def score_phonetic_alignment(srcw,
tgtw,
slang,
tlang,
sim_matrix_path,
gap_start_p=-1.0,
gap_extend_p=-1.0):
# convert to ascii required by align library
nsrcw = ''.join(
make_ascii(srcw, slang) if slang in
langinfo.SCRIPT_RANGES else [str(c) for c in srcw])
ntgtw = ''.join(
make_ascii(tgtw, tlang) if tlang in
langinfo.SCRIPT_RANGES else [str(c) for c in tgtw])
## use global alignment
src_aln, tgt_aln = nw.global_align(nsrcw,
ntgtw,
matrix=sim_matrix_path,
gap_open=gap_start_p,
gap_extend=gap_extend_p)
return nw.score_alignment(src_aln,
tgt_aln,
matrix=sim_matrix_path,
gap_open=gap_start_p,
gap_extend=gap_extend_p)
def sound_seq_distance_str(self,seq1_str, seq2_str):
seq1_str = np.asanyarray(seq1_str)
seq2_str = np.asanyarray(seq2_str)
align = nw.global_align(seq1_str.tostring(), seq2_str.tostring(), gap_open=0, gap_extend=-5, matrix='/tmp/som.costs')
len1 = len(seq1_str.tostring())
len2 = len(seq2_str.tostring())
return (-nw.score_alignment(*align, gap_open=0, gap_extend=-5, matrix='/tmp/som.costs'))/(len1+len2+0.0)
def score_phonetic_alignment(srcw,tgtw,slang,tlang,sim_matrix_path,gap_start_p=-1.0,gap_extend_p=-1.0):
# convert to ascii required by align library
nsrcw=''.join(make_ascii(srcw,slang) if slang in langinfo.SCRIPT_RANGES else [str(c) for c in srcw ])
ntgtw=''.join(make_ascii(tgtw,tlang) if tlang in langinfo.SCRIPT_RANGES else [str(c) for c in tgtw ])
## use global alignment
src_aln,tgt_aln=nw.global_align(nsrcw,ntgtw,matrix=sim_matrix_path, gap_open=gap_start_p, gap_extend=gap_extend_p)
return nw.score_alignment(src_aln,tgt_aln,matrix=sim_matrix_path, gap_open=gap_start_p, gap_extend=gap_extend_p)
def testNeedleman(N):
alpha = [
'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N',
'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'
]
Validated = 0
SamePath = 0
for epoch in tqdm(range(N)):
sizeA = random.randint(1, SIZEMAX)
sizeB = random.randint(sizeA // 2, 2 * sizeA)
A = ""
B = ""
for i in range(sizeA):
A += alpha[random.randint(0, len(alpha) - 1)]
for i in range(sizeB):
B += alpha[random.randint(0, len(alpha) - 1)]
aligned = nw.global_align(A,
B,
matrix='atiam-fpa_alpha.dist',
gap_open=-3,
gap_extend=-3)
score = nw.score_alignment(aligned[0],
aligned[1],
gap_open=-3,
gap_extend=-3,
matrix='atiam-fpa_alpha.dist')
res = (aligned[0], aligned[1], score)
try:
(a, b, s) = myNeedleman(A,
B,
matrix='atiam-fpa_alpha.dist',
gap_open=-3,
gap_extend=-3)
if s == score:
Validated += 1
if res == (a, b, s):
SamePath += 1
except RuntimeError:
print(A, B)
pass
print(str(100 * Validated / N) + "% are validated.")
print(str(100 * SamePath / N) + "% have the exact same path.")
