def alignSequences(ref_seq, query_seq, mis_allow):
"""remove PAm site """
ref_seq=ref_seq[:-6]
match = 2
mismatch = -1
ref_length = len(ref_seq) + 6
matches_required = len(ref_seq) - mis_allow # allow up to 8 mismatches
scoring = swalign.NucleotideScoringMatrix(match, mismatch)
sw = swalign.LocalAlignment(scoring, gap_penalty=-100, gap_extension_penalty=-100, prefer_gap_runs=True) # you can also choose gap penalties, etc...
# sw = swalign.LocalAlignment(scoring, gap_penalty=-10, gap_extension_penalty=-0.5, prefer_gap_runs=True) # you can also choose gap penalties, etc...
forward_alignment = sw.align(ref_seq, query_seq)
reverse_alignment = sw.align(ref_seq, reverseComplement(query_seq))
if forward_alignment.matches >= matches_required and forward_alignment.matches > reverse_alignment.matches:
start_pad = forward_alignment.r_pos
start = forward_alignment.q_pos - start_pad
end_pad = ref_length - forward_alignment.r_end
end = forward_alignment.q_end + end_pad
strand = "+"
return [forward_alignment.query[start:end], ref_length - forward_alignment.matches - 6, end - start, strand, start, end]
elif reverse_alignment.matches >= matches_required and reverse_alignment.matches > forward_alignment.matches:
start_pad = reverse_alignment.r_pos
start = reverse_alignment.q_pos - start_pad
end_pad = ref_length - reverse_alignment.r_end
end = reverse_alignment.q_end + end_pad
strand = "-"
return [reverse_alignment.query[start:end], ref_length - reverse_alignment.matches - 6, end - start, strand, start, end]
else:
return ["", "", "", "", "", ""]
def _get_mhc_pep(none_tcr, fasta):
MATCH = 2
MISMATCH = -1
SW_SCORE = swalign.NucleotideScoringMatrix(MATCH, MISMATCH)
SMITH_WATERMAN = swalign.LocalAlignment(SW_SCORE)
b2m_found = False
proteins = {}
peptides = []
mhcas = []
mhcbs = []
mhc_class = None
with open(fasta) as f:
for name, seq in _read_fasta(f):
letter = name.split(":")[1]
if letter in none_tcr:
proteins[letter] = seq
for test in proteins:
scores = []
refs = []
for reference in mhc_fastas:
refs.append(reference)
ref_protein = mhc_fastas[reference]
hit_protein = proteins[test]
swout = SMITH_WATERMAN.align(ref_protein, hit_protein)
swout = swout.score
scores.append(swout)
max_score = max(scores)
max_index = scores.index(max(scores))
protein_len = len(hit_protein)
#Low BLAST score
if max_score < 50:
#is it a peptide?
if protein_len <= 40:
peptides.append(test)
else:
ref_name = refs[max_index]
if ref_name == "B2M":
mhcbs.append(test)
b2m_found = True
elif "D" in ref_name.split("*")[0] and "B" in ref_name.split("*")[0]: #contains a d for class II and b for beta chain
mhcbs.append(test)
else:
mhcas.append(test)
if b2m_found:
mhc_class = 1
else:
mhc_class = 2
return peptides, mhcas, mhcbs, mhc_class
def clculatedelta(s,i,j):
import swalign;
dc=0;
match = 1
mismatch = -3
scoring = swalign.NucleotideScoringMatrix(match,mismatch)
sw = swalign.LocalAlignment(scoring)
ali1 = sw.align(s[i-1],s[j]);
ali1.dump();
ali2 = sw.align(s[i],s[j+1]);
ali2.dump();
delt_f = ali1.score + ali2.score;
f1=ali1.score;
f2=ali2.score;
ali1 = sw.align(s[i-1],s[i]);
ali1.dump();
ali2 = sw.align(s[j],s[j+1]);
ali2.dump();
delt_f = delt_f - (ali1.score + ali2.score);
f3=ali1.score;
f4=ali2.score;
if(f3>30):
dc=dc+1;
if(f4>30):
dc=dc+1;
if(f1>30):
dc=dc-1;
if(f2>30):
dc=dc-1;
return delt_f,dc;
def alignSequences(ref_seq, query_seq):
sys.stderr.write(ref_seq + "\t" + query_seq + "\n");
match = 2
mismatch = -1
ref_length = len(ref_seq)
matches_required = len(ref_seq) - 1 - 19 # allow up to 20 mismatches
scoring = swalign.NucleotideScoringMatrix(match, mismatch)
sw = swalign.LocalAlignment(scoring, gap_penalty=-100, gap_extension_penalty=-100, prefer_gap_runs=True) # you can also choose gap penalties, etc...
