGene(TerminatorName)
TerminatorRec = fetchNeighbor(TerminatorGeneRec, "downstream", 250)
TerminatorRec.id = TerminatorRec.id + "ts"
#and last, the gene
print("What is the name of your gene, e.g., KlGapDH")
print("What's the sequence")
orfRecord = SeqRecord(Seq(orfSeq, SingleLetterAlphabet()), id=orfName)
insertRec = [PromoterRec, orfRecord, TerminatorRec]
return PromoterRec, orfRecord, TerminatorRec
def editExisting(name,
option,
promoter=None,
terminator=None,
NewGeneName="",
NewGeneSeq=""):
OrigGeneRecord = fetchGene(name)
UpHomRec = fetchNeighbor(OrigGeneRecord, "upstream", HomologyLength)
DownHomRec = fetchNeighbor(OrigGeneRecord, "downstream", HomologyLength)
if option == 1:
fragments = [UpHomRec, DownHomRec]
elif option == 2:
InsertRec = SeqRecord(Seq(NewGeneSeq, SingleLetterAlphabet()),
id=NewGeneName)
fragments = [UpHomRec, InsertRec, DownHomRec]
elif option == 3:
PromoterRec, orfRecord, TerminatorRec = standardCassette(
promoter, terminator, NewGeneName, NewGeneSeq)
fragments = [
UpHomRec, PromoterRec, orfRecord, TerminatorRec, DownHomRec
]
elif option == 4:
pass
elif option == 5:
pass
return stitch(fragments)
def create_new_sequence_record(seq_record):
# get new sequence values
new_id = mod_read_id(seq_record)
new_seq = reduce_seq_length(seq_record)
if new_seq is None:
print("Warning: Sequence less than 30 characters")
return None
phred_quality = list(seq_record.letter_annotations["phred_quality"])
phred_quality = phred_quality[0:30]
# Phred score less than 30
for score in phred_quality:
if score < 30:
# print("Warning: PHRED scroe lower than 30 - discarding - ", score)
return None
# construct a new sequence record
new_seq_record = SeqRecord(Seq(str(new_seq), SingleLetterAlphabet()),
id=new_id,
name=seq_record.name,
description=seq_record.description,
dbxrefs=seq_record.dbxrefs,
features=seq_record.features,
annotations=seq_record.annotations)
new_seq_record.letter_annotations["phred_quality"] = phred_quality
return new_seq_record
def _clean_DNA_seq(self, record):
'''
Exchange all X in sequence with N
:param record: Biopython SeqRecord object
:return: Biopython seq object with Ns instead of Xs
'''
return Seq.Seq(re.sub('[^GATC]', 'N', str(record.seq).upper()), SingleLetterAlphabet())
def make_fpa_fasta(genenamestr, genenamelist):
PSDO = []
FUNC = []
if genenamelist:
for allele in genenamelist:
if any(c in allele.description
for c in ('|F|', '|(F)|', '|[F]|', '|ORF|', '|(ORF)|',
'|[ORF]|')):
FUNC.append(allele)
if any(c in allele.description for c in ('|P|', '|(P)|', '|[P]|')):
PSDO.append(allele)
if "TRBC" in genenamestr:
cdict = dict()
for exons in FUNC:
all_name = exons.id[exons.id.find("|") +
1:nth_occur(exons.id, "|", 2)]
rdict(all_name, exons, cdict)
FUNC = []
exn_order = dict()
for n in xrange(len(cdict.values())):
for seqs in cdict.values(
)[n]: #arrange exons according to their order
i = int(
seqs.description[nth_occur(seqs.description, "|", 5) -
1])
exn_order[i] = seqs
#make joined sequence and new description
ntseq = []
ntpos = []
ntnt = []
for j in sorted(exn_order.keys()):
ntseq.append(str(exn_order[j].seq))
ntpos.append(exn_order[j].description[
nth_occur(exn_order[j].description, "|", 5) +
1:nth_occur(exn_order[j].description, "|", 6)])
ntnt.append(exn_order[j].description[
nth_occur(exn_order[j].description, "|", 6) +
1:nth_occur(exn_order[j].description, "|", 7) - 2])
seq = "".join(ntseq)[1:]
position = ";".join(ntpos)
region = "C-REGION"
nt = "+".join(ntnt).replace(" ", "")
fle = SeqRecord(
Seq(seq, SingleLetterAlphabet()),
id=cdict.values()[n][0].id,
name=cdict.values()[n][0].name,
description="|".join([
cdict.values()[n][0].description[:nth_occur(
cdict.values()[n][0].description, "|", 4)], region,
position, nt, " | | | | | | | |"
]),
dbxrefs=cdict.values()[n][0].dbxrefs)
FUNC.append(fle)
SeqIO.write(FUNC, "extdata/%s_F.fasta" % genenamestr, "fasta")
SeqIO.write(PSDO, "extdata/%s_P.fasta" % genenamestr, "fasta")
SeqIO.write(genenamelist, "extdata/%s.fasta" % genenamestr, "fasta")
def variableCassette(geneList, seqList, toVary="", variants=[], variantSeq=[]):
# Store both name and sequence in a SeqRecord
# Append them to a list
# Return list as fragments to be stitched
if toVary != "":
toVary = int(toVary)
records = []
counter = 0
for gene in geneList:
name = gene
sequence = seqList[counter]
Rec = SeqRecord(Seq(sequence, SingleLetterAlphabet()),
id=str(counter + 1))
Rec.name = name
records.append(Rec)
counter += 1
variantRecords = []
variantRecords.append(records)
# Executes if variants is not empty
counter = 0
if variants != []:
for variant in variants:
name = variant
sequence = variantSeq[counter]
Rec = SeqRecord(Seq(sequence, SingleLetterAlphabet()),
id=str(counter + 1))
