def run(self, network, in_data, out_attributes, user_options, num_cores,
outfile):
import os
from genomicode import filelib
from genomicode import parselib
from genomicode import alignlib
from genomicode import config
from genomicode import parallel
log_filenames = _find_output_logs(in_data.identifier)
assert log_filenames
results = {} # dict of sample -> dictionary of output
for filename in log_filenames:
# <path>/<sample>.log
path, file_ = os.path.split(filename)
f, e = os.path.splitext(file_)
assert e == ".log"
sample = f
results[sample] = alignlib.parse_bowtie1_output(filename)
# Make table where the rows are the samples and the columns
# are the statistics.
all_samples = sorted(results)
table = []
header = "Sample", "Aligned Reads", "Total Reads", "Perc Aligned"
table.append(header)
for sample in all_samples:
stats = results[sample]
total_reads = stats["reads_processed"]
aligned_reads = stats["aligned_reads"]
perc_aligned = float(aligned_reads) / total_reads * 100
x1 = parselib.pretty_int(aligned_reads)
x2 = parselib.pretty_int(total_reads)
x3 = "%.2f%%" % perc_aligned
x = sample, x1, x2, x3
table.append(x)
# Write out the table as text file.
TXT_FILE = "summary.txt"
handle = open(TXT_FILE, 'w')
for x in table:
print >> handle, "\t".join(x)
handle.close()
txt2xls = filelib.which_assert(config.txt2xls)
os.system("%s -b %s > %s" %
(parallel.quote(txt2xls), TXT_FILE, outfile))
def change_directory(cache_path, arg):
import os
from genomicode import jmath
from genomicode import filelib
from genomicode import parallel
module_paths = _list_module_directories(cache_path)
if jmath.is_int(arg):
# Go to the ith most recent module_path
i = int(arg)
assert i > 0
assert i < len(module_paths), "There are only %d modules" % \
len(module_paths)
desired_path = module_paths[i - 1]
else:
x = [x for x in module_paths if x.find(arg) >= 0]
assert x, "I could not find path containing: %s" % arg
desired_path = x[0]
x = os.path.join(cache_path, desired_path)
print "cd %s" % parallel.quote(x)
def run(self, network, in_data, out_attributes, user_options, num_cores,
out_path):
import os
from genomicode import filelib
from genomicode import parallel
from Betsy import module_utils as mlib
filenames = mlib.find_fastq_files(in_data.identifier)
assert filenames, "FASTQ files not found: %s" % in_data.identifier
filelib.safe_mkdir(out_path)
metadata = {}
fastqc = mlib.findbin("fastqc")
fastqc_q = parallel.quote(fastqc)
commands = [
"%s --outdir=%s --extract %s" % (fastqc_q, out_path, x)
for x in filenames
]
metadata["commands"] = commands
metadata["num_cores"] = num_cores
#commands = ["ls > %s" % x for x in filenames]
parallel.pshell(commands, max_procs=num_cores)
# Fastqc generates files:
# <file>_fastqc/
# <file>_fastqc.zip
# The contents of the .zip file are identical to the directories.
# If this happens, then delete the .zip files because they are
# redundant.
files = os.listdir(out_path)
filenames = [os.path.join(out_path, x) for x in files]
for filename in filenames:
zip_filename = "%s.zip" % filename
if os.path.exists(zip_filename):
os.unlink(zip_filename)
def run(self, network, antecedents, out_attributes, user_options,
num_cores, out_path):
import os
from genomicode import parallel
from genomicode import hashlib
from genomicode import filelib
from genomicode import config
from Betsy import module_utils
bam_node, group_node = antecedents
bam_path = module_utils.check_inpath(bam_node.identifier)
sample_groups = module_utils.read_sample_group_file(
group_node.identifier)
# Get options.
treat_sample = module_utils.get_user_option(user_options,
"treatment_sample",
not_empty=True)
control_sample = module_utils.get_user_option(user_options,
"control_sample")
genome_size = module_utils.get_user_option(user_options,
"macs_genome",
not_empty=True)
shiftsize = module_utils.get_user_option(user_options,
"macs_shiftsize")
if shiftsize:
shiftsize = int(shiftsize)
# Set the name.
name = hashlib.hash_var(treat_sample)
if control_sample:
x = hashlib.hash_var(control_sample)
name = "%s_vs_%s" % (treat_sample, x)
# Make sure the samples exist.
samples = [x[1] for x in sample_groups]
assert treat_sample in samples, "Unknown sample: %s" % treat_sample
if control_sample:
assert control_sample in samples, \
"Unknown sample: %s" % control_sample
# Find the BAM files.
treat_filename = find_bam_file(bam_path, treat_sample, sample_groups)
assert treat_filename, "Missing bam file for %s" % treat_sample
control_filename = None
if control_sample:
control_filename = find_bam_file(bam_path, control_sample,
sample_groups)
assert control_filename, "Missing bam file for %s" % control_sample
cmd = make_macs14_command(treat_filename,
control_filename,
name=name,
genome_size=genome_size,
shiftsize=shiftsize,
save_bedgraph_file=True)
parallel.sshell(cmd, path=out_path)
# Run Rscript on the model, if one was generated.
model_file = os.path.join(out_path, "%s_model.r" % name)
if os.path.exists(model_file):
Rscript = filelib.which_assert(config.Rscript)
cmd = [parallel.quote(Rscript), model_file]
parallel.sshell(cmd, path=out_path)
files = [
"%s_peaks.xls" % name,
"%s_summits.bed" % name,
]
filenames = [os.path.join(out_path, x) for x in files]
filelib.assert_exists_nz_many(filenames)
def run(self, network, in_data, out_attributes, user_options, num_cores,
outfile):
import os
from genomicode import filelib
from genomicode import parselib
from genomicode import alignlib
from genomicode import config
from genomicode import parallel
align_node = in_data
x = filelib.list_files_in_path(align_node.identifier,
endswith="align_summary.txt")
align_filenames = x
assert align_filenames, "Missing align_summary.txt"
results = {} # dict of sample -> dictionary of output
for filename in align_filenames:
# Names must in the format:
# <path>/<sample>.tophat/alignment_summary.txt
# full_path <path>/<sample>.tophat
# path <path>
# tophat_dir <sample>.tophat
# file_ accepted_hits.bam
# sample <sample>
full_path, file_ = os.path.split(filename)
path, tophat_dir = os.path.split(full_path)
assert file_ == "align_summary.txt"
assert tophat_dir.endswith(".tophat")
sample = tophat_dir[:-7]
x = alignlib.parse_tophat_align_summary(filename)
results[sample] = x
# Make table where the rows are the samples and the columns
# are the statistics.
all_samples = sorted(results)
table = []
header = "Sample", "Aligned Reads", "Total Reads", "Perc Aligned"
table.append(header)
for sample in all_samples:
stats = results[sample]
total_reads = stats["reads_processed"]
aligned_reads = stats["aligned_reads"]
perc_aligned = float(aligned_reads) / total_reads * 100
x1 = parselib.pretty_int(aligned_reads)
x2 = parselib.pretty_int(total_reads)
x3 = "%.2f%%" % perc_aligned
x = sample, x1, x2, x3
table.append(x)
# Write out the table as text file.
TXT_FILE = "summary.txt"
handle = open(TXT_FILE, 'w')
for x in table:
print >> handle, "\t".join(x)
handle.close()
txt2xls = filelib.which_assert(config.txt2xls)
os.system("%s -b %s > %s" %
(parallel.quote(txt2xls), TXT_FILE, outfile))
def sq(name):
# quote for a shell command.
from genomicode import parallel
return parallel.quote(name)