def main(argv=None):
settings, args = process_command_line(argv)
# Read the read names and positions
read_5ps = {}
read_3ps = {}
read_genes = {}
genome = SeqIO.read(settings.genome, "fasta").seq
for line in csv.reader(open(settings.list_reads), delimiter="\t"):
if line[7] == "single":
continue
read_5ps[line[6]] = [int(line[1]) - 1, line[2]]
read_3ps[line[6]] = [int(line[4]) - 1, line[5]]
read_genes[line[6]] = [] # [line[1], line[4]]
# Read the bam file and return the long sequences
r1_seqs, r2_seqs = get_reads_seqs(pysam.Samfile(settings.bamfile), read_genes.keys())
outer = csv.writer(open(settings.printto, "w"), delimiter="\t")
pssm_before = defaultdict(lambda: defaultdict(int))
pssm_after = defaultdict(lambda: defaultdict(int))
# For each read find the overlap, if exists and find the fusion point
for rname in set(r1_seqs.keys()) & set(r2_seqs.keys()):
s1, overlap, s2 = find_overlap(r2_seqs[rname], r1_seqs[rname])
if len(overlap) < settings.overlap:
continue
# print rname
# print "%s %s %s"%(s1, overlap, s2)
side_5p_len = extend_alignment(s1 + overlap + s2, read_5ps[rname][0], 0, False, read_5ps[rname][1], genome)
side_3p_len = extend_alignment(s1 + overlap + s2, 0, read_3ps[rname][0], True, read_3ps[rname][1], genome)
# print "%d %d %d %d %d"%(side_5p_len, side_3p_len, len(s1), len(overlap), len(s2))
if side_5p_len + side_3p_len == len(s1) + len(overlap) + len(s2):
# Report this as a fusion point
f1_point = read_5ps[rname][0] + side_5p_len - 1
f1_seq = genome[read_5ps[rname][0] : f1_point + 1]
# f1_seq = genome[f1_point-settings.width: f1_point+1]
if read_5ps[rname][1] == "-":
f1_point = read_5ps[rname][0] - side_5p_len + 1
f1_seq = genome[f1_point : read_5ps[rname][0] + 1].reverse_complement()
# f1_seq = genome[f1_point:f1_point+settings.width+1].reverse_complement()
f2_point = read_3ps[rname][0] - side_3p_len + 1
f2_seq = genome[f2_point : read_3ps[rname][0] + 1]
# f2_seq = genome[f2_point:f2_point+settings.width+1]
if read_3ps[rname][1] == "-":
f2_point = read_3ps[rname][0] + side_3p_len - 1
f2_seq = genome[read_3ps[rname][0] : f2_point + 1].reverse_complement()
# f2_seq = genome[f2_point-settings.width:f2_point+1].reverse_complement()
acc1 = computeACC((s1 + overlap)[:side_5p_len], winlen=1)
acc2 = computeACC((overlap + s2)[-side_3p_len:], winlen=1)
outer.writerow(
[rname]
+ read_genes[rname]
+ read_5ps[rname]
+ read_3ps[rname]
+ [f1_point, f1_seq, f2_point, f2_seq]
+ acc1[-settings.width :]
+ acc2[: settings.width]
)
# Add the sequences to the pssm
for i, nt in enumerate(f1_seq):
pssm_before[len(f1_seq) - i][nt] += 1
for i, nt in enumerate(f2_seq):
pssm_after[i][nt] += 1
print "%sNN%s" % (f1_seq, f2_seq)
# print pssm_before
# print pssm_after
for nt in ("A", "C", "G", "T"):
for i in range(settings.width + 1, 0, -1):
sys.stdout.write("%s\t" % pssm_before[i][nt])
sys.stdout.write("\n")
for nt in ("A", "C", "G", "T"):
for i in range(settings.width + 1):
sys.stdout.write("%s\t" % pssm_after[i][nt])
sys.stdout.write("\n")
return 0 # success
def run(argv=None):
try:
settings, args = process_command_line(argv)
except BaseException:
return 1
FRAG_1_SEQ_INDEX = 6
FRAG_2_SEQ_INDEX = 8
FRAG_1_SCORE_START = 9
positive_pos_score_list = []
negative_pos_score_list = []
# Get the reads before and after ligation point
with open(settings.reads_file, "rb") as fl:
for line in fl:
row = line.replace("\n", "").split("\t")
FRAG_2_SCORE_START = FRAG_1_SCORE_START + len(row[FRAG_1_SEQ_INDEX])
if len(row[FRAG_1_SEQ_INDEX]) + len(row[FRAG_2_SEQ_INDEX]) != len(row[FRAG_1_SCORE_START:]):
print "-" * 100
print "skipped row"
print row[FRAG_1_SEQ_INDEX]
print row[FRAG_2_SEQ_INDEX]
print "-" * 100
continue
negative_pos_score_list.append(row[FRAG_1_SEQ_INDEX])
positive_pos_score_list.append(row[FRAG_2_SEQ_INDEX])
NUM_OF_BASES_TO_EXCHANGE = 4
perms = generate_permutation(NUM_OF_BASES_TO_EXCHANGE)
