def runFeatureCounts(annotations_file,
bamfile,
outfile,
nthreads=4,
strand=2,
options=""):
'''run feature counts on *annotations_file* with
*bam_file*.
If the bam-file is paired, paired-end counting
is enabled and the bam file automatically sorted.
'''
# featureCounts cannot handle gzipped in or out files
outfile = P.snip(outfile, ".gz")
tmpdir = P.getTempDir()
annotations_tmp = os.path.join(tmpdir, 'geneset.gtf')
bam_tmp = os.path.join(tmpdir, os.path.basename(bamfile))
# -p -B specifies count fragments rather than reads, and both
# reads must map to the feature
# for legacy reasons look at feature_counts_paired
if BamTools.isPaired(bamfile):
# select paired end mode, additional options
paired_options = "-p -B"
# remove .bam extension
bam_prefix = P.snip(bam_tmp, ".bam")
# sort by read name
paired_processing = \
"""samtools
sort -@ %(nthreads)i -n %(bamfile)s %(bam_prefix)s;
checkpoint; """ % locals()
bamfile = bam_tmp
else:
paired_options = ""
paired_processing = ""
job_threads = nthreads
# AH: what is the -b option doing?
statement = '''mkdir %(tmpdir)s;
zcat %(annotations_file)s > %(annotations_tmp)s;
checkpoint;
%(paired_processing)s
featureCounts %(options)s
-T %(nthreads)i
-s %(strand)s
-b
-a %(annotations_tmp)s
%(paired_options)s
-o %(outfile)s
%(bamfile)s
>& %(outfile)s.log;
checkpoint;
gzip -f %(outfile)s;
checkpoint;
rm -rf %(tmpdir)s
'''
P.run()
def runTomTom(infile, outfile):
'''compare ab-initio motifs against tomtom.'''
tmpdir = P.getTempDir(".")
to_cluster = True
databases = " ".join(P.asList(PARAMS["tomtom_databases"]))
target_path = os.path.join(os.path.abspath(PARAMS["exportdir"]), "tomtom",
outfile)
if IOTools.isEmpty(infile):
E.warn("input is empty - no computation performed")
P.touch(outfile)
return
statement = '''
tomtom %(tomtom_options)s -oc %(tmpdir)s %(infile)s %(databases)s > %(outfile)s.log
'''
P.run()
# copy over results
try:
os.makedirs(os.path.dirname(target_path))
except OSError:
# ignore "file exists" exception
pass
if os.path.exists(target_path): shutil.rmtree(target_path)
shutil.move(tmpdir, target_path)
shutil.copyfile(os.path.join(target_path, "tomtom.txt"), outfile)
def build(self, config):
'''
return build statement to be run
'''
# output directory
outdir = "soapdenovo.dir"
# get track from config file
for line in open(config).readlines():
if line.startswith("q2"): continue
elif line.startswith("q") or line.startswith("q1"):
track = self.getTrack(line[:-1].split("=")[1])
options = "%(soapdenovo_options)s"
tempdir = P.getTempDir(".")
statement = '''%%(soapdenovo_executable)s all
-s %%(infile)s
-o %(tempdir)s/%(track)s
-K %%(kmer)s
%(options)s; checkpoint;
mv %(tempdir)s/%(track)s* %(outdir)s;
mv %(outdir)s/%(track)s.contig %(outdir)s/%(track)s.contigs.fa;
cat %(outdir)s/%(track)s.contigs.fa
| python %%(scriptsdir)s/rename_contigs.py -a
--log=%(outdir)s/%(track)s.contigs.log
rm -rf %(tempdir)s''' % locals()
return statement
def runPicardOnRealigned(infile, outfile):
to_cluster = USECLUSTER
job_options = getGATKOptions()
tmpdir_gatk = P.getTempDir('/ifs/scratch')
threads = PARAMS["gatk_threads"]
outfile_tumor = outfile.replace("Control", PARAMS["mutect_tumour"])
infile_tumor = infile.replace("Control", PARAMS["mutect_tumour"])
track = P.snip(os.path.basename(infile), ".bam")
track_tumor = track.replace("Control", PARAMS["mutect_tumour"])
statement = '''
cat %(infile)s
| python %%(scriptsdir)s/bam2bam.py -v 0 --set-sequence --bam
| CollectMultipleMetrics
INPUT=/dev/stdin
REFERENCE_SEQUENCE=%%(bwa_index_dir)s/%%(genome)s.fa
ASSUME_SORTED=true
OUTPUT=%(outfile)s
VALIDATION_STRINGENCY=SILENT
>& %(outfile)s;
cat %(infile_tumor)s
| python %%(scriptsdir)s/bam2bam.py -v 0 --set-sequence --sam
| CollectMultipleMetrics
INPUT=/dev/stdin
REFERENCE_SEQUENCE=%%(bwa_index_dir)s/%%(genome)s.fa
ASSUME_SORTED=true
OUTPUT=%(outfile_tumor)s
VALIDATION_STRINGENCY=SILENT
>& %(outfile_tumor)s;''' % locals()
P.run()
def runTomTom(infile, outfile):
'''compare ab-initio motifs against tomtom.'''
tmpdir = P.getTempDir(".")
to_cluster = True
databases = " ".join(P.asList(PARAMS["tomtom_databases"]))
target_path = os.path.join(
os.path.abspath(PARAMS["exportdir"]), "tomtom", outfile)
if IOTools.isEmpty(infile):
E.warn("input is empty - no computation performed")
P.touch(outfile)
return
statement = '''
tomtom %(tomtom_options)s -oc %(tmpdir)s %(infile)s %(databases)s > %(outfile)s.log
'''
P.run()
# copy over results
try:
os.makedirs(os.path.dirname(target_path))
except OSError:
# ignore "file exists" exception
pass
if os.path.exists(target_path):
shutil.rmtree(target_path)
shutil.move(tmpdir, target_path)
shutil.copyfile(os.path.join(target_path, "tomtom.txt"), outfile)
def GATKpreprocessing(infile, outfile):
'''Reorders BAM according to reference fasta and add read groups using SAMtools, realigns around indels and
recalibrates base quality scores using GATK'''
to_cluster = USECLUSTER
track = P.snip(os.path.basename(infile), ".bam")
tmpdir_gatk = P.getTempDir('.')
job_options = getGATKOptions()
library = PARAMS["readgroup_library"]
platform = PARAMS["readgroup_platform"]
platform_unit = PARAMS["readgroup_platform_unit"]
threads = PARAMS["gatk_threads"]
dbsnp = PARAMS["gatk_dbsnp"]
solid_options = PARAMS["gatk_solid_options"]
statement = '''ReorderSam INPUT=%(infile)s OUTPUT=%(tmpdir_gatk)s/%(track)s.reordered.bam REFERENCE=%%(bwa_index_dir)s/%%(genome)s.fa ALLOW_INCOMPLETE_DICT_CONCORDANCE=true VALIDATION_STRINGENCY=SILENT ; checkpoint ;''' % locals()
statement += '''samtools index %(tmpdir_gatk)s/%(track)s.reordered.bam ; checkpoint ;''' % locals()
statement += '''AddOrReplaceReadGroups INPUT=%(tmpdir_gatk)s/%(track)s.reordered.bam OUTPUT=%(tmpdir_gatk)s/%(track)s.readgroups.bam RGLB=%(library)s RGPL=%(platform)s RGPU=%(platform_unit)s RGSM=%(track)s VALIDATION_STRINGENCY=SILENT ; checkpoint ;''' % locals(
)
statement += '''samtools index %(tmpdir_gatk)s/%(track)s.readgroups.bam ; checkpoint ;''' % locals()
statement += '''GenomeAnalysisTK -T RealignerTargetCreator -o %(tmpdir_gatk)s/%(track)s.indelrealignment.intervals --num_threads %(threads)s -R %%(bwa_index_dir)s/%%(genome)s.fa -I %(tmpdir_gatk)s/%(track)s.readgroups.bam ; checkpoint ;''' % locals(
)
statement += '''GenomeAnalysisTK -T IndelRealigner -o %(tmpdir_gatk)s/%(track)s.indelrealigned.bam -R %%(bwa_index_dir)s/%%(genome)s.fa -I %(tmpdir_gatk)s/%(track)s.readgroups.bam -targetIntervals %(tmpdir_gatk)s/%(track)s.indelrealignment.intervals ; checkpoint ;''' % locals(
)
statement += '''GenomeAnalysisTK -T BaseRecalibrator --out %(tmpdir_gatk)s/%(track)s.recal.grp -R %%(bwa_index_dir)s/%%(genome)s.fa -I %(tmpdir_gatk)s/%(track)s.indelrealigned.bam --knownSites %(dbsnp)s %(solid_options)s ; checkpoint ;''' % locals(
)
statement += '''GenomeAnalysisTK -T PrintReads -o %(outfile)s -BQSR %(tmpdir_gatk)s/%(track)s.recal.grp -R %%(bwa_index_dir)s/%%(genome)s.fa -I %(tmpdir_gatk)s/%(track)s.indelrealigned.bam ; checkpoint ;''' % locals(
)
statement += '''rm -rf %(tmpdir_gatk)s ;'''
P.run()
def GATKpreprocessing(infile, outfile):
'''Reorders BAM according to reference fasta and add read groups using SAMtools, realigns around indels and
recalibrates base quality scores using GATK'''
to_cluster = USECLUSTER
track = P.snip(os.path.basename(infile), ".bam")
tmpdir_gatk = P.getTempDir('.')
job_options = getGATKOptions()
library = PARAMS["readgroup_library"]
platform = PARAMS["readgroup_platform"]
platform_unit = PARAMS["readgroup_platform_unit"]
threads = PARAMS["gatk_threads"]
dbsnp = PARAMS["gatk_dbsnp"]
solid_options = PARAMS["gatk_solid_options"]
statement = '''ReorderSam INPUT=%(infile)s OUTPUT=%(tmpdir_gatk)s/%(track)s.reordered.bam REFERENCE=%%(bwa_index_dir)s/%%(genome)s.fa ALLOW_INCOMPLETE_DICT_CONCORDANCE=true VALIDATION_STRINGENCY=SILENT ; checkpoint ;''' % locals(
)
statement += '''samtools index %(tmpdir_gatk)s/%(track)s.reordered.bam ; checkpoint ;''' % locals(
)
statement += '''AddOrReplaceReadGroups INPUT=%(tmpdir_gatk)s/%(track)s.reordered.bam OUTPUT=%(tmpdir_gatk)s/%(track)s.readgroups.bam RGLB=%(library)s RGPL=%(platform)s RGPU=%(platform_unit)s RGSM=%(track)s VALIDATION_STRINGENCY=SILENT ; checkpoint ;''' % locals(
)
statement += '''samtools index %(tmpdir_gatk)s/%(track)s.readgroups.bam ; checkpoint ;''' % locals(
)
statement += '''GenomeAnalysisTK -T RealignerTargetCreator -o %(tmpdir_gatk)s/%(track)s.indelrealignment.intervals --num_threads %(threads)s -R %%(bwa_index_dir)s/%%(genome)s.fa -I %(tmpdir_gatk)s/%(track)s.readgroups.bam ; checkpoint ;''' % locals(
)
statement += '''GenomeAnalysisTK -T IndelRealigner -o %(tmpdir_gatk)s/%(track)s.indelrealigned.bam -R %%(bwa_index_dir)s/%%(genome)s.fa -I %(tmpdir_gatk)s/%(track)s.readgroups.bam -targetIntervals %(tmpdir_gatk)s/%(track)s.indelrealignment.intervals ; checkpoint ;''' % locals(
)
statement += '''GenomeAnalysisTK -T BaseRecalibrator --out %(tmpdir_gatk)s/%(track)s.recal.grp -R %%(bwa_index_dir)s/%%(genome)s.fa -I %(tmpdir_gatk)s/%(track)s.indelrealigned.bam --knownSites %(dbsnp)s %(solid_options)s ; checkpoint ;''' % locals(
)
statement += '''GenomeAnalysisTK -T PrintReads -o %(outfile)s -BQSR %(tmpdir_gatk)s/%(track)s.recal.grp -R %%(bwa_index_dir)s/%%(genome)s.fa -I %(tmpdir_gatk)s/%(track)s.indelrealigned.bam ; checkpoint ;''' % locals(
)
statement += '''rm -rf %(tmpdir_gatk)s ;'''
P.run()
def build(self, config):
'''
return build statement to be run
'''
# output directory
outdir = "soapdenovo.dir"
# get track from config file
for line in open(config).readlines():
if line.startswith("q2"): continue
elif line.startswith("q") or line.startswith("q1"):
track = self.getTrack(line[:-1].split("=")[1])
options = "%(soapdenovo_options)s"
tempdir = P.getTempDir(".")