#sw = swalign.LocalAlignment(scoring, gap_penalty=-10, gap_extension_penalty=-0.5, prefer_gap_runs=True) # you can also choose gap penalties, etc...
forward_alignment = sw.align(ref_seq, query_seq)
reverse_alignment = sw.align(ref_seq, reverseComplement(query_seq))
sys.stderr.write("fwdmatch: " + str(forward_alignment.matches) + "\n")
sys.stderr.write("revmatch: " + str(reverse_alignment.matches) + "\n")
if forward_alignment.matches >= matches_required and forward_alignment.matches > reverse_alignment.matches:
start_pad = forward_alignment.r_pos
start = forward_alignment.q_pos - start_pad
end_pad = ref_length - forward_alignment.r_end
end = forward_alignment.q_end + end_pad
strand = "+"
return [forward_alignment.query[start:end], ref_length - forward_alignment.matches - 1, end - start, strand, start, end]
elif reverse_alignment.matches >= matches_required and reverse_alignment.matches > forward_alignment.matches:
start_pad = reverse_alignment.r_pos
start = reverse_alignment.q_pos - start_pad
end_pad = ref_length - reverse_alignment.r_end
end = reverse_alignment.q_end + end_pad
strand = "-"
return [reverse_alignment.query[start:end], ref_length - reverse_alignment.matches - 1, end - start, strand, start, end]
else:
return ["", "", "", "", "", ""]
def getAlign(ref_seq, query_seq):
match = 1
mismatch = -5
scoring = swalign.NucleotideScoringMatrix(match, mismatch)
sw = swalign.LocalAlignment(
scoring) # you can also choose gap penalties, etc...
alignment = sw.align(ref_seq, query_seq)
return alignment
def do_swalign(seq1, seq2, match=2, mismatch=-1, gap_penalty=-2, gap_extension_decay=0.5):
"""
Align two sequences using swalign
"""
scoring = swalign.NucleotideScoringMatrix(match, mismatch)
sw = swalign.LocalAlignment(scoring, gap_penalty=gap_penalty, gap_extension_decay=gap_extension_decay)
aln = sw.align(seq1, seq2)
return aln
def smith_water_align(seq1, seq2):
"""
Applies Smith-Waterman sequence alignment algo. on the two input sequences and returns the score
:param seq1: aminoacid sequence 1
:param seq2: aminoacid sequence 2
:return:
"""
scoring = swalign.NucleotideScoringMatrix(MATCH, MISMATCH)
align_obj = swalign.LocalAlignment(scoring, GAP_PENALTY,
GAP_EXTEND_PENALTY)
align = align_obj.align(seq1, seq2)
return align
def align(s1, s2, out):
match = 2
mismatch = -1
scoring = swalign.NucleotideScoringMatrix(match, mismatch)
