def makeAdaptorFasta(infile, outfile):
'''Make a single fasta file for each sample of all contaminant adaptor
sequences for removal
'''
PipelinePreprocess.makeAdaptorFasta(
infile=infile,
outfile=outfile,
track=re.match(REGEX_TRACK, infile).groups()[0],
dbh=connect(),
contaminants_file=PARAMS['contaminants'])
def makeAdaptorFasta(infile, outfile):
'''Make a single fasta file for each sample of all contaminant adaptor
sequences for removal
'''
PipelinePreprocess.makeAdaptorFasta(
infile=infile,
outfile=outfile,
track=re.match(REGEX_TRACK, infile).groups()[0],
dbh=connect(),
contaminants_file=PARAMS['contaminants'])
def processReadsGSE65525(infile, outfile):
''' process the reads with trimmomatic as per Klein et al 2015 '''
track = P.snip(os.path.basename(infile), ".fastq.gz")
threads = 1
job_memory = "7G"
# as per Allon et al 2015
trimmomatic_options = "LEADING:28 SLIDINGWINDOW:4:20 MINLEN:19"
m = PipelinePreprocess.MasterProcessor(threads=threads)
m.add(PipelinePreprocess.Trimmomatic(trimmomatic_options, threads=threads))
statement = m.build((infile, ), "GSE65525/processed.dir/trimmed-", track)
P.run()
def processReads(infile, outfile):
'''process reads from .fastq or .sra files.
Tasks specified in PREPROCESSTOOLS are run in order
'''
job_threads = PARAMS["general_threads"]
job_options = "-l mem_free=%s" % PARAMS["general_memory"]
m = PipelinePreprocess.Preprocessor()
statement = m.build((infile,), outfile, PREPROCESSTOOLS)
print statement
P.run()
def processReads(infile, outfiles):
'''process reads from .fastq and other sequence files.
'''
trimmomatic_options = PARAMS["trimmomatic_options"]
if PARAMS["trimmomatic_adapter"]:
trimmomatic_options = " ILLUMINACLIP:%s:%s:%s:%s:%s:%s " % (
PARAMS["trimmomatic_adapter"],
PARAMS["trimmomatic_mismatches"],
PARAMS["trimmomatic_p_thresh"],
PARAMS["trimmomatic_c_thresh"],
PARAMS["trimmomatic_min_adapter_len"],
PARAMS["trimmomatic_keep_both_reads"]) + trimmomatic_options
if PARAMS["auto_remove"]:
trimmomatic_options = " ILLUMINACLIP:%s:%s:%s:%s:%s:%s " % (
"contaminants.fasta",
PARAMS["trimmomatic_mismatches"],
PARAMS["trimmomatic_p_thresh"],
PARAMS["trimmomatic_c_thresh"],
PARAMS["trimmomatic_min_adapter_len"],
PARAMS["trimmomatic_keep_both_reads"]) + trimmomatic_options
job_threads = PARAMS["threads"]
job_memory = "12G"
track = re.match(REGEX_TRACK, infile).groups()[0]
m = PipelinePreprocess.MasterProcessor(
save=PARAMS["save"],
summarize=PARAMS["summarize"],
threads=PARAMS["threads"])
for tool in P.asList(PARAMS["preprocessors"]):
if tool == "fastx_trimmer":
m.add(PipelinePreprocess.FastxTrimmer(
PARAMS["fastx_trimmer_options"],
threads=PARAMS["threads"]))
elif tool == "trimmomatic":
m.add(PipelinePreprocess.Trimmomatic(
trimmomatic_options,
threads=PARAMS["threads"]))
elif tool == "sickle":
m.add(PipelinePreprocess.Sickle(
PARAMS["sickle_options"],
threads=PARAMS["threads"]))
elif tool == "trimgalore":
m.add(PipelinePreprocess.Trimgalore(
PARAMS["trimgalore_options"],
threads=PARAMS["threads"]))
elif tool == "flash":
m.add(PipelinePreprocess.Flash(
PARAMS["flash_options"],
threads=PARAMS["threads"]))
elif tool == "reversecomplement":
m.add(PipelinePreprocess.ReverseComplement(
PARAMS["reversecomplement_options"]))
elif tool == "pandaseq":
m.add(PipelinePreprocess.Pandaseq(
PARAMS["pandaseq_options"],
threads=PARAMS["threads"]))
elif tool == "cutadapt":
cutadapt_options = PARAMS["cutadapt_options"]
if PARAMS["auto_remove"]:
cutadapt_options += " -a file:contaminants.fasta "
m.add(PipelinePreprocess.Cutadapt(
cutadapt_options,
threads=PARAMS["threads"],
untrimmed=PARAMS['cutadapt_reroute_untrimmed'],
process_paired=PARAMS["cutadapt_process_paired"]))
else:
raise NotImplementedError("tool '%s' not implemented" % tool)
statement = m.build((infile,), "processed.dir/trimmed-", track)
P.run()