Rec.name = name
# Make a copy of the original, switch the fragments and add it to the list.
# Deep-copy ensures there are no pointer issues
tempVariant = copy.deepcopy(records)
tempVariant[toVary - 1] = Rec
variantRecords.append(copy.deepcopy(tempVariant))
counter += 1
# Returns a list of lists of the answers.
answer = [[stitch(variantRecords[0])]]
variants = []
for n in range(len(variantRecords) - 1):
frags = variantRecords[n + 1][toVary - 2:toVary]
variantStitch = [stitch(frags)]
answer.append(variantStitch)
return answer
def concatenate_fasta(args):
for fasta_file in glob.glob(args.input):
concat = Seq.Seq("", SingleLetterAlphabet())
for s in SeqIO.parse(fasta_file, 'fasta'):
concat += s
print(fasta_file)
concat.id = fasta_file
concat.description = ""
SeqIO.write(concat, args.output, 'fasta')
def _cleanAli2(recordNuc, omit, fileName, stage):
handleP = open('tAligned.fas', 'rU')
records = list(SeqIO.parse(handleP, 'fasta'))
store = list()
for rec in records:
if "gi|" in rec.id or "Homo_sapiens" in rec.id:
n_count_s = rec.seq[:3].count("N")
n_count_e = rec.seq[-3:].count("N")
break
#print records
#print recordNuc
for i, rec in enumerate(records):
nucData = [x.seq for x in recordNuc if x.id in rec.id]
nucSeqData = _spliter(nucData[0], 3)
if stage == "mapper":
nucSeqData[0] = nucSeqData[0].lstrip("N")
nucSeqData[-1] = nucSeqData[-1].rstrip("N")
sequence = Seq("", SingleLetterAlphabet())
pos = 0
for j, amino in enumerate(rec.seq):
if amino == '-':
sequence = sequence + Seq("---", SingleLetterAlphabet())
elif amino == "Z":
sequence = sequence + Seq("NNN", SingleLetterAlphabet())
pos = pos + 1
else:
sequence = sequence + nucSeqData[pos]
pos = pos + 1
records[i].seq = Seq(str(sequence), SingleLetterAlphabet())
with open(fileName, 'w') as fp:
SeqIO.write(records, fp, "fasta")
os.remove('translated.fas')
os.remove('tAligned.fas')
def record_from_indices(record, indices):
'''Given a list of integers and a sequence record, will create a new
record corresponding to the indices specified.'''
new_seq = Seq(''.join([record[i] for i in indices]),
SingleLetterAlphabet())
new_record = SeqRecord(new_seq, record.id)
new_record.description, new_record.name = record.id, record.id
if record.letter_annotations:
new_annotations = [
record.letter_annotations['phred_quality'][i] for i in indices
]
new_record.letter_annotations['phred_quality'] = new_annotations
return new_record
def convert_a2m(ali):
fh = cStringIO.StringIO(ali)
msa = AlignIO.read(fh, 'fasta')
fh.close()
new_msa = []
for rec in msa:
new_seq = Seq(re.sub(r'[a-z.]', '', str(rec.seq)),
SingleLetterAlphabet())
new_rec = rec
new_rec.seq = new_seq
new_msa.append(new_rec)
new_msa = MultipleSeqAlignment(new_msa)
return new_msa.format('fasta')
def add_sequences(input_df, seqrecords):
"""
It modifies seqrecords by appending the new sequences.