# This sets up the aligner object. You must set your scoring matrix, but
# you can also choose gap penalties, etc...
sw = swalign.LocalAlignment(scoring)
# Using your aligner object, calculate the alignment between
# ref (first) and query (second)
alignment = sw.align(s1, s2)
return alignment.identity
def swFactory():
match = 2
mismatch = -1
gap_penalty = -1
gap_extension_penalty = -1
gap_extension_decay = 0.0
scoring = swalign.NucleotideScoringMatrix(match, mismatch)
return swalign.LocalAlignment((scoring),
gap_penalty,
gap_extension_penalty,
gap_extension_decay=gap_extension_decay,
verbose=False,
globalalign=False,
full_query=False)
def init_pop_score(chromosomes):
pop_fitness = list();
import swalign
scoring = swalign.NucleotideScoringMatrix(1,-3)
sw = swalign.LocalAlignment(scoring)
for i in range(len(chromosomes)):
arrayscore=list();
for j in range(len(chromosomes[i])-1):
alignment = sw.align(chromosomes[i][j],chromosomes[i][j+1]);
alignment.dump();
var = alignment.score;
arrayscore.append(var);
pop_fitness.append(sum(arrayscore))
# print(arrayscore);
print(pop_fitness)
return pop_fitness
def sw_one(query,refseq):
match = 5
mismatch = -4
scoring = swalign.NucleotideScoringMatrix(match, mismatch)
sw = swalign.LocalAlignment(scoring,gap_penalty = -30,gap_extension_penalty = -1)
alignment = sw.align(refseq, query)
#score = alignment.score
q_pos = alignment.q_pos
q_end = alignment.q_end
r_pos = alignment.r_pos
#print q_pos, q_end, r_pos, r_end
q_len = q_end-q_pos
middle_q = q_pos+0.5*q_len
middle_r = r_pos+0.5*q_len
#print query,refseq
#print middle_q, middle_r
return middle_q, middle_r
def assemble_seq(readid2seq, junc_seq, tmp_file_path):
match = 2
mismatch = -1
scoring = swalign.NucleotideScoringMatrix(match, mismatch)
sw = swalign.LocalAlignment(
scoring) # you can also choose gap penalties, etc...
hout = open(tmp_file_path + ".tmp3.assemble_input.fa", 'w')
for tid in sorted(readid2seq):
print >> hout, '>' + tid
print >> hout, readid2seq[tid]
hout.close()
hout = open(tmp_file_path + ".tmp3.assemble_output.fq", 'w')
sret = subprocess.call(
["fml-asm", tmp_file_path + ".tmp3.assemble_input.fa"], stdout=hout)
hout.close()
if sret != 0:
print >> sys.stderr, "fml-asm error, error code: " + str(sret)
sys.exit()
line_num = 0
temp_contig = ""
with open(tmp_file_path + ".tmp3.assemble_output.fq", 'r') as hin:
for line in hin:
line_num = line_num + 1
if line_num % 4 == 2:
tseq = line.rstrip('\n')
aln_1 = sw.align(tseq, junc_seq)
if aln_1.score >= 35:
ttcontig = tseq[aln_1.r_end:]
if len(ttcontig) > len(temp_contig): temp_contig = ttcontig
aln_2 = sw.align(tseq, my_seq.reverse_complement(junc_seq))
if aln_2.score >= 35:
ttcontig = my_seq.reverse_complement(tseq[:aln_2.r_pos])
if len(ttcontig) > len(temp_contig): temp_contig = ttcontig
# subprocess.call(["rm", "-rf", tmp_file_path + ".tmp3.assemble_input.fa"])
# subprocess.call(["rm", "-rf", tmp_file_path + ".tmp3.assemble_output.fq"])
return temp_contig
def RRGA_score(init_center):
print(init_center)
arrayscore = list()
import swalign
match = 1
mismatch = -3
scoring = swalign.NucleotideScoringMatrix(match, mismatch)
sw = swalign.LocalAlignment(scoring)
for j in range(len(init_center) - 1):
alignment = sw.align(init_center[j], init_center[j + 1])
#alignment = sw.align('ACACACTA','AGCACACA');
alignment.dump()
var = alignment.score
arrayscore.append(var)
init_cen_fitness = sum(arrayscore)
return init_cen_fitness
def calculate_missing_start(old, new):
"""
This function finds the start of the alignment for
the new IMGT numbered sequence, in essence it jumps
past the sequences lost in the HMM
"""
MATCH = 2
MISMATCH = -1
SCORE = swalign.NucleotideScoringMatrix(MATCH, MISMATCH)
sw = swalign.LocalAlignment(SCORE)
new = new.replace(".", "")
alignment = sw.align(old, new)
offset = alignment.r_pos
return offset
def fastq_trim(fastq,
linker_5=None,
linker_3=None,
out=sys.stdout,
pct_identity=0.8,
min_trim=4,
min_len=25,
verbose=False,
quiet=False,
failed_out=None):
'''
fname - the fastq filename
linker_5 - the 5' linker to remove
linker_3 - the 3' linker to remove
out - an output stream (eg: file, stdout)
pct_identity - the percentage of matches that must be present in the alignment to strip away linkers
min_trim - the distance away from the edges that the linkers much match w/in
failed_out - an output for failed reads
'''
sw = swalign.LocalAlignment(swalign.NucleotideScoringMatrix(2, -1), -1)
removed = 0
trimmed = 0
is_colorspace = fastq.is_colorspace # preload to keep reader happy.