def _score_alignment(self):
if all([self._score_match is not None, self._score_mismatch is not None]):
matrix = self._get_matrix_file(match=self._score_match,
mismatch=self._score_mismatch)
elif self._matrix is not None:
matrix = self._get_matrix_file(matrix=self._matrix)
else:
matrix = self._get_matrix_file(match=self._match,
mismatch=self._mismatch)
gap_open = self._score_gap_open if self._score_gap_open is not None else self._gap_open
gap_extend = self._score_gap_extend if self._score_gap_extend is not None else self._gap_extend
aln = nw.score_alignment(self.aligned_query,
self.aligned_target,
gap_open=gap_open,
gap_extend=gap_extend,
matrix=matrix)
return aln
def _score_alignment(self):
if all(
[self._score_match is not None, self._score_mismatch is not None]):
matrix = self._get_matrix_file(match=self._score_match,
mismatch=self._score_mismatch)
elif self._matrix is not None:
matrix = self._get_matrix_file(matrix=self._matrix)
else:
matrix = self._get_matrix_file(match=self._match,
mismatch=self._mismatch)
gap_open = self._score_gap_open if self._score_gap_open is not None else self._gap_open
gap_extend = self._score_gap_extend if self._score_gap_extend is not None else self._gap_extend
aln = nw.score_alignment(self.aligned_query,
self.aligned_target,
gap_open=gap_open,
gap_extend=gap_extend,
matrix=matrix)
return aln
# Reference code for testing
import nwalign as nw
print("myNeedleman")
print(
myNeedleman("CEELECANTH",
"PELICAN",
matrix='atiam-fpa_alpha.dist',
gap_open=-1,
gap_extend=-1))
print("Nwalign")
aligned = nw.global_align("CEELECANTH",
"PELICAN",
matrix='atiam-fpa_alpha.dist')
score = nw.score_alignment(aligned[0],
aligned[1],
gap_open=-1,
gap_extend=-1,
matrix='atiam-fpa_alpha.dist')
print('Results for basic gap costs (linear)')
print(aligned[0])
print(aligned[1])
print('Score : ' + str(score))
print("myNeedleman")
print(
myNeedleman("CEELECANTH",
"PELICAN",
matrix='atiam-fpa_alpha.dist',
gap_open=-5,
gap_extend=-2))
print("Nwalign")
def score_alignment(self, seq1, seq2, gp_e=-2, gp_o=-5):
score = nw.score_alignment(seq1, seq2, matrix='BLOSUM62.txt', gap_extend=gp_e, gap_open=gp_o)
return score
def compare_datapoints(p1, p2):
gist_1 = p1[2:len(p1) - 2]
gist_2 = p2[2:len(p2) - 2]
gist_1_flipped = list(gist_1)
# We also try the 'flipped' (mirrored) version of the gesture to allow comparison between left-handed and right-handed gestures
# (although it's not super accurate).
# Need to take into account not to flip spine (so we drop the last 3 parts)
for i in range(0, (len(gist_1_flipped) - 3) / 2):
tmp = GestureComparison._flip_quadrants(gist_1_flipped[i])
# print gist_1_flipped[i] + " --> " + tmp
# print str(i) + "<->" + str(len(gist_1_flipped) / 2 + i - 3 + 1)
gist_1_flipped[i] = GestureComparison._flip_quadrants(
gist_1_flipped[len(gist_1_flipped) / 2 + i - 3 + 1])
gist_1_flipped[len(gist_1_flipped) / 2 + i - 3 + 1] = tmp
score = 0.0
score_flipped = 0.0
for i in range(0, len(gist_1)):
if gist_1[i] != 0 and gist_2[i] != '':
res = nwalign.global_align(
gist_1[i],
gist_2[i],
matrix=os.path.dirname(os.path.realpath(__file__)) +
'/alignment.matrix')
this_score = nwalign.score_alignment(
res[0],
res[1],
gap_open=0,
gap_extend=-5,
matrix=os.path.dirname(os.path.realpath(__file__)) +
'/alignment.matrix')
if i >= len(gist_1) - 3:
this_score *= 2
score += this_score
res = nwalign.global_align(
gist_1_flipped[i],
gist_2[i],
matrix=os.path.dirname(os.path.realpath(__file__)) +
'/alignment.matrix')
this_score_flipped = nwalign.score_alignment(
res[0],
res[1],
gap_open=0,
gap_extend=-5,
matrix=os.path.dirname(os.path.realpath(__file__)) +
'/alignment.matrix')
if i >= len(gist_1) - 3:
this_score_flipped *= 2
score_flipped += this_score_flipped
# print str(score_flipped) + " " + str(score)
if score_flipped > score:
score_flipped -= abs(int(p1[-2]) -
int(p2[-1])) + abs(int(p1[-1]) - int(p2[-2]))
# print "using flipped! " + p1[1] + " " + p2[1] + " " + str(score_flipped) + " > " + str(score)
return -score_flipped
else:
score -= abs(int(p1[-2]) -
int(p2[-2])) + abs(int(p1[-1]) - int(p2[-1]))
return -score
def RemapReadsSingle(self, count=None):
#scores = {}
counter = 0
has_score = False
write_mode = 'wb'
if self.binary_mode is False:
write_mode = 'wh'
self._out = pysam.Samfile(self.sam_out,
mode=write_mode,