scores_per_fragment = []
i = 1
# Calculate the score for each possible end for the fragments before
# the ligation point
for frag in negative_pos_score_list:
base_seq = frag[:-NUM_OF_BASES_TO_EXCHANGE]
for extension in perms:
scores_per_fragment.append(computeACC(base_seq + extension, winlen=1))
row = [str(i), base_seq + extension] + [str(val) for val in scores_per_fragment[-1]]
print "\t".join(row)
i += 1
# calculate statistics for last postions
postions_scores = [[] for i in range(NUM_OF_BASES_TO_EXCHANGE)]
for score_list in scores_per_fragment:
for i in range(NUM_OF_BASES_TO_EXCHANGE):
postions_scores[i].append(score_list[-NUM_OF_BASES_TO_EXCHANGE + i])
for i in range(NUM_OF_BASES_TO_EXCHANGE):
print "position:"
print "-" * 50
print "mean:", np.mean(postions_scores[i])
print "median:", np.median(postions_scores[i])
print "max:", max(postions_scores[i])
print "min:", min(postions_scores[i])
print "\n"
print "*" * 100
print "*" * 100
print "finished negatives, starting positives"
print "*" * 100
print "*" * 100
# Calculate the score for each possible end for the fragments before
# the ligation point
for frag in positive_pos_score_list:
base_seq = frag[:-NUM_OF_BASES_TO_EXCHANGE]
for extension in perms:
scores_per_fragment.append(computeACC(base_seq + extension, winlen=1))
row = [str(i), base_seq + extension] + [str(val) for val in scores_per_fragment[-1]]
print "\t".join(row)
i += 1
# calculate statistics for first postions
postions_scores = [[] for i in range(NUM_OF_BASES_TO_EXCHANGE)]
for score_list in scores_per_fragment:
for i in range(NUM_OF_BASES_TO_EXCHANGE):
postions_scores[i].append(score_list[i])
for i in range(NUM_OF_BASES_TO_EXCHANGE):
print "position:"
print "-" * 50
print "mean:", np.mean(postions_scores[i])
print "median:", np.median(postions_scores[i])
print "max:", max(postions_scores[i])
print "min:", min(postions_scores[i])
print "\n"
return 0
def main(argv=None):
settings, args = process_command_line(argv)
# Read the read names and positions
read_5ps = {}
read_3ps = {}
read_genes = {}
genome = SeqIO.read(settings.genome, 'fasta').seq
for line in csv.reader(open(settings.list_reads), delimiter='\t'):
if line[7] == 'single':
continue
read_5ps[line[6]] = [int(line[1]) - 1, line[2]]
read_3ps[line[6]] = [int(line[4]) - 1, line[5]]
read_genes[line[6]] = [] #[line[1], line[4]]
# Read the bam file and return the long sequences
r1_seqs, r2_seqs = get_reads_seqs(pysam.Samfile(settings.bamfile),
read_genes.keys())
outer = csv.writer(open(settings.printto, 'w'), delimiter='\t')
pssm_before = defaultdict(lambda: defaultdict(int))
pssm_after = defaultdict(lambda: defaultdict(int))
# For each read find the overlap, if exists and find the fusion point
for rname in set(r1_seqs.keys()) & set(r2_seqs.keys()):
s1, overlap, s2 = find_overlap(r2_seqs[rname], r1_seqs[rname])
if len(overlap) < settings.overlap:
continue
# print rname
# print "%s %s %s"%(s1, overlap, s2)
side_5p_len = extend_alignment(s1 + overlap + s2, read_5ps[rname][0],
0, False, read_5ps[rname][1], genome)
side_3p_len = extend_alignment(s1 + overlap + s2, 0,
read_3ps[rname][0], True,
read_3ps[rname][1], genome)
# print "%d %d %d %d %d"%(side_5p_len, side_3p_len, len(s1), len(overlap), len(s2))
if side_5p_len + side_3p_len == len(s1) + len(overlap) + len(s2):
# Report this as a fusion point
f1_point = read_5ps[rname][0] + side_5p_len - 1
f1_seq = genome[read_5ps[rname][0]:f1_point + 1]
# f1_seq = genome[f1_point-settings.width: f1_point+1]
if read_5ps[rname][1] == '-':
f1_point = read_5ps[rname][0] - side_5p_len + 1
f1_seq = genome[f1_point:read_5ps[rname][0] +
1].reverse_complement()
# f1_seq = genome[f1_point:f1_point+settings.width+1].reverse_complement()
f2_point = read_3ps[rname][0] - side_3p_len + 1
f2_seq = genome[f2_point:read_3ps[rname][0] + 1]