for read in fastq.fetch(quiet=quiet):
retval = seq_trim(read.name, read.seq, read.qual, linker_5, linker_3,
is_colorspace, sw, pct_identity, min_trim, min_len,
verbose)
if not retval:
if failed_out:
read.write(failed_out)
removed += 1
else:
n_seq, n_qual = retval
if len(read.qual) != n_qual:
trimmed += 1
read.clone(seq=n_seq, qual=n_qual).write(out)
if not quiet:
sys.stderr.write('Trimmed: %s\n' % trimmed)
sys.stderr.write('Removed: %s (len)\n' % removed)
def last_fit_score(sequence):
print(sequence, len(sequence))
import swalign
contg = 0
arrayscore = list()
match = 1
mismatch = -3
scoring = swalign.NucleotideScoringMatrix(match, mismatch)
sw = swalign.LocalAlignment(scoring)
for j in range(len(sequence) - 1):
alignment = sw.align(sequence[j], sequence[j + 1])
alignment.dump()
var = alignment.score
if (var > 30):
contg = contg - 1
else:
contg = contg + 1
arrayscore.append(var)
fitness_value = sum(arrayscore)
return fitness_value, contg
def swalign_df(ref, query):
'''
This function returns swalign info:
ref, query, r_pos, r_end, q_pos, q_end, score, matches, mismatches, identity, cigar
'''
match = 2
mismatch = -1
scoring = swalign.NucleotideScoringMatrix(match, mismatch)
sw = swalign.LocalAlignment(
scoring, gap_extension_penalty=-2,
prefer_gap_runs=False) # you can also choose gap penalties, etc...
aligned = sw.align(ref, query) #ref, query
#return alignment
align_series = pd.Series([ref, query, aligned.r_pos, aligned.r_end, aligned.q_pos, aligned.q_end,\
aligned.score, aligned.matches, aligned.mismatches, \
aligned.identity, cigar_to_align(aligned.cigar)])
align_series.index = 'ref, query, r_pos, r_end, q_pos, q_end, score, matches, mismatches, identity, cigar'.split(
', ')
return align_series
def main(options):
"""Main logic of the script"""
if options.assign_score_threshold:
with open(options.stats) as s:
stats = {}
for row in csv.reader(s, delimiter="\t"):
# print row
stats[row[0]] = row[1]
score_thersh = float(stats["Score threshold"])
else:
score_thersh = 0.0
if options.five_prime_adapter or options.three_prime_adapter:
scoring = swalign.NucleotideScoringMatrix(match=1, mismatch=-1)
sw = swalign.LocalAlignment(scoring,
gap_penalty=-6,
gap_extension_penalty=-4)
with open(options.input) as f:
with open(options.output, 'w') as out:
if options.filter_ambiguous:
for row in csv.reader(f, delimiter='\t'):
if row[6] == "NA":
continue
if len(row[6]) >= options.length and float(
row[2]) >= score_thersh and row[7] == "Unique":
if options.five_prime_adapter is not None:
alignment_five = sw.align(
options.five_prime_adapter, row[6])
score_five = alignment_five.score
normed_five = score_five / float(len(row[6]))
else:
normed_five = 0.0
if options.three_prime_adapter is not None:
alignment_three = sw.align(
options.three_prime_adapter, row[6])
score_three = alignment_three.score
normed_three = score_three / float(len(row[6]))
else:
normed_three = 0.0
if normed_three < options.three_prime_adapter_threshold and normed_five < options.five_prime_adapter_threshold:
out.write(">%s:N\n%s\n" % (row[0], row[6]))
for match in re.finditer("C", row[6]):
out.write(">%s\n%s\n" %
(row[0] + ":" + str(match.span()[0]),
row[6][:match.span()[0]] + "T" +
row[6][match.span()[1]:]))
else:
counter_adapt = 0
counter = 0
for row in csv.reader(f, delimiter='\t'):
if row[6] == "NA":
continue
if len(row[6]) >= options.length and float(
row[2]) >= score_thersh:
counter += 1
if options.five_prime_adapter is not None:
alignment_five = sw.align(
options.five_prime_adapter, row[6])
score_five = alignment_five.score
normed_five = score_five / float(len(row[6]))
else:
normed_five = 0.0
if options.three_prime_adapter is not None:
alignment_three = sw.align(
options.three_prime_adapter, row[6])
score_three = alignment_three.score
normed_three = score_three / float(len(row[6]))
else:
normed_three = 0.0
if normed_three < options.three_prime_adapter_threshold and normed_five < options.five_prime_adapter_threshold:
counter_adapt += 1
out.write(">%s:N\n%s\n" % (row[0], row[6]))
for match in re.finditer("C", row[6]):
out.write(">%s\n%s\n" %
(row[0] + ":" + str(match.span()[0]),
row[6][:match.span()[0]] + "T" +
row[6][match.span()[1]:]))
if options.verbose:
syserr(
"%i seqs was removed out of %s because they were similar to the adapter\n"
% (counter - counter_adapt, counter))
def annot_sbinsert(infile):
#choose your own values here… 2 and -1 are common.
match = 2
mismatch = -1
scoring = swalign.NucleotideScoringMatrix(match, mismatch)
sw = swalign.LocalAlignment(scoring)
file_path = pathlib.Path(infile)
name = file_path.stem
outfile_full = name+'.ann.fil.txt'
outfile = name+'.ann.fil.strict.txt'
#5’- GTGTATGTAAACTTCCGACTTCAACTG ---TA
seq_dict = {'CGACTTCA': -4,'GACTTCAA': -3,'ACTTCAAC': -2,'CTTCAACT': -1,'TTCAACTG': 0}
with open(f'./break/{infile}', 'r') as hin, open(f'./break/{outfile}', 'w') as h1out, open(f'./break/{outfile_full}', 'w') as h2out:
next(hin)
header = '\t'.join(['chr','position(0-start)','ori_position','family_size','sb_direction','adj_seq','soft_clip_len','sw_match','sw_match_ratio','sb-seq|','|genome2bases'])
h1out.write(header+'\n')
h2out.write(header+'\n')
for line in hin:
F = line.rstrip('\n').split('\t')
#seq ='GTGTATGTAAACTTCCGACTTCAACTGTAATTCTCTGAATGG'
chr = F[0]
position = F[1]
read_direction = F[3]
reads = F[4]
seq = F[6]
sb_length = int(F[2])
sb_direction = F[7]
break_motif = seq[sb_length-8:sb_length]
j = 0
#check sb motif in nearby break position.
