def main():
args = get_args()
# setup logging
log, my_name = setup_logging(args)
log.info("Output files are in {}".format(args.output_dir))
reads = glob.glob("{}/*.fq.gz".format(args.input_dir))
reads.sort()
read_pairs = pairwise(reads)
for sample in read_pairs:
read_1_filename = os.path.basename(sample[0])
read_2_filename = os.path.basename(sample[1])
read_1_sample_name = re.search("(.*).\d.\d.fq.gz",
read_1_filename).groups()[0]
read_2_sample_name = re.search("(.*).\d.\d.fq.gz",
read_2_filename).groups()[0]
#pdb.set_trace()
assert read_1_sample_name == read_2_sample_name, IOError(
"Input fastq names do not match (or reads are not paired)")
sample_name = read_1_sample_name
log.info("Creating output and symlinking {}".format(sample_name))
outdir = os.path.join(args.output_dir, sample_name)
os.mkdir(outdir)
for fastq in sample:
os.symlink(fastq, os.path.join(outdir, os.path.basename(fastq)))
# end
text = " Completed {} ".format(my_name)
log.info(text.center(65, "="))
def main():
args = get_args()
# setup logging
log, my_name = setup_logging(args)
reference_dir, reference = os.path.split(args.input_reference)
# create samtools index
samtools.index(log, args.input_bam)
samtools.faidx(log, args.input_reference)
# create picard reference dictionary
picard.create_reference_dict(log, reference, reference_dir, args.input_reference)
# run GATK steps
intervals = gatk.get_merged_intervals(log, args.input_reference, args.input_bam, args.cores, args.output_dir)
realigned_bam = gatk.realign_bam(log, args.input_reference, args.input_bam, intervals, args.output_dir)
raw_snps_vcf = gatk.call_snps(log, args.input_reference, realigned_bam, args.cores, args.output_dir)
raw_indels_vcf = gatk.call_indels(log, args.input_reference, realigned_bam, args.cores, args.output_dir)
filtered_variants_vcf = gatk.variant_filtration(log, args.input_reference, realigned_bam, raw_snps_vcf, raw_indels_vcf, args.output_dir)
# output a file with only passing SNPS
log.info("Creating a file of PASSING SNP calls")
passing = os.path.splitext(filtered_variants_vcf)[0] + ".PASSING.vcf"
with open(filtered_variants_vcf, 'r') as infile:
with open(passing, 'w') as outfile:
for line in infile:
if line.startswith("#"):
outfile.write(line)
else:
ls = line.strip().split("\t")
if ls[6] == "PASS":
outfile.write(line)
# end
text = " Completed {} ".format(my_name)
log.info(text.center(65, "="))
def main():
args = get_args()
# setup logging
log, my_name = setup_logging(args)
interval_list = get_interval_file(log, args.input_reference)
# get coverage
gatk.coverage(log, args.input_reference, args.input_bam, interval_list, args.output_dir)
# end
text = " Completed {} ".format(my_name)
log.info(text.center(65, "="))
def main():
args = get_args()
# setup logging
log, my_name = setup_logging(args)
all_bams = get_all_bams(args.input_dir, args.follow_links)
sample = os.path.basename(args.input_dir)
picard.merge_many_bams(log, sample, all_bams, args.output_dir)
# end
text = " Completed {} ".format(my_name)
log.info(text.center(65, "="))
def main():
args = get_args()
# setup logging
log, my_name = setup_logging(args)
log.info("{}".format('-' * 65))
cnt_loci = Counter()
cnt_status = Counter()
with open(args.input_vcf, 'r') as infile:
for line in infile:
if line.startswith("#"):
pass
else:
ls = line.strip().split("\t")
cnt_status[ls[6]] += 1
if ls[6] == "PASS":
cnt_loci[ls[0]] += 1
snps_per_locus = numpy.array([v[1] for v in cnt_loci.iteritems()])
ordered_status = cnt_status.keys()