statement = '''%%(soapdenovo_executable)s all
-s %%(infile)s
-o %(tempdir)s/%(track)s
-K %%(kmer)s
%(options)s; checkpoint;
mv %(tempdir)s/%(track)s* %(outdir)s;
mv %(outdir)s/%(track)s.contig %(outdir)s/%(track)s.contigs.fa;
cat %(outdir)s/%(track)s.contigs.fa
| python %%(scriptsdir)s/rename_contigs.py -a
--log=%(outdir)s/%(track)s.contigs.log
rm -rf %(tempdir)s''' % locals()
return statement
def build(self, infile):
track = self.getTrack(infile)
format = self.getFormat(infile)
if format.endswith(".gz"):
format = P.snip(format, ".gz")
format = format.upper()
# cortex_var only uses paired end information to
# remove pcr duplicates
if not self.checkPairs(infile):
paired = "--se_list"
reads = os.path.join(os.getcwd(), infile)
elif len(self.checkPairs(infile)) > 1:
paired = "--pe_list"
read1 = infile
format = P.snip(format, ".1")
read2 = self.checkPairs(infile)[1]
elif self.checkPairs(infile) == "interleaved":
raise ValueError, "pipeline does not support file of type 'interleaved'"
temp = P.getTempDir()
read1_new = os.path.join(temp, P.snip(read1, ".1.gz"))
read2_new = os.path.join(temp, P.snip(read2, ".2.gz"))
# paired end list
list1 = open("cortex_var.dir/read1.txt", "w")
list2 = open("cortex_var.dir/read2.txt", "w")
list1.write(read1_new + "\n")
list2.write(read2_new + "\n")
list1.close()
list2.close()
list1 = os.path.abspath("cortex_var.dir/read1.txt")
list2 = os.path.abspath("cortex_var.dir/read2.txt")
reads = ",".join([os.path.join(os.getcwd(), x) for x in [read1_new, read2_new]])
statement = (
""" gunzip -c %(read1)s > %(read1_new)s
; gunzip -c %(read2)s > %(read2_new)s
; cd cortex_var.dir
; %%(cortex_var_executable)s %(paired)s %(list1)s,%(list2)s
--format %(format)s
--mem_height 15
--quality_score_threshold %%(cortex_var_qual_threshold)i
--remove_pcr_duplicates
--remove_low_coverage_supernodes %%(cortex_var_rm_low_coverage_supernodes)i
--sample_id %(track)s
--kmer_size %%(kmer)s
--dump_binary dump_binary.ctx
; rm -rf %(temp)s
"""
% locals()
)
return statement
def preprocess(self, infile):
'''
fastq files need to be converted to fasta
and pairs need to be merged
'''
mtype = None
# check for paired end data either in the same file or in a separate file
# for each read - will need to be gunzipped
# check compression status
if infile.endswith(".gz"):
if len(self.checkPairs(
infile)) > 1: # check for paired data in separate files
read1 = infile
read2 = self.checkPairs(infile)[1]
temp = P.getTempDir()
elif self.checkPairs == "interleaved":
infile_new = os.path.join(temp, P.snip(infile, ".gz"))
zippy = """gunzip -c %(infile)s > %(infile_new)s; """ % locals(
)
else:
zippy = ""
# only need to convert if the data are in fastq format
if self.getFormat(infile).find("fastq") != -1 and len(
self.checkPairs(infile)
) > 1: # reads are fastq and paired in separate files
mtype = "--merge" # argument for conversion tool
elif self.getFormat(infile).find("fastq") != -1 and self.checkPairs(
infile
) == "interleaved": # reads are fastq and in the same file
mtype = "--paired" # argument for conversion tool
# requires a merge of the fastq files in to fasta format
if mtype: # the reads are paired end
if mtype == "--merge":
outf = P.snip(os.path.basename(read1), ".fastq.1.gz") + ".fa"
# check if file exists - metaphlan also performs this preprocessing step
if not os.path.exists(outf):
statement = '''python %%(scriptsdir)s/fastqs2fasta.py -a %(read1)s -b %(read2)s --log=%(read1)s.log > %(outf)s
''' % locals()
P.run()
else:
E.info("no need to create file %s - exists" % outf)
elif mtype == "--paired":
outf = P.snip(os.path.basename(infile_new), ".fastq") + ".fa"
statement = '''%(zippy)s'''
P.run()
statement = '''fq2fa %(mtype)s %(infile_new)s %(outf)s
rm -rf %(temp)s''' % locals()
P.run()
else:
statement = None
return statement
def build(self, infile):
track = self.getTrack(infile)
format = self.getFormat(infile)
if format.endswith(".gz"):
format = P.snip(format, ".gz")
format = format.upper()
# cortex_var only uses paired end information to
# remove pcr duplicates
if not self.checkPairs(infile):
paired = "--se_list"
reads = os.path.join(os.getcwd(), infile)
elif len(self.checkPairs(infile)) > 1:
paired = "--pe_list"
read1 = infile
format = P.snip(format, ".1")
read2 = self.checkPairs(infile)[1]
elif self.checkPairs(infile) == "interleaved":
raise ValueError, "pipeline does not support file of type 'interleaved'"
temp = P.getTempDir()
read1_new = os.path.join(temp, P.snip(read1, ".1.gz"))
read2_new = os.path.join(temp, P.snip(read2, ".2.gz"))
# paired end list
list1 = open("cortex_var.dir/read1.txt", "w")
list2 = open("cortex_var.dir/read2.txt", "w")
list1.write(read1_new + "\n")
list2.write(read2_new + "\n")
list1.close()
list2.close()
list1 = os.path.abspath("cortex_var.dir/read1.txt")
list2 = os.path.abspath("cortex_var.dir/read2.txt")
reads = ",".join(
[os.path.join(os.getcwd(), x) for x in [read1_new, read2_new]])
statement = ''' gunzip -c %(read1)s > %(read1_new)s
; gunzip -c %(read2)s > %(read2_new)s
; cd cortex_var.dir
; %%(cortex_var_executable)s %(paired)s %(list1)s,%(list2)s
--format %(format)s
--mem_height 15
--quality_score_threshold %%(cortex_var_qual_threshold)i
--remove_pcr_duplicates
--remove_low_coverage_supernodes %%(cortex_var_rm_low_coverage_supernodes)i
--sample_id %(track)s
--kmer_size %%(kmer)s
--dump_binary dump_binary.ctx
; rm -rf %(temp)s
''' % locals()
return statement
def preprocess(self, infile):
'''
fastq files need to be converted to fasta
and pairs need to be merged
'''
mtype = None
# check for paired end data either in the same file or in a separate file
# for each read - will need to be gunzipped
# check compression status
if infile.endswith(".gz"):
# check for paired data in separate files
if len(self.checkPairs(infile)) > 1:
read1 = infile
read2 = self.checkPairs(infile)[1]
temp = P.getTempDir()
elif self.checkPairs == "interleaved":
infile_new = os.path.join(temp, P.snip(infile, ".gz"))
zippy = """gunzip -c %(infile)s > %(infile_new)s; """ % locals()
else:
zippy = ""
# only need to convert if the data are in fastq format
# reads are fastq and paired in separate files
if self.getFormat(infile).find("fastq") != -1 and len(self.checkPairs(infile)) > 1:
mtype = "--merge" # argument for conversion tool
# reads are fastq and in the same file
elif self.getFormat(infile).find("fastq") != -1 and self.checkPairs(infile) == "interleaved":
mtype = "--paired" # argument for conversion tool
# requires a merge of the fastq files in to fasta format
if mtype: # the reads are paired end
if mtype == "--merge":
outf = P.snip(os.path.basename(read1), ".fastq.1.gz") + ".fa"
# check if file exists - metaphlan also performs this
# preprocessing step
if not os.path.exists(outf):
statement = '''python %%(scriptsdir)s/fastqs2fasta.py -a %(read1)s -b %(read2)s --log=%(read1)s.log > %(outf)s
''' % locals()
P.run()
else:
E.info("no need to create file %s - exists" % outf)
elif mtype == "--paired":
outf = P.snip(os.path.basename(infile_new), ".fastq") + ".fa"
statement = '''%(zippy)s'''
P.run()
statement = '''fq2fa %(mtype)s %(infile_new)s %(outf)s
rm -rf %(temp)s''' % locals()
P.run()
else:
statement = None
return statement
def preprocess(self, infile):
'''
fastq files need to be converted to fasta
and pairs need to be merged
'''
mtype = None
# check for paired end data either in the same file or in a separate file
# for each read - will need to be gunzipped
# check compression status
if infile.endswith(".gz"):
if len(self.checkPairs(
infile)) > 1: # check for paired data in separate files
read1 = infile
read2 = self.checkPairs(infile)[1]
temp = P.getTempDir()
read1_new = os.path.join(temp, P.snip(infile, ".gz"))
read2_new = os.path.join(
temp, P.snip(self.checkPairs(infile)[1], ".gz"))
zippy = """gunzip -c %(read1)s > %(read1_new)s
; gunzip -c %(read2)s > %(read2_new)s; """ % locals()
elif self.checkPairs == "interleaved":
infile_new = os.path.join(temp, P.snip(infile, ".gz"))
zippy = """gunzip -c %(infile)s > %(infile_new)s; """ % locals(
)
else:
zippy = ""
# only need to convert if the data are in fastq format
if self.getFormat(infile).find("fastq") != -1 and len(
self.checkPairs(infile)
) > 1: # reads are fastq and paired in separate files
mtype = "--merge" # argument for conversion tool
elif self.getFormat(infile).find("fastq") != -1 and self.checkPairs(
infile
) == "interleaved": # reads are fastq and in the same file
mtype = "--paired" # argument for conversion tool
# build statement
if mtype: # the reads are paired end
if mtype == "--merge":
outf = P.snip(os.path.basename(read1_new), ".fastq.1") + ".fa"
statement = '''%(zippy)s
fq2fa %(mtype)s %(read1_new)s %(read2_new)s %(outf)s
''' % locals()
elif mtype == "--paired":
outf = P.snip(os.path.basename(infile_new), ".fastq") + ".fa"
statement = '''%(zippy)s
fq2fa %(mtype)s %(infile_new)s %(outf)s
rm -rf %(temp)s''' % locals()
else:
statement = None
return statement
def build(self, infile, PARAMS):
'''
run velveth and velvetg
followed by meta-velvetg
'''
outdir = P.getTempDir(".")
format = self.getFormat(infile)
paired = self.checkPairs(infile)
if not paired:
pair = ""
files = infile
read_type = "short"
else:
pair = "-%s" % paired[0]
files = " ".join([infile, paired[1]])
read_type = "shortPaired"
if format == "fastq.1.gz":
format = "fastq.gz"
metavelvet_dir = os.path.join(os.getcwd(), "metavelvet.dir")
track = self.getTrack(os.path.basename(infile))
self.stats_file = track + ".stats.txt"
if paired:
insert_length = "-ins_length %i" % PARAMS["velvetg_insert_length"]
else:
insert_length = ""
# velveth and velvetg have to be run to build hash tables and initial
# de bruijn graphs
statement = '''%%(velveth_executable)s %(outdir)s %%(kmer)i -%(format)s -%(read_type)s %(pair)s %(files)s >> %(metavelvet_dir)s/%(track)s_velveth.log
; checkpoint
; mv %(outdir)s/Log %(metavelvet_dir)s/%(track)s.velveth.log
; %%(velvetg_executable)s %(outdir)s -exp_cov auto %(insert_length)s
; checkpoint
; %%(metavelvet_executable)s %(outdir)s %(insert_length)s
; mv %(outdir)s/Roadmaps %(metavelvet_dir)s/%(track)s.roadmaps
; gzip %(metavelvet_dir)s/%(track)s.roadmaps
; mv %(outdir)s/Sequences %(metavelvet_dir)s/%(track)s.sequences
; gzip %(metavelvet_dir)s/%(track)s.sequences
; mv %(outdir)s/Graph2 %(metavelvet_dir)s/%(track)s.graph2
; gzip %(metavelvet_dir)s/%(track)s.graph2
; cat %(outdir)s/meta-velvetg.contigs.fa | python %%(scriptsdir)s/rename_contigs.py -a metavelvet
--log=%(metavelvet_dir)s/%(track)s.contigs.log
> %(metavelvet_dir)s/%(track)s.contigs.fa
; sed -i 's/in/_in/g' %(outdir)s/meta-velvetg.Graph2-stats.txt
; mv %(outdir)s/meta-velvetg.Graph2-stats.txt %(metavelvet_dir)s/%(track)s.stats.txt
; rm -rf %(outdir)s
''' % locals()
return statement
def build(self, infile, PARAMS):
'''
run velveth and velvetg
followed by meta-velvetg
'''
outdir = P.getTempDir(".")