"""
for row in input_df.itertuples():
name = '{}:{}'.format(row.Species, row.GeneID)
seqrecords.append(
SeqRecord(Seq(row.NucleotideSequence, SingleLetterAlphabet()),
id=name,
name=name,
description='{}:{} {} na:na:na:{}:{}:{}'.format(
row.Species, row.GeneID, row.ExonID,
row.ExonRegionStart, row.ExonRegionEnd, row.Strand)))
def _cleanAli(recordNuc, omit, fileName):
handleP = open('tAligned.fas', 'rU')
records = list(SeqIO.parse(handleP, 'fasta'))
store = list()
for i, rec in enumerate(records):
nucData = [x.seq for x in recordNuc if x.id in rec.id]
nucSeqData = _spliter(nucData[0], 3)
sequence = Seq("", SingleLetterAlphabet())
pos = 0
#print len([x for x in rec.seq if x!="-"]), len(nucSeqData)
for j, amino in enumerate(rec.seq):
if amino == '-':
sequence = sequence + Seq("---", SingleLetterAlphabet())
elif amino == "Z":
sequence = sequence + Seq("NNN", SingleLetterAlphabet())
pos = pos + 1
else:
if pos == 0 or pos == len(nucSeqData) - 1:
sequence = sequence + nucSeqData[pos].strip("N")
else:
sequence = sequence + nucSeqData[pos]
pos = pos + 1
records[i].seq = Seq(str(sequence).strip("N"), SingleLetterAlphabet())
optimal_length = manage_seqLength([len(rec.seq) for rec in records])
for i, rec in enumerate(records):
rec.seq = rec.seq[:optimal_length]
with open(fileName, 'w') as fp:
SeqIO.write(records, fp, "fasta")
os.remove('translated.fas')
os.remove('tAligned.fas')
def editEmpty(name, sequence, cutname, promoter=None, terminator=None):
df = pd.read_excel(os.path.join(PROJECT_ROOT, "cutsites.xlsx"))
labels = df['name'].values
ChrLetters = df['chrom. loc.'].values
ExpValues = df['exp. lev.'].values
cutSeqs = df['sequence'].values
cutArray = {
'name': Series(labels, index=labels),
'exp. lev.': Series(ExpValues, index=labels),
'chrom. loc.': Series(ChrLetters, index=labels),
'sequence': Series(cutSeqs, index=labels)
}
cutFrame = DataFrame(cutArray)
location = cutFrame.loc[cutname, 'chrom. loc.'] + ".fasta"
cutSequence = cutFrame.loc[cutname, 'sequence']
ChromosomeSeq = SeqIO.read(
os.path.join(PROJECT_ROOT, "chromosomes\\" + location), "fasta").seq
if ChromosomeSeq.find(cutSequence) == -1:
ChromosomeSeq = ChromosomeSeq.reverse_complement()
StartIndex = ChromosomeSeq.find(cutSequence)
EndIndex = StartIndex + 34
UpSeq = ChromosomeSeq[StartIndex - HomologyLength:StartIndex]
DownSeq = ChromosomeSeq[EndIndex:EndIndex + HomologyLength]
UpHomRec = SeqRecord(UpSeq, id=cutname)
DownHomRec = SeqRecord(DownSeq, id=cutname)
orfRecord = SeqRecord(Seq(sequence, SingleLetterAlphabet()), id=name)
if promoter is None:
fragments = [UpHomRec, orfRecord, DownHomRec]
else:
PromoterGeneRec = fetchGene(promoter)
PromoterRec = fetchNeighbor(PromoterGeneRec, "upstream", 600)
PromoterRec.id = PromoterRec.id + "ps"
TerminatorGeneRec = fetchGene(promoter)
TerminatorRec = fetchNeighbor(TerminatorGeneRec, "upstream", 600)
TerminatorRec.id = TerminatorRec.id + "ts"
fragments = [
UpHomRec, PromoterRec, orfRecord, TerminatorRec, DownHomRec
]
return stitch(fragments)
def test_genemap_fasta_gene1(self):
record_1 = SeqRecord(seq=Seq('AAAA', SingleLetterAlphabet()),
id='sample1__Brandomstuff__gene1__1',
name='sample1__Brandomstuff__gene1__1',
description='sample1__Brandomstuff__gene1__1',
dbxrefs=[])
record_2 = SeqRecord(seq=Seq('CCCC', SingleLetterAlphabet()),
id='sample2__Arandomstuff__gene1__1',
name='sample2__Arandomstuff__gene1__1',
description='sample2__Arandomstuff__gene1__1',
dbxrefs=[])
expected = {'gene1': [record_1, record_2]}
result = dict(sequence_split.make_genemap(self.gene1, '__', 2,
'fasta'))
''' BioSeq.Seq objects don't have proper __eq__ comparison implemented, so have to comapre __dict__ '''
def seqs_equal(seq1, seq2):
return str(seq1.seq) == str(
seq2.seq
) and seq1.id == seq2.id and seq1.name == seq2.name and seq1.description == seq2.description
for key in expected.keys():
self.assertTrue(key in result.keys())
self.assertTrue(seqs_equal(expected[key][0], result[key][0]))
def capitalize_seqs(input_fasta, output_fasta, filetype='fasta'):
"""Capitalizes the ATGC sequence in a fasta file and writes it to a new file.
:param input_fasta: Filepath to the input fasta file to capitalize.
:param output_fasta: Filepath to the output fasta file.
:param filetype: The file format to read and write. Either 'fasta' or 'fastq'
:return: Filepath to the output fasta file.