referencenames=self.sam_in.references,
referencelengths=self.sam_in.lengths,
header=self._MakeHeader(self.sam_in.header)
)
for read in self.sam_in.fetch():
'Optional setting of count, to only realign count reads'
if count is not None and counter > count:
break
if read.is_unmapped is True:
self._out.write(read)
continue
if counter == 0:
# Check if an alignment score is already present
# If it is then record this in the has_score flag
tags = read.tags
for i in range(0, len(tags)):
if tags[i][0] == 'AS':
has_score = True
continue
if self.only_gapped is True:
has_indel = False
for c in read.cigar:
if c[0] == 1 or c[0] == 2:
# read has an indel
has_indel = True
break
if has_indel == False:
# Read must not have an indel
print(read.qname + ', ' + str(read.cigar) + " does not have an indel")
self._out.write(read)
continue
'''
if the read is a perfect match then don't realign
'''
fivep_soft_clip = 0
threep_soft_clip = 0
cigar_last = len(read.cigar) - 1
if read.cigar[0][0] == 4:
fivep_soft_clip = read.cigar[0][1]
if read.cigar[cigar_last][0] == 4:
threep_soft_clip = read.cigar[cigar_last][1]
ref = self.ref.fetch(reference=self.refnames[read.tid],
start=read.aend - read.alen - fivep_soft_clip,
end=read.aend + threep_soft_clip)
# Realign sense strand reads
query = ''
subject = ''
if self.reverse_sense is True and read.is_reverse is False:
query = self.ReverseSeq(read.seq)
subject = self.ReverseSeq(ref.upper())
#query = Seq(read.seq).complement().tostring()
#subject = Seq(ref.upper()).complement().tostring()
else:
query = read.seq
subject = ref.upper()
aln = nw.global_align(query, subject,
gap_open=self.gap_open,
gap_extend=self.gap_extend,
matrix=self.matrix)
if self.compute_scores is True:
score = nw.score_alignment(aln[0], aln[1],
gap_open=self.gap_open,
gap_extend=self.gap_extend,
matrix=self.matrix)
if has_score is True:
as_index = None
tags = read.tags
for i in range(0, len(tags)):
if tags[i][0] == 'AS':
as_index = i
if as_index is None:
raise ValueError("Read " + read.qname +
" is missing an alignment score.")
tags[as_index] = ('AS', score)
read.tags = tags
else:
read.tags = [('AS', score)] + read.tags
bam_cigar = self._MakeBamCigar(aln, read)
if self.reverse_sense is True and read.is_reverse is False:
bam_cigar.reverse()
if self.verbose is True:
self.PrettyPrint(read, aln, bam_cigar)
# New read
read.cigar = bam_cigar
read.tags = read.tags + [('OC', self._MakeSamCigar(read.cigar)),
('OP', read.pos)]
self._out.write(read)
counter += 1
self._out.close
def _RealignRead(self, read):
has_score = False
if read.is_unmapped is True:
#self._out.write(read)
return read
tags = read.tags
'''If any of the read tags are AS, then remember the read has an
existing score.'''
for i in range(0, len(tags)):
if tags[i][0] == 'AS':
has_score = True
continue
if self.only_gapped is True:
has_indel = False
for c in read.cigar:
if c[0] == 1 or c[0] == 2:
# read has an indel
has_indel = True
break
if has_indel == False:
# Read must not have an indel
'''
If the read is a perfect match then don't realign
'''
print(read.qname + ', ' + str(read.cigar) + " does not have an indel")
#self._out.write(read)
return read
fivep_soft_clip = 0
threep_soft_clip = 0
cigar_last = len(read.cigar) - 1
if read.cigar[0][0] == 4:
fivep_soft_clip = read.cigar[0][1]
if read.cigar[cigar_last][0] == 4:
threep_soft_clip = read.cigar[cigar_last][1]
ref = self.ref.fetch(reference=self.refnames[read.tid],
start=read.aend - read.alen - fivep_soft_clip,
end=read.aend + threep_soft_clip)
# Realign sense strand reads
query = ''
subject = ''
if self.reverse_sense is True and read.is_reverse is False:
query = self.ReverseSeq(read.seq)
subject = self.ReverseSeq(ref.upper())
#query = Seq(read.seq).complement().tostring()
#subject = Seq(ref.upper()).complement().tostring()
else:
query = read.seq
subject = ref.upper()
print query, subject
aln = nw.global_align(query, subject,
gap_open=self.gap_open,
gap_extend=self.gap_extend,
matrix=self.matrix)
if self.compute_scores is True:
score = nw.score_alignment(aln[0], aln[1],
gap_open=self.gap_open,
gap_extend=self.gap_extend,
matrix=self.matrix)
if has_score is True:
as_index = None
tags = read.tags
for i in range(0, len(tags)):
if tags[i][0] == 'AS':
as_index = i
if as_index is None:
raise ValueError("Read " + read.qname +
" is missing an alignment score.")