if break_motif in seq_dict.keys():
sb_motif = '+'
adj = seq_dict.get(break_motif)
if adj == -4 and seq[sb_length-8:sb_length+4] == 'CGACTTCAACTG':
j = 1
genome2base = seq[sb_length+4:sb_length+6]
elif adj == -3 and seq[sb_length-8:sb_length+3] == 'GACTTCAACTG':
j = 1
genome2base = seq[sb_length+3:sb_length+5]
elif adj == -2 and seq[sb_length-8:sb_length+2] == 'ACTTCAACTG':
j = 1
genome2base = seq[sb_length+2:sb_length+4]
elif adj == -1 and seq[sb_length-8:sb_length+1] == 'CTTCAACTG':
j = 1
genome2base = seq[sb_length+1:sb_length+3]
elif adj == 0 and seq[sb_length-8:sb_length] == 'TTCAACTG':
j = 1
genome2base = seq[sb_length:sb_length+2]
if j == 1:
#adj_seq ori_sb_genome_seq = seq[0:sb_length] + '|' + seq[sb_length:]
adj_seq = seq[0:sb_length+int(adj)*-1] + '|' + seq[sb_length+int(adj)*-1:]
if read_direction == '-':
adj_position = int(position) + int(adj)
if read_direction == '+':
adj_position = int(position) + int(adj)*-1
else:
adj_position = int(position)
sb_motif = '-'
adj_seq = seq[0:sb_length] + '|' + seq[sb_length:]
genome2base = seq[sb_length:sb_length+2]
else:
#adj_position = original position
adj_position = int(position)
sb_motif = '-'
genome2base = seq[sb_length:sb_length+2]
adj_seq = seq[0:sb_length] + '|' + seq[sb_length:]
#swalign
alignment = sw.align(seq, 'GTGTATGTAAACTTCCGACTTCAACTG')
sw_match = alignment.matches
#sw_cigar = alignment.cigar
#ratio sw_match score/soft-clipping length
sw_ratio = round(float(sw_match/sb_length),2)
#'chr','position(0-start)','ori_position','family_size','sb_direction','adj_seq','soft_clip_len','sw_match','sw_match_ratio','sb-seq|','|genome2bases','ori_sb|genome_seq'
rec = str(chr)+'\t'+str(adj_position)+'\t'+str(position)+'\t'+str(reads)+'\t'+str(sb_direction)+'\t'+str(adj_seq)+'\t'+ \
str(sb_length)+'\t'+str(sw_match)+'\t'+str(sw_ratio)+'\t'+ \
str(sb_motif)+'\t'+str(genome2base)+'\n'
# filtering: reads>=3, sb_length =22~30 and sw_match_ratio>0.9 PASS
if int(reads)>=3 and float(sw_ratio)>0.9:
h2out.write(rec)
if int(reads)>=3 and float(sw_ratio)>0.9 and int(sb_length)>=22 and int(sb_length)<=30 and str(sb_motif)=='+':
h1out.write(rec)
else: continue
def scores(sequence1,matrix1):
match = 1
mismatch = -3
scoring = swalign.NucleotideScoringMatrix(match,mismatch)
sw = swalign.LocalAlignment(scoring)
#matrix = np.zeros((len(sequence1),len(sequence1)));
for i in range(len(sequence1)):
for j in range(len(sequence1)):
j=j+i;
if(i==j):
continue;
if(j>len(sequence1)-1):
break;
alignment = sw.align(sequence1[i],sequence1[j]);
#alignment = sw.align('ACACACTA','AGCACACA');
alignment.dump();
var = alignment.score;
matrix1[i][j] =var;
matrix1[j][i] =var;
#arry.append(var);
#print(matrix)
return matrix1
#def fitness_chromose(chromes_array):
#
# match = 2
# mismatch = -1
## fitness_scor=list();
# fitness_scor=[]
# fit_scor_chroms=[]
## fit_scor_chroms=list();
# fitness_scor='';
# fit_scor_chroms='';
# for kk in range(len(chromes_array)):
# for k in range(len(chromes_array[kk])-1):
# scoring = swalign.NucleotideScoringMatrix(match,mismatch)
# sw = swalign.LocalAlignment(scoring)
# alignment = sw.align(chromes_array[k],chromes_array[k+1]);
# alignment.dump();
# var = alignment.score;
## fitness_scor.append (var)
# import numpy as np
# fitness_scor=np.array([var])
# fitness_scor=sum(fitness_scor)
## print(fitness_scor,'varrrrrrrrr')
## fitness_scor.append(var)
# #dddddddddddd
# fit_scor_chroms=np.array([ fitness_scor])
## fit_scor_chroms.append([fitness_scor])
# fitness_scor=[];
# print(fit_scor_chroms,'uzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzz')
# return fit_scor_chroms
#
##*****************************************************************###
