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surveyor.py
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import argparse, os
import pysam, pyfaidx
from random_pos_generator import RandomPositionGenerator
import numpy as np
MAX_READS = 1000
GEN_DIST_SIZE = 100000
MAX_ACCEPTABLE_IS = 20000
cmd_parser = argparse.ArgumentParser(description='TranSurVeyor, a transposition caller.')
cmd_parser.add_argument('bamFile', help='Input bam file.')
cmd_parser.add_argument('workdir', help='Working directory for Surveyor to use.')
cmd_parser.add_argument('reference', help='Reference genome in FASTA format.')
cmd_parser.add_argument('--threads', type=int, default=1, help='Number of threads to be used.')
cmd_parser.add_argument('--simple-rep', help='simpleRepeats file from UCSC table browser.')
cmd_parser.add_argument('--samtools', help='Samtools path.', default='samtools')
cmd_parser.add_argument('--bwa', default='bwa', help='BWA path.')
cmd_parser.add_argument('--minSVLen', type=int, default=50, help='Min SV length.')
cmd_parser.add_argument('--maxSCDist', type=int, default=10, help='Max SC distance.')
cmd_args = cmd_parser.parse_args()
# Create config file in workdir
config_file = open(cmd_args.workdir + "/config.txt", "w")
config_file.write("threads %d\n" % cmd_args.threads)
config_file.write("min_sv_len %d\n" % cmd_args.minSVLen)
config_file.write("max_sc_dist %d\n" % cmd_args.maxSCDist)
if cmd_args.simple_rep != None:
config_file.write("simple_rep %s\n" % cmd_args.simple_rep)
# Find read length
read_len = 0
bam_file = pysam.AlignmentFile(cmd_args.bamFile)
for i, read in enumerate(bam_file.fetch(until_eof=True)):
if i > MAX_READS: break
read_len = max(read_len, read.query_length)
# Generate general distribution of insert sizes
contig_map = open("%s/contig_map" % cmd_args.workdir, "w")
num_contigs = 0
reference_fa = pyfaidx.Fasta(cmd_args.reference)
i = 1
for k in reference_fa.keys():
contig_map.write("%s %d\n" % (k, i));
i += 1
num_contigs += 1
contig_map.close();
rand_pos_gen = RandomPositionGenerator(reference_fa)
random_positions = []
for i in range(1,1000001):
if i % 100000 == 0: print i, "random positions generated."
random_positions.append(rand_pos_gen.next())
with open("%s/random_pos.txt" % cmd_args.workdir, "w") as random_pos_file:
for random_pos in random_positions:
random_pos_file.write("%s %d\n" % random_pos)
general_dist = []
avg_depth = 0
samplings = 0
rnd_i = 0
while len(general_dist) < GEN_DIST_SIZE:
chr, pos = random_positions[rnd_i]
rnd_i += 1
if pos > len(reference_fa[chr])-10000:
continue
samplings += 1
i = 0
for read in bam_file.fetch(contig=chr, start=pos, end=pos+10000):
if read.reference_start < pos:
avg_depth += 1
if read.is_proper_pair and not read.is_secondary and not read.is_supplementary and \
0 < read.template_length < MAX_ACCEPTABLE_IS and 'S' not in read.cigarstring and 'S' not in read.get_tag('MC'):
if i > 100: break
i += 1
general_dist.append(read.template_length)
mean_is = np.mean(general_dist)
stddev_is = np.std(general_dist)
avg_depth = float(avg_depth)/samplings;
print "Average depth:", avg_depth
general_dist = [x for x in general_dist if abs(x-mean_is) < 5*stddev_is]
mean_is = int(np.mean(general_dist))
lower_stddev_is = int(np.sqrt(np.mean([(mean_is-x)**2 for x in general_dist if x < mean_is])))
higher_stddev_is = int(np.sqrt(np.mean([(x-mean_is)**2 for x in general_dist if x > mean_is])))
min_is, max_is = mean_is-3*lower_stddev_is, mean_is+3*higher_stddev_is
print mean_is, min_is, max_is
config_file.write("min_is %d\n" % min_is)
config_file.write("avg_is %d\n" % mean_is)
config_file.write("max_is %d\n" % max_is)
workspace = cmd_args.workdir + "/workspace"
config_file.write("read_len %d\n" % read_len)
config_file.write("avg_depth %f\n" % avg_depth)
config_file.close();
if not os.path.exists(workspace):
os.makedirs(workspace)
read_categorizer_cmd = "./reads_categorizer %s %s" % (cmd_args.bamFile, cmd_args.workdir);
print "Executing:", read_categorizer_cmd
os.system(read_categorizer_cmd)
clip_consensus_builder_cmd = "./clip_consensus_builder %s %s" % (cmd_args.workdir, cmd_args.reference)
print "Executing:", clip_consensus_builder_cmd
os.system(clip_consensus_builder_cmd)
bwa_cmd = "%s mem -t %d %s %s/CLIPS.fa | %s view -b > %s/CLIPS.bam" \
% (cmd_args.bwa, cmd_args.threads, cmd_args.reference, workspace, cmd_args.samtools, workspace)
print "Executing:", bwa_cmd
os.system(bwa_cmd)
pysam.sort("-@", str(cmd_args.threads), "-o", "%s/CLIPS.sorted.bam" % workspace, "%s/CLIPS.bam" % workspace)
sc_categorizer_cmd = "./sc_categorizer %s" % cmd_args.workdir
print "Executing:", sc_categorizer_cmd
os.system(sc_categorizer_cmd)
for i in range(1, num_contigs+1):
for dt in ("OW", "SI", "LI"):
fname = "%s/%d-%s.bam" % (workspace, i, dt)
sorted_fname = "%s/%d-%s.sorted.bam" % (workspace, i, dt)
print "Sorting", fname
pysam.sort("-@", str(cmd_args.threads), "-o", sorted_fname, fname)
os.rename(sorted_fname, fname)
clusterer_cmd = "./clusterer %s" % cmd_args.workdir
print "Executing:", clusterer_cmd
os.system(clusterer_cmd)
add_filtering_info_cmd = "./add_filtering_info %s %s" % (cmd_args.bamFile, cmd_args.workdir)
print "Executing:", add_filtering_info_cmd
os.system(add_filtering_info_cmd)