def node_is_similar(seq1, seq2):
l1 = len(seq1)
l2 = len(seq2)
if l1 == 0 or l2 == 0: return False
if l1 <= 2 and l2 <= 2: return True
if l1 < l2:
l1, l2 = l2, l1
seq1, seq2 = seq2, seq1
# always make seq1 the longer one
o1 = Aligner(seq1,
match=2,
mismatch=5,
gap_open=3,
gap_extend=1,
report_secondary=False,
report_cigar=False)
# require the the whole (shorter) seq2 must be aligned
# and set min score to approx 90% accuracy
if EXPECTED_ERR_RATE == 0:
res = o1.align(seq2, min_score=l2 * 2 * 1.0, min_len=l2 * 1.0)
elif EXPECTED_ERR_RATE < 2:
res = o1.align(seq2, min_score=int(l1 * 2 * .80), min_len=int(l2 * .9))
else:
raise Exception, "Expected error rate not implemented for {0}% and above".format(
EXPECTED_ERR_RATE)
return res is not None
def node_is_similar(seq1, seq2):
l1 = len(seq1)
l2 = len(seq2)
if l1 == 0 or l2 == 0: return False
if l1 <= 2 and l2 <= 2: return True
if l1 < l2:
l1, l2 = l2, l1
seq1, seq2 = seq2, seq1
# always make seq1 the longer one
o1 = Aligner(seq1, match=2, mismatch=5, gap_open=3, gap_extend=1, report_secondary=False, report_cigar=False)
# require the the whole (shorter) seq2 must be aligned
# and set min score to approx 90% accuracy
res = o1.align(seq2, min_score=int(l1*2*.80), min_len=int(l2*.9))
return res is not None
def filter_reads(readfile):
print("Filtering reads\n")
ssw = Aligner(tn_seq)
total=0
matched=0
with open(filtered_filename,'w') as f:
for title, seq, qual in FastqGeneralIterator(open(readfile)):
total+=1
res = ssw.align(seq,min_score, min_match_length)
if res:
end = res.query_end+1
if len(seq)-end >= min_remaining_length:
matched+=1
f.write('@%s\n%s\n+\n%s\n' % (title, seq[end:], qual[end:]))
print("%s of %s read had the tn seq\n" % (matched, total))
def test():
filter_quality = True
min_qual = 25
trim_adapter = True
adapter_path = "/home/adrien/Programming/Python/IsFinder/test/adapter.fa"
R1 = "/home/adrien/Programming/Python/IsFinder/test/fastq/all_AAV_R1.fastq.gz"
R2 = "/home/adrien/Programming/Python/IsFinder/test/fastq/all_AAV_R2.fastq.gz"
# Define a quality filter object
if filter_quality:
q_filter = QualityFilter(min_qual)
else:
q_filter = None
# Define a adapter trimmer object
if trim_adapter:
a = Aligner()
trimmer = AdapterTrimmer(a, adapter_path)
else:
trimmer = None
# Define the global fastq filter
F = FastqFilterPP(R1,
R2,
quality_filter=q_filter,
adapter_trimmer=trimmer,
input_qual="fastq-sanger")
print(repr(F))
print(repr(q_filter))
print(repr(a))
print(repr(trimmer))
def test2():
filter_quality = True
min_qual = 25
trim_adapter = True
adapter_path = "/home/adrien/Programming/Python/IsFinder/test/adapter.fa"
R1_path = "/home/adrien/Programming/Python/IsFinder/test/fastq/all_AAV_R1.fastq.gz"
R2_path = "/home/adrien/Programming/Python/IsFinder/test/fastq/all_AAV_R2.fastq.gz"
# Define a quality filter object
if filter_quality:
q_filter = QualityFilter(min_qual)
else:
q_filter = None
# Define a adapter trimmer object
if trim_adapter:
a = Aligner(report_cigar=True)
trimmer = AdapterTrimmer(a, adapter_path)
else:
trimmer = None
# Define the global fastq filter
f_filter = FastqFilter(q_filter, trimmer)
