def mapReads(infiles, outfile):
'''Map reads to the genome using BWA '''
to_cluster = True
job_options = "-pe dedicated %i -R y" % PARAMS["bwa_threads"]
m = PipelineMapping.BWA()
statement = m.build((infiles,), outfile)
P.run()
def mapReads(infiles, outfile):
'''Map reads to the genome using BWA (output=SAM), convert to BAM, sort and index BAM file '''
to_cluster = USECLUSTER
job_options = "-pe dedicated 2 -R y -l mem_free=8G"
track = P.snip(os.path.basename(outfile), ".bam")
m = PipelineMapping.BWA(remove_unique=PARAMS["bwa_remove_non_unique"])
statement = m.build((infiles, ), outfile)
P.run()
def mapReads(infiles, outfile):
'''Map reads to the genome using BWA (output=SAM), convert to BAM,
sort and index BAM file, generate alignment statistics and
deduplicate using Picard
'''
job_options = "-pe dedicated 2 -l mem_free=8G"
track = P.snip(os.path.basename(outfile), ".bam")
m = PipelineMapping.BWA(remove_unique=PARAMS["bwa_remove_non_unique"],
align_stats=True,
dedup=True)
statement = m.build((infiles, ), outfile)
P.run()
def mapReads(infile, outfile):
'''Map reads to the genome using BWA, sort and index BAM file,
generate alignment statistics and deduplicate using Picard'''
job_threads = PARAMS["bwa_threads"]
job_memory = PARAMS["bwa_memory"]
if PARAMS["bwa_algorithm"] == "aln":
m = PipelineMapping.BWA(
remove_non_unique=PARAMS["bwa_remove_non_unique"],
strip_sequence=False)
elif PARAMS["bwa_algorithm"] == "mem":
m = PipelineMapping.BWAMEM(
remove_non_unique=PARAMS["bwa_remove_non_unique"],
strip_sequence=False)
else:
raise ValueError("bwa algorithm '%s' not known" % algorithm)
statement = m.build((infile, ), outfile)
print(statement)
P.run()
def mapBWAAgainstGenesetGSE53638(infiles, outfile):
''' map reads using BWA against transcriptome data
bwa parameterised according to soumillon et al 2014:
-l 24 = seed length - 24 bp
-k 2 = default number of mismatches allowed in seed - 2
-n 0.04 = default percentage of mismatches allowed across read - 4%
non-unique alignments will NOT be removed from the final bam
'''
infile, reference = infiles
job_threads = 2
job_options = "-l mem_free=1.9G"
bwa_aln_options = "-l 24 -k 2 -n 0.04"
bwa_index_dir = os.path.abspath(os.path.dirname(reference))
genome = P.snip(os.path.basename(reference), ".sa")
bwa_threads = job_threads
bwa_samse_options = ""
m = PipelineMapping.BWA(remove_non_unique=0, strip_sequence=0, set_nh=1)
statement = m.build((infile, ), outfile)
P.run()
def mapReadsWithBwaAgainstExpectedContigs(infiles, outfile):
'''
map reads against contigs with bowtie
'''
to_cluster = True
index_dir = os.path.dirname(outfile)
genome = os.path.basename(
re.search(".*R[0-9]*", infiles[0]).group(0) +
".filtered.contigs.expected.fa")
track = P.snip(genome, ".filtered.contigs.expected.fa")
fastq = [
infile for infile in infiles[1]
if P.snip(infile, ".fastq.1.gz") == track
][0]
job_options = " -l mem_free=%s" % (PARAMS["bwa_memory"])
bwa_index_dir = index_dir
bwa_aln_options = PARAMS["bwa_aln_options"]
bwa_sampe_options = PARAMS["bwa_sampe_options"]
bwa_threads = PARAMS["bwa_threads"]
m = PipelineMapping.BWA(remove_non_unique=True)
statement = m.build((fastq, ), outfile)
P.run()
def mapReads(infiles, outfile):
'''Map reads to the genome using BWA '''
job_threads = PARAMS["bwa_threads"]
m = PipelineMapping.BWA()
statement = m.build((infiles, ), outfile)
P.run()