#import numpy as np
#import copy;
#def center(center1,chromosomes):
##center=['TTAA','CCGA','CTTA','AAAT','TTCG','GGCA','AAATC'];
# rand_order=np.random.randint(4,size=(len(center1)+1));
##print(rand_order,'random')
# len_pop=2
## chromosomes=list();
# chrom=list();
# chrom=copy.deepcopy(center1); # center use swaping order to generate chromosomes
##print(chrom,('init chrom'))
# for k in range(len_pop):
# for z in range(len(center1)):
# print(rand_order,'random order')
# print(type(rand_order),'typerandom order')
# print(z,'L')
# ind=rand_order[0,z]
# print(ind,'ind')
# #if(ind!=len(center1)):
# print(type(chrom),'uzma')
# s=chrom[ind]
# print(chrom,'chromee')
# print(ind,'ind')
# print(rand_order,'order')
# chrom[ind]=chrom[ind+1] # swap order to generate chromosomes
# chrom[ind+1]=s
## else:
## s=chrom[len(center)]
## print(chrom,'chromee')
## print(ind,'ind')
## chrom[ind]=chrom[1] # swap order to generate chromosomes
## chrom[1]=s
# #print(chrom,('updat chorm'))
# chromosomes.append(chrom);
# #print(chromosom,('append chorm'));
# # print(chrom,(' charm'))
# chrom=[];
# chrom=copy.deepcopy(center1);
## print(center,('checking chrom'));
## print(chrom,('new center'));
# rand_order='';
# rand_order=np.random.randint(5, size=(1,len(center1)+1))
# #print(rand_order)
# return chromosomes
for line in f:
if re.match('^>', line):
b.append(next(f))
f.close()
#MANIPULATED VARIABLE, CAN CHANGE TO YOUR PREFERENCES
match = 1
mismatch = -3
gap = -1
fw = open(
'Matrix_swalign_{0}_match={1}_mismatch={2}.txt'.format(
filename, match, mismatch), 'w+')
scoring = swalign.NucleotideScoringMatrix(match, mismatch)
sw = swalign.LocalAlignment(scoring)
for i in range(len(b)):
for j in range(len(b)):
if i == j:
print >> fw, "0,",
else:
scoring = swalign.NucleotideScoringMatrix(match, mismatch)
sw = swalign.LocalAlignment(
scoring, gap) #CAN ADD MORE VARIABLE. REFER SWALIGN FILES
a = sw.align(b[i], b[j])
s = a.dump()
print >> fw, "{0},".format(s),
print >> fw, "\n",
__author__ = 'michael'
'''
F**K SO STUPEEED
'''
import os
import sys
from collections import defaultdict
from ast import literal_eval
import string
import swalign
import zipfile
SCORING = swalign.NucleotideScoringMatrix()
ALIGNER = swalign.LocalAlignment(SCORING, globalalign=True, gap_penalty=-5)
UPPERCASE = set(string.ascii_uppercase)
AGREEMENT_THRESHOLD = .99
PRIOR_WEIGHT = 3
def read_reference(ref_fn):
with open(ref_fn, 'r') as ref_file:
genome_name = ref_file.readline().strip()[1:]
chrom_name = ref_file.readline().strip()[4:]
ref = ''.join([line.strip() for line in ref_file])
return genome_name, chrom_name, ref
def process_line(line):
"""
:param line:
:return:
def align_filter(ref, query, mode, fusion_name=''):
"""
Aligns query to reference CDS sequence using the Smith-Waterman algorithm. Returns None if the
alignment is clipped at the fusion boundary.