"""
capitalized_output_file = BufferedSeqWriter(output_fasta, filetype)
for sequence in SeqIO.parse(open(input_fasta, 'rU'), "fasta"):
sequence.seq = Seq(str(sequence.seq).upper(), SingleLetterAlphabet())
capitalized_output_file.write(sequence)
capitalized_output_file.flush()
return output_fasta
def main():
args = cli(sys.argv[0], sys.argv[1:])
for infile in args.infiles:
alignments = AlignIO.read(infile,
format="fasta",
alphabet=Gapped(SingleLetterAlphabet(), "-"))
id_ = os.path.split(os.path.splitext(infile.name)[0])[-1]
fmt = alignments.format("stockholm").split("\n", maxsplit=1)
args.outfile.write(fmt[0])
args.outfile.write(f"\n#=GF ID {id_}\n")
args.outfile.write(fmt[1])
return
def clusters_alignment(file):
full_alignment = starting_pt(file)
clusters = hawk_wrap(file)
consensus_list = []
cluster_index = []
for x in range(len(clusters)):
if len(clusters[x]) > 1:
cluster_index.append(x)
while clusters:
current_cluster = clusters.pop(0)
if len(current_cluster) > 1:
multiple_seqs = [full_alignment[x] for x in current_cluster]
aligned_multiples = alignment_wrap(multiple_seqs)
subprocess.run([
"em_cons",
"/home/god/Documents/oldHawkEye/very_unlikely_to_be_called_this.fasta",
"/home/god/Documents/oldHawkEye/very_unlikely_consensus.cons"
])
with open(
"/home/god/Documents/oldHawkEye/very_unlikely_consensus.cons"
) as consensus:
seq = consensus.read()
con = []
dash = 'n'
R_DNA = ['A', 'C', 'T', 'G', 'U']
for i in seq.split():
for j in i:
if j in R_DNA:
con.append(j)
elif j not in R_DNA:
pass
con_str = ''.join(str(k) for k in con)
consensus.close()
simple_seq_r = SeqRecord(Seq(con_str, SingleLetterAlphabet()),
id="CLUSTER" + str(cluster_index.pop(0)))
consensus_list.append(simple_seq_r)
elif len(current_cluster) == 1:
single_seq = [full_alignment[x] for x in current_cluster]
consensus_list.append(single_seq.pop(0))
SeqIO.write(consensus_list, "very_unlikely_to_be_called_this.fasta",
"fasta")
file = "very_unlikely_to_be_called_this.fasta"
in_file = "/home/god/Documents/oldHawkEye/" + file
mafft_cline = MafftCommandline(input=in_file)
stdout, stderr = mafft_cline()
handle = open(file, "w")
handle.write(stdout)
handle.close()
def mask_seq(seq, start, end, id, length):
'''Replaces the coords between start and end with N'''
masked = []
insert = Seq("N" * length, SingleLetterAlphabet())
# Find contig
for seq_record in seq:
if seq_record.id == id:
newseq = seq_record.seq[:start - 1] + insert + seq_record.seq[end:]
#seq_record.seq = newseq
masked.append(
SeqRecord(id=seq_record.id,
description=seq_record.description,
seq=newseq))
else:
masked.append(seq_record)
return masked
def convert_sequence_file_format(input_filepath,
input_format,
output_format,
output_filename=None):
"""
Converts an sequence file specified in the 'input_format' argument in an alignment file
in the format specified in the 'output_format'.
"""
input_file_basename = os.path.basename(input_filepath)
input_file_name = os.path.splitext(input_file_basename)[0]
if not output_filename:
output_file_basename = "%s.%s" % (
input_file_name,
pymod_vars.alignment_extensions_dictionary[output_format])
else:
output_file_basename = "%s.%s" % (
output_filename,
pymod_vars.alignment_extensions_dictionary[output_format])
output_file_handler = open(
os.path.join(os.path.dirname(input_filepath), output_file_basename),
"w")
if input_format == "pymod":
input_file_handler = open(input_filepath, "r")
records = [
SeqRecord(Seq(l.split(" ")[1].rstrip("\n\r")), id=l.split(" ")[0])
for l in input_file_handler.readlines()
]
else:
input_file_handler = open(input_filepath, "r")
records = list(
SeqIO.parse(input_file_handler,
input_format,
alphabet=SingleLetterAlphabet()))
if output_format == "pymod":
lines = []
for i in [(rec.id, rec.seq) for rec in records]:
lines.append(str(i[0]) + '\n')
lines.append(str(i[1]) + '\n')
output_file_handler.writelines(lines)
else:
SeqIO.write(records, output_file_handler, output_format)
input_file_handler.close()
output_file_handler.close()
def _get_seqlist(self, slist, gdict):
if len(slist)==0: return Seq('',alphabet=SingleLetterAlphabet())
if self.strand() == '+':
iseq = slist[0].sequence(gdict).upper()
for i,p1 in enumerate(slist[1:]):
p0 = slist[i]
assert not p1.start < p0.end, "error: overlapping intervals:\n%s\n%s" % (p0,p1)
if p1.start > p0.end:
iseq += gdict[p1.chrom][p0.end:p1.start].lower()
iseq += p1.sequence(gdict).upper()
else:
iseq = slist[0].sequence(gdict).upper()
for i,p1 in enumerate(slist[1:]):
p0 = slist[i]
assert not p0.start < p1.end, "error: overlapping intervals:\n%s\n%s" % (p0,p1)
if p0.start > p1.end:
iseq += gdict[p1.chrom][p1.end:p0.start].reverse_complement().lower()
iseq += p1.sequence(gdict).upper()
return iseq
def filtergen(
file
): # generator function that returns edited reads that pass filter, to write new fastq file
for record in SeqIO.parse(file, "fastq"):
# Convert base qualities to Boolean based on Qscore threshold value. Only use reads with >=50% non-N:
recordqual = [