print(f_filter.filter(R1_path, R2_path))
print(repr(q_filter))
print(repr(a))
print(repr(trimmer))
def validate_reconstructed_seq(seq, orig):
"""
seq --- the sequence that is reconstructed
orig --- the original sequence
because the reconstructed seq can be longer, we don't care about deletions
(deletions w.r.t could just be exon skipping or minor base errors)
we only care that there is NOT a lot of insertions (which would indicate error in my bubble solution)
"""
o1 = Aligner(seq, match=2, mismatch=5, gap_open=3, gap_extend=1, report_secondary=False, report_cigar=True)
l2 = len(orig)
res = o1.align(orig, min_score=l2*2*.90, min_len=l2)
for num, type in iter_cigar_string(res.cigar_string):
if type == 'I' and num > 5:
return False, res.cigar_string
return True, res.cigar_string
def validate_reconstructed_seq(seq, orig):
"""
seq --- the sequence that is reconstructed
orig --- the original sequence
because the reconstructed seq can be longer, we don't care about deletions
(deletions w.r.t could just be exon skipping or minor base errors)
we only care that there is NOT a lot of insertions (which would indicate error in my bubble solution)
"""
o1 = Aligner(seq, match=2, mismatch=5, gap_open=3, gap_extend=1, report_secondary=False, report_cigar=True)
l2 = len(orig)
res = o1.align(orig, min_score=l2*2*.90, min_len=l2)
for num, type in iter_cigar_string(res.cigar_string):
if type == 'I' and num > 5:
return False, res.cigar_string
return True, res.cigar_string
def node_is_similar(seq1, seq2):
l1 = len(seq1)
l2 = len(seq2)
if l1 == 0 or l2 == 0: return False
if l1 <= 2 and l2 <= 2: return True
if l1 < l2:
l1, l2 = l2, l1
seq1, seq2 = seq2, seq1
# always make seq1 the longer one
o1 = Aligner(seq1, match=2, mismatch=5, gap_open=3, gap_extend=1, report_secondary=False, report_cigar=False)
# require the the whole (shorter) seq2 must be aligned
# and set min score to approx 90% accuracy
if EXPECTED_ERR_RATE == 0:
res = os.align(seq2, min_score=l2*2*1.0, min_len=l2*1.0)
elif EXPECTED_ERR_RATE < 2:
res = o1.align(seq2, min_score=int(l1*2*.80), min_len=int(l2*.9))
else:
raise Exception, "Expected error rate not implemented for {0}% and above".format(EXPECTED_ERR_RATE)
return res is not None
def align (opt):
print ("Inport subject sequence")
# Import fasta subject
if opt.subject.rpartition(".")[2].lower() == "gz":
subject_handle = gzip.open(opt.subject, "r")
else:
subject_handle = open(opt.subject, "r")
subject = SeqIO.read(subject_handle, "fasta")
print ("Inport query sequences and count the number of sequences")
# Import fasta subject
if opt.query.rpartition(".")[2].lower() == "gz":
nseq = count_seq(opt.query, opt.qtype, True)
query_handle = gzip.open(opt.query, "r")
else:
nseq = count_seq(opt.query, opt.qtype, False)
query_handle = open(opt.query, "r")
query_gen = SeqIO.parse(query_handle, opt.qtype)
print("{} contains {} sequences to align".format(opt.query, nseq))
# Calculate a step list for the progress bar
nseq_list = [int(nseq*i/100.0) for i in range(5,101,5)]
print ("Initialize ssw aligner with the subject sequence")
# Init the an Aligner object with the reference value
ssw = Aligner(