format = self.getFormat(infile)
paired = self.checkPairs(infile)
if not paired:
pair = ""
files = infile
read_type = "short"
else:
pair = "-%s" % paired[0]
files = " ".join([infile, paired[1]])
read_type = "shortPaired"
if format == "fastq.1.gz":
format = "fastq.gz"
metavelvet_dir = os.path.join(os.getcwd(), "metavelvet.dir")
track = self.getTrack(os.path.basename(infile))
self.stats_file = track + ".stats.txt"
if paired:
insert_length = "-ins_length %i" % PARAMS["velvetg_insert_length"]
else:
insert_length = ""
# velveth and velvetg have to be run to build hash tables and initial
# de bruijn graphs
statement = '''%%(velveth_executable)s %(outdir)s %%(kmer)i -%(format)s -%(read_type)s %(pair)s %(files)s >> %(metavelvet_dir)s/%(track)s_velveth.log
; checkpoint
; mv %(outdir)s/Log %(metavelvet_dir)s/%(track)s.velveth.log
; %%(velvetg_executable)s %(outdir)s -exp_cov auto %(insert_length)s
; checkpoint
; %%(metavelvet_executable)s %(outdir)s %(insert_length)s
; mv %(outdir)s/Roadmaps %(metavelvet_dir)s/%(track)s.roadmaps
; gzip %(metavelvet_dir)s/%(track)s.roadmaps
; mv %(outdir)s/Sequences %(metavelvet_dir)s/%(track)s.sequences
; gzip %(metavelvet_dir)s/%(track)s.sequences
; mv %(outdir)s/Graph2 %(metavelvet_dir)s/%(track)s.graph2
; gzip %(metavelvet_dir)s/%(track)s.graph2
; cat %(outdir)s/meta-velvetg.contigs.fa | python %%(scriptsdir)s/rename_contigs.py -a metavelvet
--log=%(metavelvet_dir)s/%(track)s.contigs.log
> %(metavelvet_dir)s/%(track)s.contigs.fa
; sed -i 's/in/_in/g' %(outdir)s/meta-velvetg.Graph2-stats.txt
; mv %(outdir)s/meta-velvetg.Graph2-stats.txt %(metavelvet_dir)s/%(track)s.stats.txt
; rm -rf %(outdir)s
''' % locals()
return statement
def trimReads( infile, outfile ):
'''trim reads with FastX'''
to_cluster = True
tmpdir_fastq = P.getTempDir()
track = P.snip( os.path.basename( infile ), ".gz" )
statement = """gunzip < %(infile)s | python %%(scriptsdir)s/fastq2fastq.py
--change-format=sanger
--guess-format=phred64
--log=%(outfile)s.log
> %(tmpdir_fastq)s/%(track)s;""" % locals()
statement += """zcat %(infile)s | fastx_trimmer -f %(first_base)s -l %(last_base)s -z -o %(outfile)s """
P.run()
def trimReads(infile, outfile):
'''trim reads with FastX'''
to_cluster = True
tmpdir_fastq = P.getTempDir()
track = P.snip(os.path.basename(infile), ".gz")
statement = """gunzip < %(infile)s | python %%(scriptsdir)s/fastq2fastq.py
--change-format=sanger
--guess-format=phred64
--log=%(outfile)s.log
> %(tmpdir_fastq)s/%(track)s;""" % locals()
statement += """zcat %(infile)s | fastx_trimmer -f %(first_base)s -l %(last_base)s -z -o %(outfile)s """
P.run()
def runMEME(track, outfile, dbhandle):
'''run MEME to find motifs.
In order to increase the signal/noise ratio,
MEME is not run on all intervals but only the
top 10% of intervals (peakval) are used.
Also, only the segment of 200 bp around the peak
is used and not the complete interval.
* Softmasked sequence is converted to hardmasked
sequence to avoid the detection of spurious motifs.
* Sequence is run through dustmasker
This method is deprecated - use runMEMEOnSequences instead.
'''
to_cluster = True
# job_options = "-l mem_free=8000M"
target_path = os.path.join(os.path.abspath(PARAMS["exportdir"]), "meme",
outfile)
fasta = IndexedFasta.IndexedFasta(
os.path.join(PARAMS["genome_dir"], PARAMS["genome"]))
tmpdir = P.getTempDir(".")
tmpfasta = os.path.join(tmpdir, "in.fa")
nseq = writeSequencesForIntervals(
track,
tmpfasta,
dbhandle,
full=False,
masker=P.asList(PARAMS['motifs_masker']),
halfwidth=int(PARAMS["meme_halfwidth"]),
maxsize=int(PARAMS["meme_max_size"]),
proportion=PARAMS["meme_proportion"],
min_sequences=PARAMS["meme_min_sequences"])
if nseq == 0:
E.warn("%s: no sequences - meme skipped" % outfile)
P.touch(outfile)
else:
statement = '''
meme %(tmpfasta)s -dna -revcomp -mod %(meme_model)s -nmotifs %(meme_nmotifs)s -oc %(tmpdir)s -maxsize %(meme_max_size)s %(meme_options)s > %(outfile)s.log
'''
P.run()
collectMEMEResults(tmpdir, target_path, outfile)
def assignEssentialGenesToContigs(infile, outfile):
'''
assign essential genes to contigs
'''
dirname = os.path.dirname(infile)
essential = PARAMS["hmmer_hmm"]
tempdir = P.getTempDir(".")
statement = '''zcat %(infile)s > %(tempdir)s/orfs.fa;
hmmsearch --tblout %(tempdir)s/hmm.out --cut_tc
--notextw %(essential)s %(tempdir)s/orfs.fa;
tail -n+4 %(tempdir)s/hmm.out | sed 's/ * / /g' | cut -f 1,4 -d " "
| gzip > %(outfile)s'''
P.run()
statement = '''rm -rf %(tempdir)s'''
P.run()
def runMEME(track, outfile, dbhandle):
'''run MEME to find motifs.
In order to increase the signal/noise ratio,
MEME is not run on all intervals but only the
top 10% of intervals (peakval) are used.
Also, only the segment of 200 bp around the peak
is used and not the complete interval.
* Softmasked sequence is converted to hardmasked
sequence to avoid the detection of spurious motifs.
* Sequence is run through dustmasker
This method is deprecated - use runMEMEOnSequences instead.
'''
to_cluster = True
# job_options = "-l mem_free=8000M"
target_path = os.path.join(
os.path.abspath(PARAMS["exportdir"]), "meme", outfile)
fasta = IndexedFasta.IndexedFasta(
os.path.join(PARAMS["genome_dir"], PARAMS["genome"]))
tmpdir = P.getTempDir(".")
tmpfasta = os.path.join(tmpdir, "in.fa")
nseq = writeSequencesForIntervals(track, tmpfasta,
dbhandle,
full=False,
masker=P.asList(PARAMS['motifs_masker']),
halfwidth=int(PARAMS["meme_halfwidth"]),
maxsize=int(PARAMS["meme_max_size"]),
proportion=PARAMS["meme_proportion"],
min_sequences=PARAMS["meme_min_sequences"])
if nseq == 0:
E.warn("%s: no sequences - meme skipped" % outfile)
P.touch(outfile)
else:
statement = '''
meme %(tmpfasta)s -dna -revcomp -mod %(meme_model)s -nmotifs %(meme_nmotifs)s -oc %(tmpdir)s -maxsize %(meme_max_size)s %(meme_options)s > %(outfile)s.log
'''
P.run()
collectMEMEResults(tmpdir, target_path, outfile)
def preprocess(self, infile):
'''
fastq files need to be converted to fasta
and pairs need to be merged
'''
mtype = None
# check for paired end data either in the same file or in a separate file
# for each read - will need to be gunzipped
# check compression status
if infile.endswith(".gz"):
if len(self.checkPairs(infile)) > 1: # check for paired data in separate files
read1 = infile
read2 = self.checkPairs(infile)[1]
temp = P.getTempDir()
read1_new = os.path.join(temp, P.snip(infile, ".gz"))
read2_new = os.path.join(temp, P.snip(self.checkPairs(infile)[1], ".gz"))
zippy = """gunzip -c %(read1)s > %(read1_new)s
; gunzip -c %(read2)s > %(read2_new)s; """ % locals()
elif self.checkPairs == "interleaved":
infile_new = os.path.join(temp, P.snip(infile, ".gz"))
zippy = """gunzip -c %(infile)s > %(infile_new)s; """ % locals()
else:
zippy = ""
# only need to convert if the data are in fastq format
if self.getFormat(infile).find("fastq") != -1 and len(self.checkPairs(infile)) >1: # reads are fastq and paired in separate files
mtype = "--merge" # argument for conversion tool
elif self.getFormat(infile).find("fastq") != -1 and self.checkPairs(infile) == "interleaved": # reads are fastq and in the same file
mtype = "--paired" # argument for conversion tool
# build statement
if mtype: # the reads are paired end
if mtype == "--merge":
outf = P.snip(os.path.basename(read1_new), ".fastq.1") + ".fa"
statement = '''%(zippy)s
fq2fa %(mtype)s %(read1_new)s %(read2_new)s %(outf)s
''' % locals()
elif mtype == "--paired":
outf = P.snip(os.path.basename(infile_new), ".fastq") + ".fa"
statement = '''%(zippy)s
fq2fa %(mtype)s %(infile_new)s %(outf)s
rm -rf %(temp)s''' % locals()
else:
statement = None
return statement
def build(self, infile):
'''
build statement for running idba
input is merged fasta file and
output is contigs fasta file
'''
track = self.getTrack(infile)
outdir = "idba.dir"
# get temporary file for running idba
tempdir = P.getTempDir()
# NB at the moment we assume the default maxkmer of 100
statement = '''%%(idba_executable)s -r %(infile)s -o %(tempdir)s %%(idba_options)s
; mv %(tempdir)s/scaffold.fa idba.dir/%(track)s.scaffolds.fa
; mv %(tempdir)s/contig-%%(idba_maxkmer)s.fa idba.dir/%(track)s.contigs.fa''' % locals()
shutil.rmtree(tempdir)
return statement
def buildCoverageOverContigs(infiles, outfile):
'''
build histograms of the coverage over each of the contigs
'''
bam = infiles[0]
# genomecoveragebed does not like some of the
# output from bwa. bwa outputs some reads
# that map off the end of contigs
# as having a leftmost position of 0. This is
# not ideal. Need to use temporary bam
# files with only mapped reads - this is
# nasty and needs changing
tempdir = P.getTempDir(".")
tempname = P.getTempFilename(tempdir) + ".bam"
P.submit("CGATPipelines.PipelineMetagenomeAssembly",
"filterBamOnPos",
infiles = bam,
outfiles = tempname)
# tablename where alignment stats live
tablename = os.path.dirname(
bam)[:-len(".dir")] + "_" + P.snip(os.path.basename(bam), ".bam") + "_alignment_stats"
# hack to convert to table - add .load
tablename = P.toTable(tablename + ".load")
# connect to database
dbh = connect()
cc = dbh.cursor()
# get number of reads aligned from bam2stats
if PARAMS.get("coverage_scale"):
scale_factor = cc.execute("""SELECT counts FROM %s
WHERE category == 'reads_mapped'""" % tablename).fetchone()[0]
scale_factor = 1 / (float(scale_factor) / 1000000)
scale_options = "-scale %(scale_factor)f"
else:
scale_options = ""
statement = '''genomeCoverageBed -ibam %(tempname)s %(scale_options)s -d | gzip > %(outfile)s;
rm -rf %(tempdir)s'''
P.run()
def build(self, infile):
'''
build statement for running spades
'''
track = self.getTrack(os.path.basename(infile))
format = self.getFormat(infile)
paired = self.checkPairs(infile)
tempdir = P.getTempDir(".")