x > Qscore_threshold
for x in record.letter_annotations['phred_quality']
] # list of True, False etc
if float(sum(recordqual)) / float(
len(recordqual
)) >= .5: # note that True = 1, False = 0 for summing
# generates new read sequence where all bases < threshold is switched to 'N'
seq = "".join(
[y if x else 'N' for (x, y) in zip(recordqual, record.seq)])
# create new SeqRecord with edited read sequence
newrec = SeqRecord(Seq(seq, SingleLetterAlphabet()),
id=record.id,
name=record.name,
description=record.description,
letter_annotations=record.letter_annotations)
yield newrec
def __extractReads(self, indexVal, howLong, iterator):
for record in iterator:
read = self.__getSeq(record, indexVal, howLong)
if len(read) > 0:
readID, contig = re.match("^(\d+)-(contig\d+)$",
record.id).groups()
r = self.readInfo[readID]
#>58526338-contig00001-33057/1; KO:K00927 start: 575 offset: 287
header = "%s-%s/%s\tKO:%s\tstart:%s\toffset:%s" % (
record.id, r['taxa'], r['readnum'], self.ko, indexVal,
howLong)
newseq = Seq(
str(record.seq).upper().translate(
{ord(i): None
for i in '-'}), SingleLetterAlphabet())
newrecord = SeqRecord(newseq,
id=header,
name="",
description="")
self.outputRecords.append(newrecord)
self.readInfo[readID]['readnum'] += 1
return
def combine_sequence(in_fasta, threshold):
record_dict = SeqIO.index(in_fasta, "fasta") # index the record
# initiate an empty Sequence string
combined_string = Seq("", SingleLetterAlphabet())
# fasta_dic
out_fasta_dic = {}
# dictionary map index of each contig
out_map_dic = {}
start = 0
count = 0
for record in sorted(record_dict):
count += 1
new_contig = record_dict[record].seq
if len(new_contig) >= threshold:
length = len(new_contig)
combined_string += new_contig
out_map_dic[record_dict[record].id] = [start, start + length]
start += length # increment the start position
if count == 1:
out_fasta_dic["id"] = record_dict[record].id
out_fasta_dic["description"] = record_dict[record].description
out_fasta_dic["sequence"] = combined_string
return out_fasta_dic, out_map_dic
def extract_data(rdir, LVEXON):
#make /extdata directory
final_directory = makerdir('/extdata')
if not os.path.exists(final_directory):
os.makedirs(final_directory)
#read fasta file
rawdata = list(SeqIO.parse(rdir, "fasta"))
h**o = []
for element in rawdata:
if 'H**o' in element.id:
h**o.append(element)
TRB = []
for element in h**o:
if 'TRB' in element.id:
TRB.append(element)
TRBV = []
TRBD = []
TRBJ = []
TRBC = []
TRBL = []
for element in TRB:
if 'TRBV' in element.id:
if 'L-PART1+L-PART2' in element.description:
TRBL.append(element)
else:
TRBV.append(element)
elif 'TRBD' in element.id:
TRBD.append(element)
elif 'TRBJ' in element.id:
TRBJ.append(element)
elif 'TRBC' in element.id:
TRBC.append(element)
make_fpa_fasta("TRBV", TRBV)
make_fpa_fasta("TRBD", TRBD)
make_fpa_fasta("TRBJ", TRBJ)
make_fpa_fasta("TRBC", TRBC)
make_fpa_fasta("TRBL", TRBL)
lead = list(SeqIO.parse(LVEXON, "fasta"))
TRBV_F = list(SeqIO.parse("extdata/TRBV_F.fasta", "fasta"))
TRBV_P = list(SeqIO.parse("extdata/TRBV_P.fasta", "fasta"))
TRBV = TRBV_F + TRBV_P
matchedV = []
match = dict()
TRBLV = []
TRBLV_F = []
TRBLV_P = []
nostartcodon = 0
yesmatch = 0
seqbtwLV = 0
for allele in TRBV:
for lvsq in lead:
if allele.id[allele.id.find("TRB"):allele.id.find("*") +
3] in lvsq.id:
yesmatch += 1
if allele.seq in lvsq.seq[-len(allele.seq):]:
if str(lvsq.seq)[:3] == "atg":
temp = [
str(lvsq.seq[:-len(allele.seq)]),
str(allele.seq)
]
length = "{0}+{1} nt".format(len(temp[0]),
len(temp[1]))
new_description = allele.description[:nth_occur(
allele.description, "|", 6
) + 1] + length + allele.description[
nth_occur(allele.description, "|", 7):]
TRBLV.append(
SeqRecord(Seq(str("".join(temp)),
SingleLetterAlphabet()),
description=new_description,
id=lvsq.id,
name=lvsq.name,
dbxrefs=lvsq.dbxrefs))
rdict(
allele.id[allele.id.find("TRB"):allele.id.find("*"
)],
str(lvsq.seq[:-len(allele.seq)]), match)
matchedV.append(allele)
else:
nostartcodon += 1
else:
seqbtwLV += 1
mismatch = set(TRBV) - set(matchedV)
#for V alleles with no matching L, use other allele's L sequence
for allele in mismatch:
genename = allele.id[allele.id.find("TRB"):allele.id.find("*")]
for key in match:
if genename == key:
temp = [str(most_common(match[key])), str(allele.seq)]
length = "{0}+{1} nt".format(len(temp[0]), len(temp[1]))
new_description = allele.description[:nth_occur(
allele.description, "|", 6
) + 1] + length + allele.description[
nth_occur(allele.description, "|", 7):]
TRBLV.append(
SeqRecord(Seq("".join(temp), SingleLetterAlphabet()),
description=new_description,
id=allele.id,
name=allele.name,
dbxrefs=allele.dbxrefs))
matchedV.append(allele)
mismatch = set(TRBV) - set(matchedV)
n = []
for element in mismatch:
n.append(element.description[:element.description.find("*") + 3])
make_fpa_fasta("TRBLV", TRBLV)
def basicVariantCall( pileUp, ref=None ):