:param str ref: In-frame reference transcript
:param str query: Query transcript
:param str mode: 'donor' or 'acceptor'
:return: Alignment features
:rtype: namedtuple
"""
alignment_stats = collections.namedtuple(
'AlignStats', 'qstart, qstop, rstart, rstop, insertions, deletions')
bounds_regex = re.compile(
r'Query\s*:\s*(?P<qstart>\d*)\s*\w*\s*(?P<qstop>\d*)\s*[\|\s]*\s*Ref\s*:\s*(?P<rstart>\d*)\s*\w*\s*(?P<rstop>\d*)'
)
match_regex = re.compile(r'Matches: \d+\s\((?P<percent>\d*)')
match = 5
mismatch = -1
scoring = swalign.NucleotideScoringMatrix(match, mismatch)
sw = swalign.LocalAlignment(scoring)
alignment = sw.align(ref, query)
# First check that the donor sequence is in frame
insertions = 0
deletions = 0
for chr, num in alignment.cigar:
if chr == 'I':
insertions += num
elif chr == 'D':
deletions += num
# Next grab the alignment statistics
string = StringIO()
alignment.dump(out=string)
dump = string.getvalue()
string.close()
# If it's not a near perfect match, then the quality of the assembly may not be good
m = match_regex.search(dump)
if m:
percent = int(m.group('percent'))
if percent < 99:
# print('Percent matching %d' % percent)
# print(dump)
logging.debug('%s: low percent matching %d' %
(fusion_name, percent))
return
# If the fusion transcript passes these filters, then grab the bounds of the alignment
s = bounds_regex.search(dump)
if s:
qstart = int(s.group('qstart')) - 1 # Make zero-based
qstop = int(s.group('qstop'))
# If the end of the fusion transcript doesn't align, then skip this transcript
if mode == 'donor' and qstop != len(query):
logging.debug(
'%s: donor alignment does not include end of sequence' %
fusion_name)
return
elif mode == 'acceptor' and qstart != 0:
logging.debug(
'%s: acceptor alignment does not include start of sequence' %
fusion_name)
# print('Acceptor doesn\'t start at one')
# print(dump)
return
rstart = int(s.group('rstart')) - 1 # Make zero-based
rstop = int(s.group('rstart'))
return alignment_stats(qstart, qstop, rstart, rstop, insertions,
deletions)
else:
return
import swalign
import subprocess
import sys
from Bio.SeqIO import convert as bio_convert
# Global Parameters
MATCH = 2
MISMATCH = -1
SW_SCORE = swalign.NucleotideScoringMatrix(MATCH, MISMATCH)
SMITH_WATERMAN = swalign.LocalAlignment(SW_SCORE)
def _read_fasta(fp):
name = None
seq = []
for line in fp:
line = line.rstrip()
if line.startswith(">"):
if name:
yield (name, ''.join(seq))
name, seq = line, []
else:
seq.append(line)
if name:
yield (name, ''.join(seq))
def _missing_elements(nums):
nums = list(map(int, nums))
def get_smith_waterman():
match = 4
mismatch = -1
scoring = swalign.NucleotideScoringMatrix(match, mismatch)
return swalign.LocalAlignment(scoring, globalalign=True)
import sys
import swalign
scoring = swalign.NucleotideScoringMatrix()
sw = swalign.LocalAlignment(scoring)
def selfanneal(s):
aln = sw.align(s, swalign.revcomp(s))
return aln.score
if __name__ == "__main__":
for s in sys.argv[1:]:
print(selfanneal)
Note 2: This isn't appropriate for color-space FASTQ files with a prefix base
included in the read sequence, since it trims an equal number of bases
from the sequence and quality FASTQ lines.
'''
import sys
import os
import gzip
from ngsutils.support import revcomp, FASTA
from ngsutils.fastq import FASTQ
import swalign
sw = swalign.LocalAlignment(swalign.NucleotideScoringMatrix(2, -1))
def fastx_barcode_split(reader,
outtempl,
barcodes,
edits=0,
pos=0,
allow_revcomp=False,
gzip_output=False,
stats_fname=None):
'''
Split FAST[QA] reads from {fname} using {barcodes} (hash) to write them to
output files named like {templ}.
'''
#!/usr/bin/env python2
import swalign
from . import generic_functions
# global paramaters
SW_SCORE = swalign.NucleotideScoringMatrix(2, -1)
SMITH_WATERMAN = swalign.LocalAlignment(SW_SCORE)
def read_fasta(fp):
name, seq = None, []
for line in fp:
line = line.rstrip()
if line.startswith(">"):
if name: yield (name, ''.join(seq))
name, seq = line, []
else:
seq.append(line)
if name: yield (name, ''.join(seq))
def grab_anarci_numbers(anarci_file):
start = False
alpha_num = []
beta_num = []
alpha_seq = ""
beta_seq = ""
previous_number = ""