outdir = "spades.dir"
# input files
if not paired:
files = infile
files_statement = "-s %s" % files
else:
# spades doesn't like the fastq.1.gz type format
temp1 = os.path.join(tempdir, track+".1.fastq")
temp2 = os.path.join(tempdir, track+".2.fastq")
infile2 = paired[1]
unzip_statement = "zcat %(infile)s > %(temp1)s; zcat %(infile2)s > %(temp2)s" % locals()
files_statement = " -1 " + " -2 ".join( [temp1, temp2] )
# kmer to use
k = "-k %(kmer)s"
# spades options
spades_options = "%(spades_options)s"
# deal with spades output
move_statement = """mv %(tempdir)s/corrected/%(track)s*cor.* %(outdir)s; \
mv %(tempdir)s/contigs.fasta %(outdir)s/%(track)s.contigs.fa; \
mv %(tempdir)s/scaffolds.fasta %(outdir)s/%(track)s.scaffolds.fa; \
mv %(tempdir)s/spades.log %(outdir)s/%(track)s.contigs.log""" % locals()
# statement - simple and default
statement = '''%(unzip_statement)s; checkpoint;
spades.py %(files_statement)s -o %(tempdir)s %(k)s %(spades_options)s; checkpoint;
%(move_statement)s; checkpoint;
rm -rf %(tempdir)s %(temp1)s %(temp2)s''' % locals()
return statement
def build(self, infile):
'''
build statement for running idba
input is merged fasta file and
output is contigs fasta file
'''
track = self.getTrack(infile)
outdir = "idba.dir"
# get temporary file for running idba
tempdir = P.getTempDir()
# NB at the moment we assume the default maxkmer of 100
statement = '''%%(idba_executable)s -r %(infile)s -o %(tempdir)s %%(idba_options)s
; mv %(tempdir)s/scaffold.fa idba.dir/%(track)s.scaffolds.fa
; cat %(tempdir)s/contig-%%(idba_maxkmer)s.fa | python %%(scriptsdir)s/rename_contigs.py -a idba --log=%(outdir)s/%(track)s.contigs.log
> idba.dir/%(track)s.contigs.fa''' % locals()
shutil.rmtree(tempdir)
return statement
def runMEMEOnSequences(infile, outfile):
'''run MEME to find motifs.
In order to increase the signal/noise ratio,
MEME is not run on all intervals but only the
top 10% of intervals (peakval) are used.
Also, only the segment of 200 bp around the peak
is used and not the complete interval.
* Softmasked sequence is converted to hardmasked
sequence to avoid the detection of spurious motifs.
* Sequence is run through dustmasker
'''
to_cluster = True
# job_options = "-l mem_free=8000M"
nseqs = int(FastaIterator.count(infile))
if nseqs == 0:
E.warn("%s: no sequences - meme skipped" % outfile)
P.touch(outfile)
return
target_path = os.path.join(
os.path.abspath(PARAMS["exportdir"]), "meme", outfile)
tmpdir = P.getTempDir(".")
statement = '''
meme %(infile)s -dna -revcomp
-mod %(meme_model)s
-nmotifs %(meme_nmotifs)s
-oc %(tmpdir)s
-maxsize %(motifs_max_size)s
%(meme_options)s
> %(outfile)s.log
'''
P.run()
collectMEMEResults(tmpdir, target_path, outfile)
def runMEMEOnSequences(infile, outfile):
'''run MEME to find motifs.
In order to increase the signal/noise ratio,
MEME is not run on all intervals but only the
top 10% of intervals (peakval) are used.
Also, only the segment of 200 bp around the peak
is used and not the complete interval.
* Softmasked sequence is converted to hardmasked
sequence to avoid the detection of spurious motifs.
* Sequence is run through dustmasker
'''
to_cluster = True
# job_options = "-l mem_free=8000M"
nseqs = int(FastaIterator.count(infile))
if nseqs == 0:
E.warn("%s: no sequences - meme skipped" % outfile)
P.touch(outfile)
return
target_path = os.path.join(os.path.abspath(PARAMS["exportdir"]), "meme",
outfile)
tmpdir = P.getTempDir(".")
statement = '''
meme %(infile)s -dna -revcomp
-mod %(meme_model)s
-nmotifs %(meme_nmotifs)s
-oc %(tmpdir)s
-maxsize %(motifs_max_size)s
%(meme_options)s
> %(outfile)s.log
'''
P.run()
collectMEMEResults(tmpdir, target_path, outfile)
def buildClusters( infiles, outfiles ):
'''run c-means clustering on expression level data.'''
to_cluster = USECLUSTER
job_options = "-l mem_free=10G"
# ignore the background file (why is it included in infiles?)
infile, _ = infiles
instructions_filename, centroid_filename, membership_filename = outfiles
instructions_filename = os.path.abspath( instructions_filename )
cdt_filename = os.path.abspath( infile )
kmeans_clusters = PARAMS["kmeans_clusters"]
# run aerie in a temporary directory
tmpdir = P.getTempDir(".")
with open( instructions_filename, "w" ) as outf:
outf.write( '''load %(cdt_filename)s
fuzzy %(kmeans_clusters)i
%(tmpdir)s/all
exit
''' % locals())
statement = '''
aerie < %(instructions_filename)s >& %(instructions_filename)s.log
'''
P.run()
try:
shutil.move( os.path.join( tmpdir, "all.fct"), centroid_filename )
shutil.move( os.path.join( tmpdir, "all.mb"), membership_filename )
except IOError,msg:
E.warn("no results for %s,%s: %s" % (centroid_filename,
membership_filename,
msg))
P.touch( centroid_filename )
P.touch( membership_filename )
def build(self, infile):
"""
build statement for running idba
input is merged fasta file and
output is contigs fasta file
"""
track = self.getTrack(infile)
outdir = "idba.dir"
# get temporary file for running idba
tempdir = P.getTempDir()
# NB at the moment we assume the default maxkmer of 100
statement = (
"""%%(idba_executable)s -r %(infile)s -o %(tempdir)s %%(idba_options)s
; mv %(tempdir)s/scaffold.fa idba.dir/%(track)s.scaffolds.fa
; cat %(tempdir)s/contig-%%(idba_maxkmer)s.fa | python %%(scriptsdir)s/rename_contigs.py -a idba --log=%(outdir)s/%(track)s.contigs.log
> idba.dir/%(track)s.contigs.fa"""
% locals()
)
shutil.rmtree(tempdir)
return statement
def buildClusters(infiles, outfiles):
'''run c-means clustering on expression level data.'''
to_cluster = USECLUSTER
job_options = "-l mem_free=10G"
# ignore the background file (why is it included in infiles?)
infile, _ = infiles
instructions_filename, centroid_filename, membership_filename = outfiles
instructions_filename = os.path.abspath(instructions_filename)
cdt_filename = os.path.abspath(infile)
kmeans_clusters = PARAMS["kmeans_clusters"]
# run aerie in a temporary directory
tmpdir = P.getTempDir(".")
with open(instructions_filename, "w") as outf:
outf.write('''load %(cdt_filename)s
fuzzy %(kmeans_clusters)i
%(tmpdir)s/all
exit
''' % locals())
statement = '''
aerie < %(instructions_filename)s >& %(instructions_filename)s.log
'''
P.run()
try:
shutil.move(os.path.join(tmpdir, "all.fct"), centroid_filename)
shutil.move(os.path.join(tmpdir, "all.mb"), membership_filename)
except IOError, msg:
E.warn("no results for %s,%s: %s" %
(centroid_filename, membership_filename, msg))
P.touch(centroid_filename)
P.touch(membership_filename)
def build(self, infile):
'''
run velveth and velvetg
followed by meta-velvetg
'''
outdir = P.getTempDir()
format = self.getFormat(infile)
paired = self.checkPairs(infile)
if len(paired) > 1:
pair = paired[0]
files = " ".join([infile, paired[1]])
else:
pair = paired
files = infile
if format == "fastq.1.gz":
format = "fastq.gz"
metavelvet_dir = os.path.join(os.getcwd(), "metavelvet.dir")
track = self.getTrack(infile)
self.stats_file = track + ".stats.txt"
# velveth and velvetg have to be run to build hash tables and initial de bruijn graphs
statement = '''%%(velveth_executable)s %(outdir)s %%(kmer)i -%(format)s -shortPaired -%(pair)s %(files)s
; cd %(outdir)s; %%(velvetg_executable)s %(outdir)s -exp_cov auto -ins_length %%(velvetg_insert_length)i
; %%(metavelvet_executable)s %(outdir)s -ins_length %%(velvetg_insert_length)i
; mv %(outdir)s/Roadmaps %(metavelvet_dir)s/%(track)s.roadmaps
; gzip %(metavelvet_dir)s/%(track)s.roadmaps
; mv %(outdir)s/Sequences %(metavelvet_dir)s/%(track)s.sequences
; gzip %(metavelvet_dir)s/%(track)s.sequences
; mv %(outdir)s/Graph2 %(metavelvet_dir)s/%(track)s.graph2
; gzip %(metavelvet_dir)s/%(track)s.graph2
; mv %(outdir)s/meta-velvetg.contigs.fa %(metavelvet_dir)s/%(track)s.contigs.fa
; sed -i 's/in/_in/g' %(outdir)s/meta-velvetg.Graph2-stats.txt
; mv %(outdir)s/meta-velvetg.Graph2-stats.txt %(metavelvet_dir)s/%(track)s.stats.txt
; rm -rf %(outdir)s''' % locals()
return statement
def build(self, infile):
'''
run velveth and velvetg
followed by meta-velvetg
'''
outdir = P.getTempDir()
format = self.getFormat(infile)
paired = self.checkPairs(infile)
if len(paired) > 1:
pair = paired[0]
files = " ".join([infile, paired[1]])
else:
pair = paired
files = infile
if format == "fastq.1.gz":
format = "fastq.gz"
metavelvet_dir = os.path.join(os.getcwd(), "metavelvet.dir")
track = self.getTrack(infile)
self.stats_file = track + ".stats.txt"
# velveth and velvetg have to be run to build hash tables and initial de bruijn graphs
statement = '''%%(velveth_executable)s %(outdir)s %%(kmer)i -%(format)s -shortPaired -%(pair)s %(files)s
; cd %(outdir)s; %%(velvetg_executable)s %(outdir)s -exp_cov auto -ins_length %%(velvetg_insert_length)i
; %%(metavelvet_executable)s %(outdir)s -ins_length %%(velvetg_insert_length)i
; mv %(outdir)s/Roadmaps %(metavelvet_dir)s/%(track)s.roadmaps
; gzip %(metavelvet_dir)s/%(track)s.roadmaps
; mv %(outdir)s/Sequences %(metavelvet_dir)s/%(track)s.sequences
; gzip %(metavelvet_dir)s/%(track)s.sequences
; mv %(outdir)s/Graph2 %(metavelvet_dir)s/%(track)s.graph2
; gzip %(metavelvet_dir)s/%(track)s.graph2
; mv %(outdir)s/meta-velvetg.contigs.fa %(metavelvet_dir)s/%(track)s.contigs.fa
; sed -i 's/in/_in/g' %(outdir)s/meta-velvetg.Graph2-stats.txt
; mv %(outdir)s/meta-velvetg.Graph2-stats.txt %(metavelvet_dir)s/%(track)s.stats.txt
; rm -rf %(outdir)s''' % locals()
return statement
def realignMatchedSample(infile, outfile):
''' repeat realignments with merged bam of control and tumor
this should help avoid problems with sample-specific realignments'''
# Note: need to change readgroup headers for merge and subsequent
# splitting of bam files
to_cluster = USECLUSTER
job_options = getGATKOptions()
tmpdir_gatk = P.getTempDir('/ifs/scratch')
threads = PARAMS["gatk_threads"]
outfile_tumor = outfile.replace("Control", PARAMS["mutect_tumour"])
infile_tumor = infile.replace("Control", PARAMS["mutect_tumour"])
track = P.snip(os.path.basename(infile), ".bam")
track_tumor = track.replace("Control", PARAMS["mutect_tumour"])
library = PARAMS["readgroup_library"]
platform = PARAMS["readgroup_platform"]
platform_unit = PARAMS["readgroup_platform_unit"]
statement = '''module unload apps/java/jre1.6.0_26; checkpoint;'''
statement += '''AddOrReplaceReadGroups
INPUT=%(infile)s
OUTPUT=%(tmpdir_gatk)s/control.bam
RGLB=%(library)s RGPL=%(platform)s
RGPU=%(platform_unit)s RGSM=%(track)s
ID=Control
VALIDATION_STRINGENCY=SILENT ;
checkpoint ;''' % locals()
statement += '''samtools view -H %(tmpdir_gatk)s/control.bam
> %(tmpdir_gatk)s/header.sam;
samtools view -H %(infile_tumor)s | grep "^@RG"
>> %(tmpdir_gatk)s/header.sam;
samtools merge
-h %(tmpdir_gatk)s/header.sam
%(tmpdir_gatk)s/merged.bam
%(tmpdir_gatk)s/control.bam
%(infile_tumor)s
; checkpoint ;''' % locals()
statement += '''samtools index %(tmpdir_gatk)s/merged.bam;
checkpoint ;''' % locals()
statement += '''java -Xmx4g -jar
/ifs/apps/bio/GATK-2.7-2/GenomeAnalysisTK.jar
-T RealignerTargetCreator
-o %(tmpdir_gatk)s/merged.indelrealignment.intervals
-R %%(bwa_index_dir)s/%%(genome)s.fa
-I %(tmpdir_gatk)s/merged.bam ;
checkpoint ;''' % locals()
statement += '''java -Xmx4g -jar
/ifs/apps/bio/GATK-2.7-2/GenomeAnalysisTK.jar
-T IndelRealigner
-o %(tmpdir_gatk)s/merged.indelrealigned.bam
-R %%(bwa_index_dir)s/%%(genome)s.fa
-I %(tmpdir_gatk)s/merged.bam
-targetIntervals
%(tmpdir_gatk)s/merged.indelrealignment.intervals;
checkpoint ;''' % locals()
statement += '''samtools view -hb
%(tmpdir_gatk)s/merged.indelrealigned.bam
-r Control > %(outfile)s;
samtools view -hb
%(tmpdir_gatk)s/merged.indelrealigned.bam
-r 1 > %(outfile_tumor)s;
samtools index %(outfile)s;
samtools index %(outfile_tumor)s;
checkpoint;''' % locals()
statement += '''rm -rf %(tmpdir_gatk)s ;'''
print statement
P.run()
def runMAST(infiles, outfile):
'''run mast on all intervals and motifs.