# This function will serve as a basic variant call method to be used in
# our single ref. pipelines. It will remove all singleton sites (with
# no mapping ambiguity).
from Bio.Seq import Seq
from Bio import SeqIO
from Bio.Alphabet import SingleLetterAlphabet
from itertools import izip
from scipy.stats import iqr
# import numpy as np
mapQC = []
summaryStats = []
consensusContigs = []
variants = []
for refName,counts,inserts in pileUp:
outName = refName.split("|")[-1].replace('/','_')
tmpDict = defaultdict(list)
tmpDict2 = defaultdict(int)
tmpDict['name'] = outName
tmpDict['variant'] = [np.nonzero(counts[:,n])[0].tolist() for n in xrange(counts.shape[1])]
tmpDict['cov'] = [counts[np.nonzero(counts[:,n])[0],n].tolist() for n in xrange(counts.shape[1])]
siteCov = np.sum(counts,axis=0)
tmpDict2['avg_cov'] = np.median( siteCov )
tmpDict2['std_cov'] = (20.0*iqr( siteCov )) / 27.0
tmpDict2['num_uncov'] = np.sum(siteCov == 0)
tmpDict2['contig_len'] = len(siteCov)
frac = counts[:,siteCov>0]
frac /= (1.*frac.sum(axis=0,keepdims=True))
# print np.sum(frac.sum(axis=0,keepdims=True) ==0)
S = -np.log(frac+.0001) * frac
tmpDict2['site_entropy'] = np.mean(np.sum(S,axis=0))
serialIndel = inserts
for outKey in serialIndel.keys():
for inKey in serialIndel[outKey].keys():
serialIndel[outKey][inKey] = np.asscalar(serialIndel[outKey][inKey])
tmpDict['indel'] = serialIndel
mapQC.append(tmpDict)
summaryStats.append(tmpDict2)
# Save consensus sequence (where it differs from reference) as well as locations where variation
# has been detected.
consensusSeq = []
variableSites = defaultdict(lambda: defaultdict(list))
alphabet = {0: 'A', 1: 'C', 2: 'G', 3: 'T', 4: '-', 5: 'N'}
for n,seq in enumerate(tmpDict['variant']):
if (len(seq) > 1):
variableSites[n]['nuc'] = [alphabet[s] for s in seq]
variableSites[n]['cov'] = tmpDict['cov'][n] # If there are more than one detected variant, store
consensusSeq.append(alphabet[seq[np.argmax(tmpDict['cov'][n])]])
elif (len(seq) == 0):
consensusSeq.append('-')
else:
consensusSeq.append(alphabet[seq[0]])
consensusContigs.append(Seq(''.join(consensusSeq),SingleLetterAlphabet()))
variants.append(variableSites)
# refSeq = []
# with open(ref,'r') as refFile:
# for contig in SeqIO.parse(refFile,'fasta'):
# refSeq.append(contig.seq)
# Store consensus sequence (as a variant of the reference to save space)
conSeq = defaultdict(str)
# for C in xrange(len(refSeq)):
# for n,(rN,cN) in enumerate(izip(refSeq[C],consensusContigs[C])):
# if (cN != 'N' and rN != cN):
# conSeq[n] = cN
return mapQC,summaryStats,variants,conSeq
def guess_alphabet(sequence:str):
'''
This function guesses the alphabet of a string representing a
biological sequence.