ordered_status.sort()
for status in ordered_status:
log.info("{0: <6}\tSNPs with {1}".format(cnt_status[status], status))
log.info("{}".format('-' * 65))
log.info("Total count of PASS loci\t = {}".format(len(snps_per_locus)))
log.info("Total count of PASS SNPs\t = {}".format(sum(snps_per_locus)))
log.info("Mean PASS SNPs per locus\t = {}".format(round(numpy.mean(snps_per_locus), 2)))
confidence_interval = 1.96 * (numpy.std(snps_per_locus, ddof=1) / numpy.sqrt(len(snps_per_locus)))
log.info("95 CI PASS SNPs per locus\t = {}".format(round(confidence_interval, 2)))
log.info("Max PASS SNPs per locus\t = {}".format(numpy.min(snps_per_locus)))
log.info("Min PASS SNPs per locus\t = {}".format(numpy.max(snps_per_locus)))
if args.output_file is not None:
with open(args.output_file, "w") as outfile:
outfile.write("locus,count\n")
for locus, cnt in cnt_loci.iteritems():
outfile.write("{},{}\n".format(locus, cnt))
# end
text = " Completed {} ".format(my_name)
log.info(text.center(65, "="))
def main():
# get args and options
args = get_args()
# setup logging
log, my_name = setup_logging(args)
text = " Starting {} ".format(my_name)
log.info(text.center(65, "="))
# get the config file data
conf = ConfigParser.ConfigParser(allow_no_value=True)
conf.optionxform = str
conf.read(args.config)
# get the input data
log.info("Getting input filenames and creating output directories")
reference, individuals = get_input_data(log, conf, args.output)
flowcells = dict(conf.items("flowcell"))
if args.bwa_mem:
log.info("You are running BWA-MEM")
for indiv in individuals:
bam, bam_se = False, False
sample, dir = indiv
# pretty print taxon status
text = " Processing {} ".format(sample)
log.info(text.center(65, "-"))
# make a directory for sample-specific assemblies
sample_dir = os.path.join(args.output, sample)
os.makedirs(sample_dir)
# determine how many files we're dealing with
fastq = get_input_files(dir, args.subfolder, log)
if fastq.r1 and fastq.r2:
# bwa align r1 and r2
if args.bwa_mem:
bam = bwa.mem_pe_align(log, sample, sample_dir, reference, args.cores, fastq.r1, fastq.r2)
else:
bam = bwa.pe_align(log, sample, sample_dir, reference, args.cores, fastq.r1, fastq.r2)
# clean the bam up (MAPq 0 and trim overlapping reads)
bam = picard.clean_up_bam(log, sample, sample_dir, bam, "pe")
# get flowcell id
fc = flowcells[sample]
bam = picard.add_rg_header_info(log, sample, sample_dir, fc, bam, "pe")
### !!! We are not removing duplicates because we expect them and
### !!! and they have been filtered prior to alignment (after demuxing)
if fastq.singleton:
# bwa align singleton reads
if args.bwa_mem:
bam_se = bwa.mem_se_align(log, sample, sample_dir, reference, args.cores, fastq.singleton)
else:
bam_se = bwa.se_align(log, sample, sample_dir, reference, args.cores, fastq.singleton)
# clean the bam up (MAPq 0 and trim overlapping reads)
bam_se = picard.clean_up_bam(log, sample, sample_dir, bam_se, "se")
# get flowcell id
fc = flowcells[sample]
bam_se = picard.add_rg_header_info(log, sample, sample_dir, fc, bam_se, "se")
### !!! We are not removing duplicates because we expect them and
### !!! and they have been filtered prior to alignment (after demuxing)
if bam and bam_se:
bam = picard.merge_two_bams(log, sample, sample_dir, bam, bam_se)
elif bam_se and not bam:
bam = bam_se
if not bam:
raise IOError("There is no BAM file. Check bwa log files for problems.")
# end
text = " Completed {} ".format(my_name)
log.info(text.center(65, "="))