Collect all results for an E-value up to 10000 so that
all sequences are output and MAST curves can be computed.
10000 is a heuristic.
'''
to_cluster = True
# job_options = "-l mem_free=8000M"
controlfile, dbfile, motiffiles = infiles
if IOTools.isEmpty(dbfile):
P.touch(outfile)
return
if not os.path.exists(controlfile):
raise P.PipelineError(
"control file %s for %s does not exist" % (controlfile, dbfile))
# remove previous results
if os.path.exists(outfile):
os.remove(outfile)
tmpdir = P.getTempDir(".")
tmpfile = P.getTempFilename(".")
for motiffile in motiffiles:
if IOTools.isEmpty(motiffile):
L.info("skipping empty motif file %s" % motiffile)
continue
of = IOTools.openFile(tmpfile, "a")
motif, x = os.path.splitext(motiffile)
of.write(":: motif = %s - foreground ::\n" % motif)
of.close()
# mast bails if the number of nucleotides gets larger than
# 2186800982?
# To avoid this, run db and control file separately.
statement = '''
cat %(dbfile)s
| mast %(motiffile)s - -nohtml -oc %(tmpdir)s -ev %(mast_evalue)f %(mast_options)s >> %(outfile)s.log 2>&1;
cat %(tmpdir)s/mast.txt >> %(tmpfile)s 2>&1
'''
P.run()
of = IOTools.openFile(tmpfile, "a")
motif, x = os.path.splitext(motiffile)
of.write(":: motif = %s - background ::\n" % motif)
of.close()
statement = '''
cat %(controlfile)s
| mast %(motiffile)s - -nohtml -oc %(tmpdir)s -ev %(mast_evalue)f %(mast_options)s >> %(outfile)s.log 2>&1;
cat %(tmpdir)s/mast.txt >> %(tmpfile)s 2>&1
'''
P.run()
statement = "gzip < %(tmpfile)s > %(outfile)s"
P.run()
shutil.rmtree(tmpdir)
os.unlink(tmpfile)
def runFeatureCounts(annotations_file,
bamfile,
outfile,
nthreads=4,
strand=2,
options=""):
'''run feature counts on *annotations_file* with
*bam_file*.
If the bam-file is paired, paired-end counting
is enabled and the bam file automatically sorted.
'''
# featureCounts cannot handle gzipped in or out files
outfile = P.snip(outfile, ".gz")
tmpdir = P.getTempDir()
annotations_tmp = os.path.join(tmpdir,
'geneset.gtf')
bam_tmp = os.path.join(tmpdir,
bamfile)
# -p -B specifies count fragments rather than reads, and both
# reads must map to the feature
# for legacy reasons look at feature_counts_paired
if BamTools.isPaired(bamfile):
# select paired end mode, additional options
paired_options = "-p -B"
# remove .bam extension
bam_prefix = P.snip(bam_tmp, ".bam")
# sort by read name
paired_processing = \
"""samtools
sort -@ %(nthreads)i -n %(bamfile)s %(bam_prefix)s;
checkpoint; """ % locals()
bamfile = bam_tmp
else:
paired_options = ""
paired_processing = ""
job_options = "-pe dedicated %i" % nthreads
# AH: what is the -b option doing?
statement = '''mkdir %(tmpdir)s;
zcat %(annotations_file)s > %(annotations_tmp)s;
checkpoint;
%(paired_processing)s
featureCounts %(options)s
-T %(nthreads)i
-s %(strand)s
-b
-a %(annotations_tmp)s
%(paired_options)s
-o %(outfile)s
%(bamfile)s
>& %(outfile)s.log;
checkpoint;
gzip -f %(outfile)s;
checkpoint;
rm -rf %(tmpdir)s
'''
P.run()
def build(self, infile):
'''
build statement for running Ray
'''
track = self.getTrack(infile)
format = self.getFormat(infile)
paired = self.checkPairs(infile)
tempdir = P.getTempDir()
# check whether the data are paired-end
if not paired:
pair = paired
files = os.path.join(tempdir, P.snip(infile, ".gz"))
gunzy = "gunzip -c %(infile)s > %(files)s" % locals()
else:
pair = paired[0]
# Ray doesn't like .fastq.1.gz etc
read1 = infile
read2 = paired[1]
read1_new = os.path.join(tempdir,read1.replace(".fastq.1.gz", ".1.fastq"))
read2_new = os.path.join(tempdir,read2.replace(".fastq.2.gz", ".2.fastq"))
files = " ".join([read1_new, read2_new])
gunzy = """gunzip -c %(read1)s > %(read1_new)s
; gunzip -c %(read2)s > %(read2_new)s""" % locals()
# ray likes an output directory but needs it not
# to exist beforehand
raydir = os.path.join(os.getcwd(), "ray.dir/export_%s" % track)
raydir_orig = os.path.join(os.getcwd(), "ray.dir")
# Ray picks up file types so should just have to
# say whether its paired or not
# build statement
common_options = "-k %(kmer)s"
if pair == "interleaved":
filetype = "-i"
elif not pair:
filetype = "-s"
elif pair == "separate":
filetype = "-p"
else:
raise IOError, "do not support file of this type: %s" % infile
# note restrict use to 5 cores
statement = ''' %(gunzy)s
; mpiexec -n 5 %%(ray_executable)s %(common_options)s %(filetype)s %(files)s -o %(raydir)s
; checkpoint; mv %(raydir)s/Scaffolds.fasta %(raydir_orig)s/%(track)s.scaffolds.fa
; mv %(raydir)s/ScaffoldComponents.txt %(raydir_orig)s/%(track)s.scaffold_components.txt
; mv %(raydir)s/ScaffoldLengths.txt %(raydir_orig)s/%(track)s.scaffold_lengths.txt
; mv %(raydir)s/ScaffoldLinks.txt %(raydir_orig)s/%(track)s.scaffold_links.txt
; mv %(raydir)s/Contigs.fasta %(raydir_orig)s/%(track)s.contigs.fa
; mv %(raydir)s/OutputNumbers.txt %(raydir_orig)s/%(track)s.numbers.txt
; mv %(raydir)s/CoverageDistribution.txt %(raydir_orig)s/graph/%(track)s.coverage_distribution.txt
; mkdir %(raydir)s/graph
; mv %(raydir)s/CoverageDistributionAnalysis.txt %(raydir_orig)s/graph/%(track)s.coverage_distribution_analysis.txt
; mv %(raydir)s/degreeDistribution.txt %(raydir_orig)s/graph/%(track)s.degree_distribution.txt
; mv %(raydir)s/Kmers.txt %(raydir_orig)s/graph/%(track)s.kmers.txt
; mkdir %(raydir)s/assembly
; mv %(raydir)s/SeedLengthDistribution.txt %(raydir_orig)s/assembly/%(track)s.seed_length_distribution.txt
; mv %(raydir)s/LibraryStatistics.txt %(raydir_orig)s/%(track)s.library_statistics.txt
; mv %(raydir)s/LibraryData.xml %(raydir_orig)s/%(track)s.library_data.xml
; rm -rf %(tempdir)s''' % locals()
return statement
def buildCodingPotential(infile, outfile):
'''run CPC analysis as in the cpc script.
This module runs framefinder and blastx on both strands.
It seems to work, but I have not thoroughly tested it.
I expect that the false positive rate increases (i.e.,
predicting non-coding as coding) in cases where the best
framefinder match and the best blast match are on opposite
strands. In the original CPC, these would be separated.
'''
try:
cpc_dir = os.environ["CPC_HOME"]
except KeyError:
raise ValueError("CPC_HOME environment variable is not set. ")
tmpdir = P.getTempDir(".")
track = P.snip(outfile, ".coding.gz")
# extract features for frame finder
# replaces extract_framefinder_feats.pl to parse both strands
with open(os.path.join(tmpdir, "ff.feat"), "w") as outf:
outf.write("\t".join(("QueryID", "CDSLength", "Score", "Used",
"Strict")) + "\n")
for line in IOTools.openFile("%s.frame.gz" % track):
if line.startswith(">"):
try:
(id, start, end, score, used, mode, tpe) = \
re.match(
">(\S+).*framefinder \((\d+),(\d+)\) score=(\S+) used=(\S+)% \{(\S+),(\w+)\}", line).groups()
except AttributeError:
raise ValueError("parsing error in line %s" % line)
length = int(end) - int(start) + 1
strict = int(tpe == "strict")
outf.write("\t".join((id, str(length), used, str(strict))) +
"\n")
to_cluster = USECLUSTER
# extract features and prepare svm data
s = []
s.append('''
zcat %(infile)s
| perl %(cpc_dir)s/libs/blast2table.pl
| tee %(tmpdir)s/blastx.table
| perl %(cpc_dir)s/bin/extract_blastx_features.pl
> %(tmpdir)s/blastx.feat1;
''')
s.append('''
cat %(track)s_norepeats.fasta
| perl %(cpc_dir)s/bin/add_missing_entries.pl
%(tmpdir)s/blastx.feat1
> %(tmpdir)s/blastx.feat;
''')
# step 2 - prepare data
s.append('''
perl %(cpc_dir)s/bin/feat2libsvm.pl -c 2,4,6 NA NA %(tmpdir)s/blastx.feat
> %(tmpdir)s/blastx.lsv;
''')
s.append('''
perl %(cpc_dir)s/bin/feat2libsvm.pl -c 2,3,4,5 NA NA %(tmpdir)s/ff.feat
> %(tmpdir)s/ff.lsv;
''')
s.append('''
perl -w %(cpc_dir)s/bin/lsv_cbind.pl %(tmpdir)s/blastx.lsv %(tmpdir)s/ff.lsv
> %(tmpdir)s/test.lsv;
''')
s.append('''
%(cpc_dir)s/libs/libsvm/libsvm-2.81/svm-scale
-r %(cpc_dir)s/data/libsvm.range
%(tmpdir)s/test.lsv
> %(tmpdir)s/test.lsv.scaled;
''')
# step 3: prediction
m_libsvm_model0 = os.path.join(cpc_dir, "data/libsvm.model0") # standard
m_libsvm_model = os.path.join(cpc_dir, "data/libsvm.model") # Prob
m_libsvm_model2 = os.path.join(
cpc_dir, "data/libsvm.model2") # Prob + weighted version
m_libsvm_range = os.path.join(cpc_dir, "data/libsvm.range")
s.append('''
%(cpc_dir)s/libs/libsvm/libsvm-2.81/svm-predict2
%(tmpdir)s/test.lsv.scaled
%(m_libsvm_model0)s
%(tmpdir)s/test.svm0.predict
> %(tmpdir)s/test.svm0.stdout 2> %(tmpdir)s/test.svm0.stderr;
''')
s.append('''
printf "gene_id\\tlength\\tresult\\tvalue\\n"
| gzip > %(outfile)s;
cat %(tmpdir)s/test.svm0.predict
| perl -w %(cpc_dir)s/bin/predict.pl %(track)s_norepeats.fasta
| gzip >> %(outfile)s;
''')
# generate reports
s.append('''cat %(tmpdir)s/blastx.feat
| perl -w %(cpc_dir)s/bin/generate_plot_features.pl %(tmpdir)s/blastx.table <( zcat %(track)s.frame.gz)
| perl -w %(cpc_dir)s/bin/split_plot_features_by_type.pl %(outfile)s.homology %(outfile)s.orf;
gzip %(outfile)s.orf %(outfile)s.homology;
''')
# now run it all
statement = " checkpoint; ".join(s)
P.run()
# clean up
shutil.rmtree(tmpdir)
def filterBamfiles(infile, sentinel):
"""
Pre-process bamfiles prior to peak calling.