tags[as_index] = ('AS', score)
read.tags = tags
else:
read.tags = [('AS', score)] + read.tags
bam_cigar = self._MakeBamCigar(aln, read)
if self.reverse_sense is True and read.is_reverse is False:
bam_cigar.reverse()
if self.verbose is True:
self.PrettyPrint(read, aln, bam_cigar)
# New read
read.cigar = bam_cigar
read.tags = read.tags + [('OC', self._MakeSamCigar(read.cigar)),
('OP', read.pos)]
#self._out.write(read)
return read
print 'Performing alignement on CEELECANTH and PELICAN...'
ali = myNeedleman('CEELECANTH', 'PELICAN')
print ali[0]
print ali[1]
print ''
# Reference code for testing
import nwalign as nw
aligned = nw.global_align("CEELECANTH",
"PELICAN",
matrix='atiam-fpa_alpha.dist')
score = nw.score_alignment('CEELECANTH',
'-PELICAN--',
gap_open=-5,
gap_extend=-2,
matrix='atiam-fpa_alpha.dist')
print('Results for basic gap costs (linear)')
print(aligned[0])
print(aligned[1])
print('Score : ' + str(score))
aligned = nw.global_align("CEELECANTH",
"PELICAN",
matrix='atiam-fpa_alpha.dist',
gap_open=-5,
gap_extend=-2)
score = nw.score_alignment('CEELECANTH',
'-PELICAN--',
gap_open=-5,
gap_extend=-2,
singreps=line.split("#")
rep_count.append(len(singreps)-1)
poslist=[]
for singrep in singreps:
if singrep != "\n":
repinfo=singrep.split(":")
poslist.append(int(repinfo[1]))
rep_pos.append(poslist)
locus_num.append(i)
locus.append(curloc)
fullscore=0
array=()
score=0
for k in range(len(locus)):
print "Processing repeat", k, "of", locus[k]
fullscore=nw.score_alignment(rep[k], rep[k], gap_open=-5,\
gap_extend=-2, matrix='/home/CT/server/pybin/BLOSUM62')
print >> fileout, ">", k, locus[k], rep[k], rep_count[k], rep_pos[k]
for j in range(len(rep)):
array=nw.global_align(rep[k], rep[j], gap_open=-5,\
gap_extend=-2, matrix='/home/CT/server/pybin/BLOSUM62')
score=nw.score_alignment(array[0], array[1], gap_open=-5,\
gap_extend=-2, matrix='/home/CT/server/pybin/BLOSUM62')
if score>0 and score/float(fullscore)>=threshold and j!=k:
print >> fileout, j, locus[j], rep[j], rep_count[j], rep_pos[j]
filein.close()
fileout.close()
scoring = sw.ScoringMatrix('scoring_matrix.txt')
sw = sw.LocalAlignment(scoring)
match = 2
n = 0
for x, seq1 in enumerate(unique_sequences):
for y, seq2 in enumerate(unique_sequences):
alignment = nw.global_align(allsequences[seq1], allsequences[seq2])
score = float(
nw.score_alignment(alignment[0],
alignment[1],
gap_open=-5,
gap_extend=-2,
matrix='scoring_matrix.txt'))
n = float(len(alignment[0]) * match)
if abs(score) > n:
score = 0
similarity_matrix[x, y] = int(score)
dist_matrix[x, y] = float(score / n)
print '%d/%d Calculated %d alignments in %f seconds' % (
x, len(unique_sequences), len(unique_sequences), time.clock() - t0)
t0 = time.clock()