str(subject.seq),
match=int(opt.match),
mismatch=int(opt.mismatch),
gap_open=int(opt.gap_open),
gap_extend= int(opt.gap_extend),
report_secondary=False,
report_cigar=True)
# Write the header of the SAM file
with open("result.sam", "w") as f:
f.write("@HD\tVN:1.0\tSO:unsorted\n")
f.write("@SQ\tSN:{}\tLN:{}\n".format(subject.id, len(subject.seq)))
f.write("@PG\tID:Striped-Smith-Waterman\tPN:pyssw\tVN:0.1\n")
f.write("@CO\tScore_values = match {}, mismatch {}, gap_open {}, gap_extend {}\n".format(
opt.match,
opt.mismatch,
opt.gap_open,
opt.gap_extend))
f.write("@CO\tFilter Options = min_score {}, min_len {}\n".format(
opt.min_score,
opt.min_len))
print ("Starting alignment of queries against the subject sequence")
start = time()
# Align each query along the subject an write result in a SAM file
i = 0
for query in query_gen:
# Find the best alignment
if opt.reverse:
al, orient = find_best_align (ssw, query, float(opt.min_score), int(opt.min_len))
else:
al, orient = ssw.align(str(query.seq), float(opt.min_score), int(opt.min_len)), True
# If valid match found
if al:
f.write(sam_line(
qname=query.id,
flag=0 if orient else 16,
rname=subject.id,
pos=al.ref_begin+1,
cigar=al.cigar_string,
seq=str(query.seq),
qual=SeqIO.QualityIO._get_sanger_quality_str(query) if opt.qtype == "fastq" else "*",
tags=["AS:i:{}".format(al.score)]))
# If no valid match found and -u flag activated (report unaligned)
elif opt.unaligned:
f.write(sam_line(
qname=query.id,
flag=4,
seq=str(query.seq),
qual=SeqIO.QualityIO._get_sanger_quality_str(query) if opt.qtype == "fastq" else "*"))
# Else = match unreported
# Progress bar
i+=1
if i in nseq_list:
frac = i/float(nseq)
t = time()-start
print ("{} sequences \t{}% \tRemaining time = {}s".format(i, int(frac*100), round(t/frac-t, 2)))
print ("\n{} Sequences processed in {}s".format(i, round(time()-start, 2)))
def align(opt):
print("Inport subject sequence")
# Import fasta subject
if opt.subject.rpartition(".")[2].lower() == "gz":
subject_handle = gzip.open(opt.subject, "r")
else:
subject_handle = open(opt.subject, "r")
subject = SeqIO.read(subject_handle, "fasta")
print("Inport query sequences and count the number of sequences")
# Import fasta subject
if opt.query.rpartition(".")[2].lower() == "gz":
nseq = count_seq(opt.query, opt.qtype, True)
query_handle = gzip.open(opt.query, "r")
else:
nseq = count_seq(opt.query, opt.qtype, False)
query_handle = open(opt.query, "r")
query_gen = SeqIO.parse(query_handle, opt.qtype)
print("{} contains {} sequences to align".format(opt.query, nseq))
# Calculate a step list for the progress bar
nseq_list = [int(nseq * i / 100.0) for i in range(5, 101, 5)]
print("Initialize ssw aligner with the subject sequence")
# Init the an Aligner object with the reference value
ssw = Aligner(str(subject.seq),
match=int(opt.match),
mismatch=int(opt.mismatch),
gap_open=int(opt.gap_open),
gap_extend=int(opt.gap_extend),
report_secondary=False,
report_cigar=True)
# Write the header of the SAM file
with open("result.sam", "w") as f:
f.write("@HD\tVN:1.0\tSO:unsorted\n")