i) sort bamfiles
ii) remove unmapped readswith bam2bam.py
iii) remove non-uniquely mapping reads with bam2bam.py (optional)
iv) remove duplicates with Picards MarkDuplicates (optional)
v) remove reads from masked regions with bedtools intersect (optional)
vi) index
"""
# create tempfile for Picard's MarkDuplicates
picard_tmp = picard_tmp = P.getTempDir(PARAMS["scratchdir"])
outfile = P.snip(sentinel, ".sentinel") + ".bam"
# ensure bamfile is sorted,
statement = ["samtools sort @IN@ @OUT@", ]
# remove unmapped reads
statement.append("python %(scriptsdir)s/bam2bam.py"
" --filter=mapped"
" --log=%(outfile)s.log"
" < @[email protected]"
" > @OUT@")
# remove non-uniquely mapping reads, if requested
if PARAMS["filter_remove_non_unique"]:
statement.append("python %(scriptsdir)s/bam2bam.py"
" --filter=unique"
" --log=%(outfile)s.log"
" < @IN@"
" > @OUT@")
# remove duplicates, if requested
if PARAMS["filter_remove_duplicates"]:
statement.append("MarkDuplicates"
" INPUT=@IN@"
" ASSUME_SORTED=true"
" REMOVE_DUPLICATES=true"
" QUIET=false"
" OUTPUT=@OUT@"
" METRICS_FILE=/dev/null"
" VALIDATION_STRINGENCY=SILENT"
" TMP_DIR=%(picard_tmp)s"
" 2> %(outfile)s.log")
# mask regions, if intervals supplied
if PARAMS["filter_mask_intervals"]:
mask = PARAMS["filter_mask_intervals"]
statement.append("bedtools intersect"
" -abam @IN@"
" -b %(mask)s"
" -wa"
" -v"
" > @OUT@")
statement.append("mv @IN@ %(outfile)s")
statement.append("samtools index %(outfile)s")
job_options = "-l mem_free=10G"
statement = P.joinStatements(statement, infile)
P.run()
P.touch(sentinel)
shutil.rmtree(picard_tmp)
def build(self, infile):
'''
build statement for running Ray
'''
track = self.getTrack(os.path.basename(infile))
format = self.getFormat(infile)
paired = self.checkPairs(infile)
tempdir = P.getTempDir(dir=".")
# check whether the data are paired-end
if not paired:
pair = paired
files = os.path.join(
tempdir, P.snip(os.path.basename(infile), ".gz"))
gunzy = "gunzip -c %(infile)s > %(files)s" % locals()
else:
pair = paired[0]
# Ray doesn't like .fastq.1.gz etc
read1 = infile
read2 = paired[1]
read1_new = os.path.join(
tempdir, read1.replace(".fastq.1.gz", ".1.fastq"))
read2_new = os.path.join(
tempdir, read2.replace(".fastq.2.gz", ".2.fastq"))
files = " ".join([read1_new, read2_new])
gunzy = """gunzip -c %(read1)s > %(read1_new)s
; gunzip -c %(read2)s > %(read2_new)s""" % locals()
# ray likes an output directory but needs it not
# to exist beforehand
raydir = os.path.join(os.getcwd(), "ray.dir/export_%s" % track)
raydir_orig = os.path.join(os.getcwd(), "ray.dir")
# Ray picks up file types so should just have to
# say whether its paired or not
# build statement
common_options = "-k %(kmer)s"
if pair == "interleaved":
filetype = "-i"
elif not pair:
filetype = "-s"
elif pair == "separate":
filetype = "-p"
else:
raise IOError, "do not support file of this type: %s" % infile
# note restrict use to 10 cores
statement = ''' %(gunzy)s
; mpiexec %%(ray_executable)s %(common_options)s %(filetype)s %(files)s -o %(raydir)s >> %(raydir_orig)s/%(track)s.log
; checkpoint; mv %(raydir)s/Scaffolds.fasta %(raydir_orig)s/%(track)s.scaffolds.fa
; mv %(raydir)s/ScaffoldComponents.txt %(raydir_orig)s/%(track)s.scaffold_components.txt
; mv %(raydir)s/ScaffoldLengths.txt %(raydir_orig)s/%(track)s.scaffold_lengths.txt
; mv %(raydir)s/ScaffoldLinks.txt %(raydir_orig)s/%(track)s.scaffold_links.txt
; cat %(raydir)s/Contigs.fasta | python %%(scriptsdir)s/rename_contigs.py -a ray --log=%(raydir_orig)s/%(track)s.contigs.log
> %(raydir_orig)s/%(track)s.contigs.fa
; mv %(raydir)s/OutputNumbers.txt %(raydir_orig)s/%(track)s.numbers.txt
; mv %(raydir)s/CoverageDistribution.txt %(raydir_orig)s/graph/%(track)s.coverage_distribution.txt
; mkdir %(raydir)s/graph
; mv %(raydir)s/CoverageDistributionAnalysis.txt %(raydir_orig)s/graph/%(track)s.coverage_distribution_analysis.txt
; mv %(raydir)s/degreeDistribution.txt %(raydir_orig)s/graph/%(track)s.degree_distribution.txt
; mv %(raydir)s/Kmers.txt %(raydir_orig)s/graph/%(track)s.kmers.txt
; mkdir %(raydir)s/assembly
; mv %(raydir)s/SeedLengthDistribution.txt %(raydir_orig)s/assembly/%(track)s.seed_length_distribution.txt
; mv %(raydir)s/LibraryStatistics.txt %(raydir_orig)s/%(track)s.library_statistics.txt
; mv %(raydir)s/LibraryData.xml %(raydir_orig)s/%(track)s.library_data.xml
; rm -rf %(tempdir)s''' % locals()
return statement
def buildCodingPotential( infile, outfile ):
'''run CPC analysis as in the cpc script.
This module runs framefinder and blastx on both strands.
It seems to work, but I have not thoroughly tested it.
I expect that the false positive rate increases (i.e.,
predicting non-coding as coding) in cases where the best
framefinder match and the best blast match are on opposite
strands. In the original CPC, these would be separated.
'''
try:
cpc_dir = os.environ["CPC_HOME"]
except KeyError:
raise ValueError("CPC_HOME environment variable is not set. ")
tmpdir = P.getTempDir( ".")
track = P.snip( outfile, ".coding.gz" )
# extract features for frame finder
# replaces extract_framefinder_feats.pl to parse both strands
with open( os.path.join(tmpdir, "ff.feat"), "w") as outf:
outf.write( "\t".join(("QueryID", "CDSLength", "Score", "Used", "Strict")) + "\n")
for line in IOTools.openFile( "%s.frame.gz" % track ):
if line.startswith(">"):
try:
( id, start, end, score, used, mode, tpe) = \
re.match(
">(\S+).*framefinder \((\d+),(\d+)\) score=(\S+) used=(\S+)% \{(\S+),(\w+)\}", line ).groups()
except AttributeError:
raise ValueError( "parsing error in line %s" % line )
length = int(end) - int(start) + 1
strict = int(tpe == "strict")
outf.write( "\t".join( (id, str(length), used, str(strict )) )+ "\n")
to_cluster = USECLUSTER
# extract features and prepare svm data
s = []
s.append( '''
zcat %(infile)s
| perl %(cpc_dir)s/libs/blast2table.pl
| tee %(tmpdir)s/blastx.table
| perl %(cpc_dir)s/bin/extract_blastx_features.pl
> %(tmpdir)s/blastx.feat1;
''' )
s.append( '''
cat %(track)s_norepeats.fasta
| perl %(cpc_dir)s/bin/add_missing_entries.pl
%(tmpdir)s/blastx.feat1
> %(tmpdir)s/blastx.feat;
''')
# step 2 - prepare data
s.append( '''
perl %(cpc_dir)s/bin/feat2libsvm.pl -c 2,4,6 NA NA %(tmpdir)s/blastx.feat
> %(tmpdir)s/blastx.lsv;
''' )
s.append( '''
perl %(cpc_dir)s/bin/feat2libsvm.pl -c 2,3,4,5 NA NA %(tmpdir)s/ff.feat
> %(tmpdir)s/ff.lsv;
''' )
s.append( '''
perl -w %(cpc_dir)s/bin/lsv_cbind.pl %(tmpdir)s/blastx.lsv %(tmpdir)s/ff.lsv
> %(tmpdir)s/test.lsv;
''' )
s.append( '''
%(cpc_dir)s/libs/libsvm/libsvm-2.81/svm-scale
-r %(cpc_dir)s/data/libsvm.range
%(tmpdir)s/test.lsv
> %(tmpdir)s/test.lsv.scaled;
''' )
# step 3: prediction
m_libsvm_model0=os.path.join( cpc_dir, "data/libsvm.model0") # standard
m_libsvm_model=os.path.join( cpc_dir, "data/libsvm.model") # Prob
m_libsvm_model2=os.path.join( cpc_dir, "data/libsvm.model2" ) # Prob + weighted version
m_libsvm_range=os.path.join( cpc_dir, "data/libsvm.range" )
s.append( '''
%(cpc_dir)s/libs/libsvm/libsvm-2.81/svm-predict2
%(tmpdir)s/test.lsv.scaled
%(m_libsvm_model0)s
%(tmpdir)s/test.svm0.predict
> %(tmpdir)s/test.svm0.stdout 2> %(tmpdir)s/test.svm0.stderr;
''' )
s.append( '''
printf "gene_id\\tlength\\tresult\\tvalue\\n"
| gzip > %(outfile)s;
cat %(tmpdir)s/test.svm0.predict
| perl -w %(cpc_dir)s/bin/predict.pl %(track)s_norepeats.fasta
| gzip >> %(outfile)s;
''' )
# generate reports
s.append( '''cat %(tmpdir)s/blastx.feat
| perl -w %(cpc_dir)s/bin/generate_plot_features.pl %(tmpdir)s/blastx.table <( zcat %(track)s.frame.gz)
| perl -w %(cpc_dir)s/bin/split_plot_features_by_type.pl %(outfile)s.homology %(outfile)s.orf;
gzip %(outfile)s.orf %(outfile)s.homology;
''' )
# now run it all
statement = " checkpoint; ".join( s )
P.run()
# clean up
shutil.rmtree( tmpdir )
def preprocess( self, infiles, outfile ):
'''build preprocessing statement
Build a command line statement that extracts/converts
various input formats to fastq formatted files.
Mapping qualities are changed to solexa format.
returns the statement and the fastq files to map.