# f2_seq = genome[f2_point:f2_point+settings.width+1]
if read_3ps[rname][1] == '-':
f2_point = read_3ps[rname][0] + side_3p_len - 1
f2_seq = genome[read_3ps[rname][0]:f2_point +
1].reverse_complement()
# f2_seq = genome[f2_point-settings.width:f2_point+1].reverse_complement()
acc1 = computeACC((s1 + overlap)[:side_5p_len], winlen=1)
acc2 = computeACC((overlap + s2)[-side_3p_len:], winlen=1)
outer.writerow([rname] + read_genes[rname] + read_5ps[rname] +
read_3ps[rname] +
[f1_point, f1_seq, f2_point, f2_seq] +
acc1[-settings.width:] + acc2[:settings.width])
# Add the sequences to the pssm
for i, nt in enumerate(f1_seq):
pssm_before[len(f1_seq) - i][nt] += 1
for i, nt in enumerate(f2_seq):
pssm_after[i][nt] += 1
print "%sNN%s" % (f1_seq, f2_seq)
# print pssm_before
# print pssm_after
for nt in ('A', 'C', 'G', 'T'):
for i in range(settings.width + 1, 0, -1):
sys.stdout.write("%s\t" % pssm_before[i][nt])
sys.stdout.write("\n")
for nt in ('A', 'C', 'G', 'T'):
for i in range(settings.width + 1):
sys.stdout.write("%s\t" % pssm_after[i][nt])
sys.stdout.write("\n")
return 0 # success
def run(argv=None):
try:
settings, args = process_command_line(argv)
except BaseException:
return 1
FRAG_1_SEQ_INDEX = 6
FRAG_2_SEQ_INDEX = 8
FRAG_1_SCORE_START = 9
positive_pos_score_list = []
negative_pos_score_list = []
# Get the reads before and after ligation point
with open(settings.reads_file, "rb") as fl:
for line in fl:
row = line.replace("\n", "").split("\t")
FRAG_2_SCORE_START = FRAG_1_SCORE_START + len(
row[FRAG_1_SEQ_INDEX])
if len(row[FRAG_1_SEQ_INDEX]) + len(row[FRAG_2_SEQ_INDEX]) != len(
row[FRAG_1_SCORE_START:]):
print "-" * 100
print "skipped row"
print row[FRAG_1_SEQ_INDEX]
print row[FRAG_2_SEQ_INDEX]
print "-" * 100
continue
negative_pos_score_list.append(row[FRAG_1_SEQ_INDEX])
positive_pos_score_list.append(row[FRAG_2_SEQ_INDEX])
NUM_OF_BASES_TO_EXCHANGE = 4
perms = generate_permutation(NUM_OF_BASES_TO_EXCHANGE)
scores_per_fragment = []
i = 1
# Calculate the score for each possible end for the fragments before
# the ligation point
for frag in negative_pos_score_list:
base_seq = frag[:-NUM_OF_BASES_TO_EXCHANGE]
for extension in perms:
scores_per_fragment.append(
computeACC(base_seq + extension, winlen=1))
row = [str(i), base_seq + extension
] + [str(val) for val in scores_per_fragment[-1]]
print "\t".join(row)
i += 1
# calculate statistics for last postions
postions_scores = [[] for i in range(NUM_OF_BASES_TO_EXCHANGE)]
for score_list in scores_per_fragment:
for i in range(NUM_OF_BASES_TO_EXCHANGE):
postions_scores[i].append(score_list[-NUM_OF_BASES_TO_EXCHANGE +
i])
for i in range(NUM_OF_BASES_TO_EXCHANGE):
print "position:"
print "-" * 50
print "mean:", np.mean(postions_scores[i])
print "median:", np.median(postions_scores[i])
print "max:", max(postions_scores[i])
print "min:", min(postions_scores[i])
print "\n"
print "*" * 100
print "*" * 100
print "finished negatives, starting positives"
print "*" * 100
print "*" * 100
# Calculate the score for each possible end for the fragments before
# the ligation point
for frag in positive_pos_score_list:
base_seq = frag[:-NUM_OF_BASES_TO_EXCHANGE]
for extension in perms:
scores_per_fragment.append(
computeACC(base_seq + extension, winlen=1))
row = [str(i), base_seq + extension
] + [str(val) for val in scores_per_fragment[-1]]
print "\t".join(row)
i += 1
# calculate statistics for first postions
postions_scores = [[] for i in range(NUM_OF_BASES_TO_EXCHANGE)]
for score_list in scores_per_fragment:
for i in range(NUM_OF_BASES_TO_EXCHANGE):
postions_scores[i].append(score_list[i])
for i in range(NUM_OF_BASES_TO_EXCHANGE):
print "position:"
print "-" * 50
print "mean:", np.mean(postions_scores[i])
print "median:", np.median(postions_scores[i])
print "max:", max(postions_scores[i])
print "min:", min(postions_scores[i])
print "\n"
return 0