'''
import string
from Bio.Alphabet import SingleLetterAlphabet
from Bio.Alphabet import NucleotideAlphabet
from Bio.Alphabet import ProteinAlphabet
from Bio.Alphabet.IUPAC import extended_protein
from Bio.Alphabet.IUPAC import protein
from Bio.Alphabet.IUPAC import ambiguous_dna
from Bio.Alphabet.IUPAC import unambiguous_dna
from Bio.Alphabet.IUPAC import extended_dna
from Bio.Alphabet.IUPAC import ambiguous_rna
from Bio.Alphabet.IUPAC import unambiguous_rna
if len(sequence)<1:
return SingleLetterAlphabet()
for c in sequence:
if c not in string.printable:
return SingleLetterAlphabet()
xp = set(extended_protein.letters)
pr = set(protein.letters)
ad = set(ambiguous_dna.letters)
ud = set(unambiguous_dna.letters)
ed = set(extended_dna.letters)
ar = set(ambiguous_rna.letters)
ur = set(unambiguous_rna.letters)
all = xp|pr|ad|ud|ed|ar|ur
sequence_chars = set(sequence.upper())
if sequence_chars - all - set(string.punctuation+string.whitespace):
return SingleLetterAlphabet()
nucleic_count = 0
for letter in "GATCUNgatcun":
nucleic_count += sequence.count(letter)
if float(nucleic_count) / float(len(sequence)) >= 0.9: # DNA or RNA
if 'T' in sequence_chars and 'U' in sequence_chars:
alphabet = NucleotideAlphabet()
elif not sequence_chars-ud:
alphabet = unambiguous_dna
elif not sequence_chars-ad :
alphabet = ambiguous_dna
elif not sequence_chars-ed:
alphabet = extended_dna
elif not sequence_chars-ur:
alphabet = unambiguous_rna
elif not sequence_chars-ar:
alphabet = ambiguous_rna
else:
alphabet = NucleotideAlphabet()
else:
threecode = ['ALA', 'ASX', 'CYS', 'ASP','GLU', 'PHE', 'GLY', 'HIS',
'ILE', 'LYS', 'LEU', 'MET','ASN', 'PRO', 'GLN', 'ARG',
'SER', 'THR', 'VAL', 'TRP','TYR', 'GLX', 'XAA', 'TER',
'SEL', 'PYL', 'XLE']
tc=set(threecode)
three_letter_alphabet = set( [ sequence[i:i+3] for i in range(0,len(sequence),3)] )
if not three_letter_alphabet - tc:
alphabet = "three letter code"
elif sequence_chars - pr:
alphabet = protein
elif sequence_chars - xp:
alphabet = extended_protein
else:
alphabet = ProteinAlphabet()
return alphabet
if trna_mod_row['Organellum'] != 'cytosolic':
continue
key = (trna_mod_row['Amino acid'], trna_mod_row['Anticodon (Canonical)'])
if key not in trna_mods:
trna_mods[key] = []
trna_mods[key].append({
'id': trna_mod_row['Id'],
'can': trna_mod_row['Sequence (Canonical)'],
'nc': trna_mod_row['Sequence'],
})
trna_seq_rows = pandas.read_excel('examples/homo_sapiens_rna/summary.xlsx',
sheet_name='tRNA seqs - Gogakos et al.',
header=[0, 1])
trna_seqs = []
alphabet = SingleLetterAlphabet()
for _, trna_seq_row in trna_seq_rows.iterrows():
aa = list(trna_seq_row.items())[0][1]
anticodon = list(trna_seq_row.items())[1][1]
key = (aa, anticodon)
if key not in trna_mods:
continue
# find most similar sequences in MODOMICS
best_id = None
best_can_seq = None
best_seq = None
best_alignment = None
best_score = -float('inf')
for trna_mod in trna_mods[key]:
alignment = pairwise2.align.globalxs(
def clusters_alignment(file):
full_alignment = starting_pt(file)
clusters = hawk_wrap(file)
consensus_list = []
cluster_index = []
if type(clusters[0]) == list:
for x in range(len(clusters)):
if len(clusters[x]) > 1:
cluster_index.append(x)
while clusters:
if type(clusters[0]) == list:
current_cluster = clusters.pop(0)
if len(current_cluster) > 1:
multiple_seqs = [full_alignment[x] for x in current_cluster]
with tempfile.NamedTemporaryFile() as alignment_file:
SeqIO.write(multiple_seqs, alignment_file.name, "fasta")
with tempfile.NamedTemporaryFile() as consensus_file:
subprocess.call([
"em_cons", alignment_file.name, consensus_file.name
])
seq = consensus_file.name
data = open(seq).read()
con = []
dash = 'n'
R_DNA = [
'A', 'a', 'C', 'c', 'T', 't', 'G', 'g', 'U', 'u'
]
for i in data.split():
for j in i:
if j in R_DNA:
con.append(j)
elif j not in R_DNA:
pass
con_str = ''.join(str(k) for k in con)
cluster_seq_id = []
while multiple_seqs:
seq_info = multiple_seqs.pop(0)
cluster_seq_id.append(seq_info.id)
seqid_string = '|'.join(str(l) for l in cluster_seq_id)
simple_seq_r = SeqRecord(Seq(con_str, SingleLetterAlphabet()),
id="CLUSTER_" +
str(cluster_index.pop(0)) + ": " +
seqid_string)
consensus_list.append(simple_seq_r)
elif len(current_cluster) == 1:
single_seq = [full_alignment[x] for x in current_cluster]
consensus_list.append(single_seq.pop(0))
elif type(clusters[0]) == int:
noSaturation = alignment_wrap(full_alignment)
return noSaturation
final_aligned_clusters = []
with tempfile.NamedTemporaryFile() as consensus_alignment:
SeqIO.write(consensus_list, consensus_alignment.name, "fasta")
file = consensus_alignment.name
in_file = consensus_alignment.name
mafft_cline = MafftCommandline(input=in_file)
stdout, stderr = mafft_cline()
handle = open(file, "w")
handle.write(stdout)
handle.close()
path = consensus_alignment.name
data = open(path).read()