'''
assert len(infiles) > 0, "no input files for mapping"
tmpdir_fastq = P.getTempDir()
# create temporary directory again for nodes
statement = [ "mkdir -p %s" % tmpdir_fastq ]
fastqfiles = []
# get track by extension of outfile
track = os.path.splitext( os.path.basename( outfile ) )[0]
if self.compress:
compress_cmd = "| gzip"
extension = ".gz"
else:
compress_cmd = ""
extension = ""
for infile in infiles:
if infile.endswith( ".export.txt.gz"):
# single end illumina export
statement.append( """gunzip < %(infile)s
| awk '$11 != "QC" || $10 ~ /(\d+):(\d+):(\d+)/ \
{ if ($1 != "")
{ readname=sprintf( "%%%%s_%%%%s:%%%%s:%%%%s:%%%%s:%%%%s", $1,$2,$3,$4,$5,$6);}
else { readname=sprintf( "%%%%s:%%%%s:%%%%s:%%%%s:%%%%s", $1,$3,$4,$5,$6); }
printf("@%%%%s\\n%%%%s\\n+\\n%%%%s\\n",readname,$9,$10);}'
%(compress_cmd)s
> %(tmpdir_fastq)s/%(track)s.fastq%(extension)s""" % locals() )
fastqfiles.append( ("%s/%s.fastq%s" % (tmpdir_fastq, track, extension ),) )
elif infile.endswith( ".fa.gz" ):
statement.append( '''gunzip < %(infile)s > %(tmpdir_fastq)s/%(track)s.fa''' % locals() )
fastqfiles.append( ("%s/%s.fa" % (tmpdir_fastq, track ),) )
self.datatype = "fasta"
elif infile.endswith( ".sra"):
# sneak preview to determine if paired end or single end
outdir = P.getTempDir()
# --split-files is present in fastq-dump 2.1.7
P.execute( "fastq-dump --split-files --gzip -X 1000 --outdir %(outdir)s %(infile)s" % locals() )
# --split-files will create files called prefix_#.fastq.gz
# where # is the read number.
# The following cases are:
# * file cotains paired end data: output = prefix_1.fastq.gz, prefix_2.fastq.gz
# * special case: unpaired reads in a paired end run end up in prefix.fastq.gz
# * special case: if paired reads are stored in a single read, fastq-dump will split.
# There might be a joining sequence. The output would thus be:
# prefix_1.fastq.gz, prefix_2.fastq.gz and prefix_3.fastq.gz
# You want files 1 and 3.
f = sorted(glob.glob( os.path.join( outdir, "*.fastq.gz" ) ))
ff = [ os.path.basename(x) for x in f ]
if len(f) == 1:
# sra file contains one read: output = prefix.fastq.gz
pass
elif len(f) == 2:
# sra file contains read pairs: output = prefix_1.fastq.gz, prefix_2.fastq.gz
assert ff[0].endswith( "_1.fastq.gz") and ff[1].endswith( "_2.fastq.gz" )
elif len(f) == 3:
if ff[2].endswith( "_3.fastq.gz"):
f = glob.glob( os.path.join( outdir, "*_[13].fastq.gz" ) )
else:
f = glob.glob( os.path.join( outdir, "*_[13].fastq.gz" ) )
E.info("sra file contains the following files: %s" % f )
shutil.rmtree( outdir )
fastqfiles.append( [ "%s/%s" % (tmpdir_fastq, os.path.basename( x )) for x in sorted(f) ] )
statement.append( "fastq-dump --split-files --gzip --outdir %(tmpdir_fastq)s %(infile)s" % locals() )
elif infile.endswith( ".fastq.gz" ):
format = Fastq.guessFormat( IOTools.openFile( infile, "r"), raises = False)
if 'sanger' not in format and self.convert:
statement.append( """gunzip < %(infile)s
| python %%(scriptsdir)s/fastq2fastq.py --change-format=sanger --guess-format=phred64 --log=%(outfile)s.log
%(compress_cmd)s
> %(tmpdir_fastq)s/%(track)s.fastq%(extension)s""" % locals() )
fastqfiles.append( ("%s/%s.fastq%s" % (tmpdir_fastq, track, extension),) )
else:
E.debug( "%s: assuming quality score format %s" % (infile, format ) )
fastqfiles.append( (infile, ) )
elif infile.endswith( ".csfasta.gz" ):
# single end SOLiD data
if self.preserve_colourspace:
quality = P.snip( infile, ".csfasta.gz" ) + ".qual.gz"
if not os.path.exists( quality ):
raise ValueError( "no quality file for %s" % infile )
statement.append( """gunzip < %(infile)s
> %(tmpdir_fastq)s/%(track)s.csfasta%(extension)s""" % locals() )
statement.append( """gunzip < %(quality)s
> %(tmpdir_fastq)s/%(track)s.qual%(extension)s""" % locals() )
fastqfiles.append( ("%s/%s.csfasta%s" % (tmpdir_fastq, track, extension ),
"%s/%s.qual%s" % (tmpdir_fastq, track, extension) ) )
self.datatype = "solid"
else:
quality = P.snip( infile, ".csfasta.gz" ) + ".qual.gz"
statement.append( """solid2fastq <(gunzip < %(infile)s) <(gunzip < %(quality)s)
%(compress_cmd)s
> %(tmpdir_fastq)s/%(track)s.fastq%(extension)""" % locals() )
fastqfiles.append( ("%s/%s.fastq%s" % (tmpdir_fastq, track, extension),) )
elif infile.endswith( ".csfasta.F3.gz" ):
# paired end SOLiD data
if self.preserve_colourspace:
bn = P.snip( infile, ".csfasta.F3.gz" )
# order is important - mirrors tophat reads followed by quals
f = []
for suffix in ("csfasta.F3", "csfasta.F5", "qual.F3", "qual.F5" ):
fn = "%(bn)s.%(suffix)s" % locals()
if not os.path.exists( fn + ".gz"): raise ValueError( "expected file %s.gz missing" % fn )
statement.append( """gunzip < %(fn)s.gz
%(compress_cmd)s
> %(tmpdir_fastq)s/%(track)s.%(suffix)s%(extension)s""" % locals() )
f.append( "%(tmpdir_fastq)s/%(track)s.%(suffix)s%(extension)s" % locals() )
fastqfiles.append( f )
self.datatype = "solid"
else:
quality = P.snip( infile, ".csfasta.gz" ) + ".qual.gz"
statement.append( """solid2fastq <(gunzip < %(infile)s) <(gunzip < %(quality)s)
%(compress_cmd)s
> %(tmpdir_fastq)s/%(track)s.fastq%(extension)s""" % locals() )
fastqfiles.append( ("%s/%s.fastq%s" % (tmpdir_fastq, track, extension),) )
elif infile.endswith( ".fastq.1.gz" ):
bn = P.snip( infile, ".fastq.1.gz" )
infile2 = "%s.fastq.2.gz" % bn
if not os.path.exists( infile2 ):
raise ValueError("can not find paired ended file '%s' for '%s'" % (infile2, infile))
format = Fastq.guessFormat( IOTools.openFile( infile ), raises = False )
if 'sanger' not in format:
statement.append( """gunzip < %(infile)s
| python %%(scriptsdir)s/fastq2fastq.py --change-format=sanger --guess-format=phred64 --log=%(outfile)s.log
%(compress_cmd)s
> %(tmpdir_fastq)s/%(track)s.1.fastq%(extension)s;
gunzip < %(infile2)s
| python %%(scriptsdir)s/fastq2fastq.py --change-format=sanger --guess-format=phred64 --log=%(outfile)s.log
%(compress_cmd)s
> %(tmpdir_fastq)s/%(track)s.2.fastq%(extension)s
""" % locals() )
fastqfiles.append( ("%s/%s.1.fastq%s" % (tmpdir_fastq, track, extension),
"%s/%s.2.fastq%s" % (tmpdir_fastq, track, extension) ) )
else:
E.debug( "%s: assuming quality score format %s" % (infile, format ) )
fastqfiles.append( (infile,
infile2, ) )
else:
raise NotImplementedError( "unknown file format %s" % infile )
self.tmpdir_fastq = tmpdir_fastq
assert len(fastqfiles) > 0, "no fastq files for mapping"
return "; ".join( statement) + ";", fastqfiles
def GATKpreprocessing(infile, outfile):
'''Reorders BAM according to reference fasta and add read groups using
SAMtools, realigns around indels and recalibrates base quality scores
using GATK'''
to_cluster = USECLUSTER
track = P.snip(os.path.basename(infile), ".bam")
tmpdir_gatk = P.getTempDir('/ifs/scratch')
job_options = getGATKOptions()
library = PARAMS["readgroup_library"]
platform = PARAMS["readgroup_platform"]
platform_unit = PARAMS["readgroup_platform_unit"]
threads = PARAMS["gatk_threads"]
dbsnp = PARAMS["gatk_dbsnp"]
solid_options = PARAMS["gatk_solid_options"]
# need to unload java before runnning GATK as it now runs on java version 7
# full path to .jar file being specified as using module "GenomeAnalysisTK"
# resulted in error: "Could not find the main class:
# org.broadinstitute.sting.gatk.CommandLineGATK. Program will exit."
# This error is seen when java version 6 is used
# Find out why this error occurs when not specifying full path
statement = '''module unload apps/java/jre1.6.0_26; checkpoint;'''
statement += '''ReorderSam INPUT=%(infile)s
OUTPUT=%(tmpdir_gatk)s/%(track)s.reordered.bam
REFERENCE=%%(bwa_index_dir)s/%%(genome)s.fa
ALLOW_INCOMPLETE_DICT_CONCORDANCE=true
VALIDATION_STRINGENCY=SILENT ;
checkpoint ;''' % locals()
statement += '''samtools index %(tmpdir_gatk)s/%(track)s.reordered.bam ;
checkpoint ;''' % locals()
statement += '''AddOrReplaceReadGroups
INPUT=%(tmpdir_gatk)s/%(track)s.reordered.bam
OUTPUT=%(tmpdir_gatk)s/%(track)s.readgroups.bam
RGLB=%(library)s RGPL=%(platform)s
RGPU=%(platform_unit)s RGSM=%(track)s
VALIDATION_STRINGENCY=SILENT ;
checkpoint ;''' % locals()
statement += '''samtools index %(tmpdir_gatk)s/%(track)s.readgroups.bam ;
checkpoint ;''' % locals()
statement += '''java -Xmx4g -jar
/ifs/apps/bio/GATK-2.7-2/GenomeAnalysisTK.jar
-T RealignerTargetCreator
-o %(tmpdir_gatk)s/%(track)s.indelrealignment.intervals
-R %%(bwa_index_dir)s/%%(genome)s.fa
-I %(tmpdir_gatk)s/%(track)s.readgroups.bam ;
checkpoint ;''' % locals()
statement += '''java -Xmx4g -jar
/ifs/apps/bio/GATK-2.7-2/GenomeAnalysisTK.jar
-T IndelRealigner
-o %(tmpdir_gatk)s/%(track)s.indelrealigned.bam
-R %%(bwa_index_dir)s/%%(genome)s.fa
-I %(tmpdir_gatk)s/%(track)s.readgroups.bam
-targetIntervals %(tmpdir_gatk)s/%(track)s.indelrealignment.intervals ;
checkpoint ;''' % locals()
statement += '''java -Xmx4g -jar
/ifs/apps/bio/GATK-2.7-2/GenomeAnalysisTK.jar
-T BaseRecalibrator
--out %(tmpdir_gatk)s/%(track)s.recal.grp
-R %%(bwa_index_dir)s/%%(genome)s.fa
-I %(tmpdir_gatk)s/%(track)s.indelrealigned.bam
--knownSites %(dbsnp)s %(solid_options)s ;
checkpoint ;''' % locals()
statement += '''java -Xmx4g -jar
/ifs/apps/bio/GATK-2.7-2/GenomeAnalysisTK.jar
-T PrintReads -o %(outfile)s
-BQSR %(tmpdir_gatk)s/%(track)s.recal.grp
-R %%(bwa_index_dir)s/%%(genome)s.fa
-I %(tmpdir_gatk)s/%(track)s.indelrealigned.bam ;
checkpoint ;''' % locals()
statement += '''rm -rf %(tmpdir_gatk)s ;'''
P.run()
def build(self, infile):
'''
build statement for running SGA
'''
track = self.getTrack(os.path.basename(infile))
# decide which algorithm to use based on
# read length
if "%(sga_long)s":
index_algorithm = "sais"
else:
index_algorithm = "ropebwt"
format = self.getFormat(infile)
paired = self.checkPairs(infile)
# directory in which to do the assembly
tempdir = P.getTempDir(dir=".")