f.write("@SQ\tSN:{}\tLN:{}\n".format(subject.id, len(subject.seq)))
f.write("@PG\tID:Striped-Smith-Waterman\tPN:pyssw\tVN:0.1\n")
f.write(
"@CO\tScore_values = match {}, mismatch {}, gap_open {}, gap_extend {}\n"
.format(opt.match, opt.mismatch, opt.gap_open, opt.gap_extend))
f.write("@CO\tFilter Options = min_score {}, min_len {}\n".format(
opt.min_score, opt.min_len))
print("Starting alignment of queries against the subject sequence")
start = time()
# Align each query along the subject an write result in a SAM file
i = 0
for query in query_gen:
# Find the best alignment
if opt.reverse:
al, orient = find_best_align(ssw, query, float(opt.min_score),
int(opt.min_len))
else:
al, orient = ssw.align(str(query.seq), float(opt.min_score),
int(opt.min_len)), True
# If valid match found
if al:
f.write(
sam_line(
qname=query.id,
flag=0 if orient else 16,
rname=subject.id,
pos=al.ref_begin + 1,
cigar=al.cigar_string,
seq=str(query.seq),
qual=SeqIO.QualityIO._get_sanger_quality_str(query)
if opt.qtype == "fastq" else "*",
tags=["AS:i:{}".format(al.score)]))
# If no valid match found and -u flag activated (report unaligned)
elif opt.unaligned:
f.write(
sam_line(
qname=query.id,
flag=4,
seq=str(query.seq),
qual=SeqIO.QualityIO._get_sanger_quality_str(query)
if opt.qtype == "fastq" else "*"))
# Else = match unreported
# Progress bar
i += 1
if i in nseq_list:
frac = i / float(nseq)
t = time() - start
print("{} sequences \t{}% \tRemaining time = {}s".format(
i, int(frac * 100), round(t / frac - t, 2)))
print("\n{} Sequences processed in {}s".format(
i, round(time() - start, 2)))
from ssw_wrap import Aligner
polyA = Aligner("A"*200,
match=5,
mismatch=3,
gap_open=4,
gap_extend=1,
report_secondary=False,
report_cigar=True)
query_seq = "CTACGTAGCTAGCTAGCTATGCTAGCTGATGCTAGCTGTGTAAAAAAAAAAAAAAGAAAAAATTTAAAAAAAACGTGCTAGCTGTGCTATTAGCTAGTCGTGGCTAGTGTAC"
result = polyA.align(query_seq, min_score=20, min_len=20)
begin = result.query_begin
end = result.query_end
print(" "*begin+query_seq[begin:end+1]+"\n"+query_seq)
from csv import DictReader
from Bio import SeqIO
from ssw_wrap import Aligner
MIN_BARCODE_MATCH_LEN = 10 # have to at least see 10 of the 16 bp aligned
MIN_BARCODE_MATCH_SCORE = 20
MIN_BARCODE_SCORE_LEAD = 10
barcodes = ['atgacgcatcgtctga', 'gcagagtcatgtatag', 'gagtgctactctagta', 'catgtactgatacaca']
for i in xrange(4): barcodes[i] = barcodes[i].upper()
aligners = [Aligner(barcodes[i], match=2, mismatch=5, gap_open=3, gap_extend=1, report_secondary=False, report_cigar=True) for i in xrange(4)]
def main(ccs_fasta, flnc_fasta, primer_csv):
good_flnc = []
d = {}
reader = DictReader(open(primer_csv),delimiter=',')
for r in reader:
zmw = r['id'][:r['id'].rfind('/')]
d[zmw] = r
flog = open(flnc_fasta+'.filtered.log', 'w')
flog.write("flnc_id\tthreelen\tthreeseq\tscore0\tscore1\tscore2\tscore3\n")
for r in SeqIO.parse(open(ccs_fasta), 'fasta'):
zmw = r.id[:r.id.rfind('/')]
if zmw not in d: continue # discarded short sequences
rec = d[zmw]
cands = [] # list of (barcode i, score, end-start)
if rec['threeseen']=='1' and rec['fiveseen']=='1' and rec['polyAseen']=='1' and rec['chimera']=='0':
# is FLNC