with tempfile.NamedTemporaryFile() as clusters_file:
records = (
rec.upper()
for rec in SeqIO.parse(consensus_alignment.name, "fasta"))
SeqIO.write(records, clusters_file.name, "fasta")
aligned_list = AlignIO.read(open(clusters_file.name), 'fasta')
return aligned_list
def grande_alignment(file):
raw_seqs = starting_pt(file)
before_segment = clusters_alignment(file)
list_of_clusters = hawk_wrap(file)
print("Clusters formed:")
print(len(list_of_clusters))
print(list_of_clusters)
seqs_in_consensus = []
allEqualSeqs = []
position = 0
if type(list_of_clusters[0]) == list:
big_final_alignment = []
while list_of_clusters:
current_cluster = list_of_clusters.pop(0)
if len(current_cluster) == 1:
single_seq = before_segment[position]
big_final_alignment.append(single_seq)
position += 1
elif len(current_cluster) > 1:
seqs_in_the_cluster = [raw_seqs[x] for x in current_cluster]
multiple_seq = before_segment[position]
while seqs_in_the_cluster:
seqs_in_consensus.append(seqs_in_the_cluster.pop(0))
with tempfile.NamedTemporaryFile() as segment_align:
SeqIO.write(seqs_in_consensus, segment_align.name,
"fasta")
original_seqs = list(
SeqIO.parse(segment_align.name, "fasta"))
with tempfile.NamedTemporaryFile() as segmenter:
dash = '-'
dashes = []
consensus = multiple_seq.seq
seq = original_seqs
for n in range(len(seq)):
seq_str = seq[n].seq
seq_id = seq[n].id
able_to_insert_seq = seq_str.tomutable()
for x in consensus:
if x == dash:
dashes = [
y for y, x in enumerate(consensus)
if x == dash
]
while dashes:
dash_position = dashes.pop(0)
able_to_insert_seq.insert(dash_position, '-')
new_seq_record = SeqRecord(Seq(
str(able_to_insert_seq),
SingleLetterAlphabet()),
id=seq_id)
big_final_alignment.append(new_seq_record)
position += 1
with tempfile.NamedTemporaryFile() as unequalSeqs:
dash = '-'
SeqIO.write(big_final_alignment, unequalSeqs.name, "fasta")
total = list(SeqIO.parse(unequalSeqs.name, "fasta"))
largestSeq = len(
max([total[ind].seq for ind in range(len(total))], key=len))
while total:
checkSeq = total.pop(0)
if len(checkSeq.seq) < largestSeq:
smallerLength = len(checkSeq.seq)
seqStr = checkSeq.seq
seqId = checkSeq.id
needsEndingFilled = seqStr.tomutable()
endingDashes = list(
range(smallerLength + 1, largestSeq + 1))
while endingDashes:
j = endingDashes.pop(0)
needsEndingFilled.insert(j, '-')
nowEqual = SeqRecord(Seq(str(needsEndingFilled),
SingleLetterAlphabet()),
id=seqId)
allEqualSeqs.append(nowEqual)
elif len(checkSeq.seq) == largestSeq:
allEqualSeqs.append(checkSeq)
elif type(list_of_clusters[0]) == int:
allEqualSeqs = before_segment
for seqs in range(len(allEqualSeqs)):
print(">" + allEqualSeqs[seqs].id)
print(allEqualSeqs[seqs].seq)
def mask_fna_with_spacers(INTERMEDIATES: str,
FNA_FILE: str,
SELECT_SPACER_FASTA: str,
MASKED_FNA: str = 'masked.fna'):
from subprocess import call as execute
MASKED_ORGANISM_DB = INTERMEDIATES + 'masked_db/' # file to store masked FNA file
execute(['mkdir', MASKED_ORGANISM_DB])
MASKED_FNA = MASKED_ORGANISM_DB + MASKED_FNA
NEIGHBORING_NUCLEOTIDES = 500 # number of BP (+/-) to also mask around the array(s)
arrayStartEndList = get_start_end_list(
SELECT_SPACER_FASTA) # list containing the start and end of all arrays
sequence_dict = fna_to_dict(
FNA_FILE
) # header along with its start/end and the full sequence of the organism
crisprName_maps_seqName = dict()
for key in sequence_dict.keys():
nc_name = key.split('.')
if len(nc_name) > 1:
crisprName_maps_seqName[''.join(nc_name[0:-1])] = key
else:
crisprName_maps_seqName[nc_name[0]] = key
# crisprName_maps_seqName = {''.join(key.split('.')[0:-1]):key for key in sequence_dict.keys()}
'''
Masks CRISPR spacers from the organisms FNA file.
'''
for arrayStartEnd in arrayStartEndList:
start = int(arrayStartEnd[0]) - 1
end = int(arrayStartEnd[1])
crispr_name = arrayStartEnd[2] # fetch the nc # of the array
nc_id = crisprName_maps_seqName[crispr_name]
start, end = start - NEIGHBORING_NUCLEOTIDES, end + NEIGHBORING_NUCLEOTIDES
sequence = str(sequence_dict[nc_id].seq
) # obtain the sequence: str from the sequence dict
if start < 0: # corrects neg number
start = 0
if end > len(
sequence
): # corrects if end happens to be bigger than the length of the sequence
end = len(sequence)
blank = 'N'
blank = blank * (
(end - start)
) # multiplies masking to cover the array plus the neighboring BP's
sequence = sequence[0:start] + blank + sequence[end:len(
sequence)] # masks sequences
sequence_dict[nc_id].seq = Seq(sequence, SingleLetterAlphabet())
from sys import path as sys_path
sys_path.append('dependencies/PyGornism/')
# from regex import string_with_limited_width
'''
Writes new FNA file with masking to disk.
'''
with open(MASKED_FNA, 'w') as handle:
for seq_record in sequence_dict:
SeqIO.write(sequence_dict[seq_record], handle, 'fasta')
return MASKED_FNA, crisprName_maps_seqName