# check whether the data are paired-end
if not paired:
pe_mode = "--pe-mode=0"
files = os.path.abspath(infile)
else:
# DOESN'T DEAL WITH INTERLEAVED FILES YET
pe_mode = "--pe-mode=1"
files = " ".join([os.path.abspath(infile), os.path.abspath(paired[1])])
executable = "%(sga_executable)s"
outdir = os.path.abspath("sga.dir")
###############################################
# preprocessing step converts missing bases to
# random bases or removes sequences with
# missing bases
###############################################
preprocess_options = "%(sga_preprocess_options)s"
# outputs a merged fastq file
outf_preprocessed = track + ".fastq"
preprocess_statement = "cd %(tempdir)s; %(executable)s preprocess %(pe_mode)s %(preprocess_options)s %(files)s \
-o %(outf_preprocessed)s 2> %(outdir)s/%(track)s_preprocess.log"
###############################################
# indexing reads with FM index
###############################################
index_options = "%(sga_index_options)s"
index_statement = "%(executable)s index --algorithm=%(index_algorithm)s \
%(outf_preprocessed)s 2> %(outdir)s/%(track)s_index.log"
###############################################
# correct sequencing errors in reads
###############################################
correction_method = "%(sga_correction_method)s"
# if correction_method == "kmer":
# ADD WARNING HERE
correction_options = "%(sga_kmer_correction_options)s"
# elif correction_method == "hybrid":
# correction_options = "%(sga_hybrid_correction_options)s"
# elif correction_method == "overlap":
# correction_options = "%(sga_overlap_correction_options)s"
# else:
# raise ValueError("method %s does not exist: choose one of kmer, hybrid, overlap" % correction_method)
outf_corrected = track + "_corrected.fa"
metrics = "--metrics=%(track)s.metrics" % locals()
correction_prefix = os.path.join(
tempdir, P.snip(outf_corrected, ".fa"))
correction_statement = "%(executable)s correct %(metrics)s \
--algorithm=%(correction_method)s \
%(correction_options)s \
%(outf_preprocessed)s \
-o %(outf_corrected)s 2> %(outdir)s/%(track)s_corrected.log"
###############################################
# filter low quality reads and low abundance
# kmers
###############################################
filter_options = "%(sga_filter_options)s"
outf_filtered = track + "_filtered.fa"
filter_statement = "sga index %(outf_corrected)s; \
%(executable)s filter %(filter_options)s \
-o %(outf_filtered)s \
%(outf_corrected)s 2> %(outdir)s/%(track)s_filtered.log"
###############################################
# overlap reads
###############################################
# Note "asqg" is the default output from sga
outf_overlap = track + "_filtered.asqg.gz"
threads = "%(sga_threads)s"
overlap_options = "%(sga_overlap_options)s"
overlap_statement = "%(executable)s overlap %(overlap_options)s \
%(outf_filtered)s 2> %(outdir)s/%(track)s_overlap.log"
###############################################
# assemble reads and perform error removal
###############################################
assembly_options = "%(sga_assembly_options)s"
error_removal_options = "%(sga_error_removal_options)s"
out_prefix = track
assembly_statement = "%(executable)s assemble %(assembly_options)s \
%(outf_overlap)s \
--out-prefix=%(out_prefix)s 2> %(outdir)s/%(track)s_contigs.log"
###############################################
# build statement
###############################################
metrics_file = os.path.basename(metrics.replace("--metrics=", ""))
contigs_file = os.path.basename(out_prefix + "-contigs.fa")
move_statement = "mv %(metrics_file)s %(outdir)s/%(metrics_file)s; \
cat %(contigs_file)s \
| python %%(scriptsdir)s/rename_contigs.py \
--log=%(outdir)s/%(track)s.contigs.log \
-a sga > %(outdir)s/%(track)s.contigs.fa"
statement = "; ".join([preprocess_statement,
index_statement,
correction_statement,
filter_statement,
overlap_statement,
assembly_statement,
move_statement,
"rm -rf %(tempdir)s"]) % locals()
return statement
def filterBamfiles(infile, sentinel):
"""
Pre-process bamfiles prior to peak calling.
i) sort bamfiles
ii) remove unmapped readswith bam2bam.py
iii) remove non-uniquely mapping reads with bam2bam.py (optional)
iv) remove duplicates with Picards MarkDuplicates (optional)
v) remove reads from masked regions with bedtools intersect (optional)
vi) index
"""
# create tempfile for Picard's MarkDuplicates
picard_tmp = picard_tmp = P.getTempDir(PARAMS["scratchdir"])
outfile = P.snip(sentinel, ".sentinel") + ".bam"
# ensure bamfile is sorted,
statement = [
"samtools sort @IN@ @OUT@",
]
# remove unmapped reads
statement.append("python %(scriptsdir)s/bam2bam.py"
" --filter=mapped"
" --log=%(outfile)s.log"
" < @[email protected]"
" > @OUT@")
# remove non-uniquely mapping reads, if requested
if PARAMS["filter_remove_non_unique"]:
statement.append("python %(scriptsdir)s/bam2bam.py"
" --filter=unique"
" --log=%(outfile)s.log"
" < @IN@"
" > @OUT@")
# remove duplicates, if requested
if PARAMS["filter_remove_duplicates"]:
statement.append("MarkDuplicates"
" INPUT=@IN@"
" ASSUME_SORTED=true"
" REMOVE_DUPLICATES=true"
" QUIET=false"
" OUTPUT=@OUT@"
" METRICS_FILE=/dev/null"
" VALIDATION_STRINGENCY=SILENT"
" TMP_DIR=%(picard_tmp)s"
" 2> %(outfile)s.log")
# mask regions, if intervals supplied
if PARAMS["filter_mask_intervals"]:
mask = PARAMS["filter_mask_intervals"]
statement.append("bedtools intersect"
" -abam @IN@"
" -b %(mask)s"
" -wa"
" -v"
" > @OUT@")
statement.append("mv @IN@ %(outfile)s")
statement.append("samtools index %(outfile)s")
job_options = "-l mem_free=10G"
statement = P.joinStatements(statement, infile)
P.run()
P.touch(sentinel)
shutil.rmtree(picard_tmp)
def runMAST(infiles, outfile):
'''run mast on all intervals and motifs.
Collect all results for an E-value up to 10000 so that
all sequences are output and MAST curves can be computed.
10000 is a heuristic.
'''
to_cluster = True
# job_options = "-l mem_free=8000M"
controlfile, dbfile, motiffiles = infiles
if IOTools.isEmpty(dbfile):
P.touch(outfile)
return
if not os.path.exists(controlfile):
raise P.PipelineError("control file %s for %s does not exist" %
(controlfile, dbfile))
# remove previous results
if os.path.exists(outfile): os.remove(outfile)
tmpdir = P.getTempDir(".")
tmpfile = P.getTempFilename(".")
for motiffile in motiffiles:
if IOTools.isEmpty(motiffile):
L.info("skipping empty motif file %s" % motiffile)
continue
of = IOTools.openFile(tmpfile, "a")
motif, x = os.path.splitext(motiffile)
of.write(":: motif = %s - foreground ::\n" % motif)
of.close()
# mast bails if the number of nucleotides gets larger than
# 2186800982?
# To avoid this, run db and control file separately.
statement = '''
cat %(dbfile)s
| mast %(motiffile)s - -nohtml -oc %(tmpdir)s -ev %(mast_evalue)f %(mast_options)s >> %(outfile)s.log 2>&1;
cat %(tmpdir)s/mast.txt >> %(tmpfile)s 2>&1
'''
P.run()
of = IOTools.openFile(tmpfile, "a")
motif, x = os.path.splitext(motiffile)
of.write(":: motif = %s - background ::\n" % motif)
of.close()
statement = '''
cat %(controlfile)s
| mast %(motiffile)s - -nohtml -oc %(tmpdir)s -ev %(mast_evalue)f %(mast_options)s >> %(outfile)s.log 2>&1;
cat %(tmpdir)s/mast.txt >> %(tmpfile)s 2>&1
'''
P.run()
statement = "gzip < %(tmpfile)s > %(outfile)s"
P.run()
shutil.rmtree(tmpdir)
os.unlink(tmpfile)
def build(self, infile):
'''
build statement for running SGA
'''
track = self.getTrack(os.path.basename(infile))
# decide which algorithm to use based on
# read length
if "%(sga_long)s":
index_algorithm = "sais"
else:
index_algorithm = "ropebwt"
format = self.getFormat(infile)
paired = self.checkPairs(infile)
# directory in which to do the assembly
tempdir = P.getTempDir(dir=".")
# check whether the data are paired-end
if not paired:
pe_mode = "--pe-mode=0"
files = os.path.abspath(infile)
else:
# DOESN'T DEAL WITH INTERLEAVED FILES YET
pe_mode = "--pe-mode=1"
files = " ".join([os.path.abspath(infile), os.path.abspath(paired[1])])
executable = "%(sga_executable)s"
outdir = os.path.abspath("sga.dir")
###############################################
# preprocessing step converts missing bases to
# random bases or removes sequences with
# missing bases
###############################################
preprocess_options = "%(sga_preprocess_options)s"
# outputs a merged fastq file
outf_preprocessed = track + ".fastq"
preprocess_statement = "cd %(tempdir)s; %(executable)s preprocess %(pe_mode)s %(preprocess_options)s %(files)s \
-o %(outf_preprocessed)s 2> %(outdir)s/%(track)s_preprocess.log"
###############################################
# indexing reads with FM index
###############################################
index_options = "%(sga_index_options)s"
index_statement = "%(executable)s index --algorithm=%(index_algorithm)s \
%(outf_preprocessed)s 2> %(outdir)s/%(track)s_index.log"
###############################################
# correct sequencing errors in reads
###############################################
correction_method = "%(sga_correction_method)s"
# if correction_method == "kmer":
# ADD WARNING HERE
correction_options = "%(sga_kmer_correction_options)s"
# elif correction_method == "hybrid":
# correction_options = "%(sga_hybrid_correction_options)s"
# elif correction_method == "overlap":
# correction_options = "%(sga_overlap_correction_options)s"
# else:
# raise ValueError("method %s does not exist: choose one of kmer, hybrid, overlap" % correction_method)
outf_corrected = track + "_corrected.fa"
metrics = "--metrics=%(track)s.metrics" % locals()
correction_prefix = os.path.join(
tempdir, P.snip(outf_corrected, ".fa"))
correction_statement = "%(executable)s correct %(metrics)s \
--algorithm=%(correction_method)s \
%(correction_options)s \
%(outf_preprocessed)s \
-o %(outf_corrected)s 2> %(outdir)s/%(track)s_corrected.log"
###############################################
# filter low quality reads and low abundance
# kmers
###############################################
filter_options = "%(sga_filter_options)s"
outf_filtered = track + "_filtered.fa"
filter_statement = "sga index %(outf_corrected)s; \
%(executable)s filter %(filter_options)s \
-o %(outf_filtered)s \
%(outf_corrected)s 2> %(outdir)s/%(track)s_filtered.log"
###############################################
# overlap reads
###############################################
# Note "asqg" is the default output from sga
outf_overlap = track + "_filtered.asqg.gz"
threads = "%(sga_threads)s"
overlap_options = "%(sga_overlap_options)s"
overlap_statement = "%(executable)s overlap %(overlap_options)s \
%(outf_filtered)s 2> %(outdir)s/%(track)s_overlap.log"
###############################################
# assemble reads and perform error removal
###############################################
assembly_options = "%(sga_assembly_options)s"
error_removal_options = "%(sga_error_removal_options)s"
out_prefix = track
assembly_statement = "%(executable)s assemble %(assembly_options)s \
%(outf_overlap)s \
--out-prefix=%(out_prefix)s 2> %(outdir)s/%(track)s_contigs.log"
###############################################
# build statement
###############################################
metrics_file = os.path.basename(metrics.replace("--metrics=", ""))
contigs_file = os.path.basename(out_prefix + "-contigs.fa")
move_statement = "mv %(metrics_file)s %(outdir)s/%(metrics_file)s; \
cat %(contigs_file)s \
| python %%(scriptsdir)s/rename_contigs.py \
--log=%(outdir)s/%(track)s.contigs.log \
-a sga > %(outdir)s/%(track)s.contigs.fa"
statement = "; ".join([preprocess_statement,
index_statement,
correction_statement,
filter_statement,
overlap_statement,
assembly_statement,
move_statement,
"rm -rf %(tempdir)s"]) % locals()
return statement
def build(self, infile):
'''
build statement for running Ray
'''
track = self.getTrack(infile)
format = self.getFormat(infile)
paired = self.checkPairs(infile)
tempdir = P.getTempDir()
# check whether the data are paired-end
if len(paired) > 1:
pair = paired[0]
# Ray doesn't like .fastq.1.gz etc
read1 = infile
read2 = paired[1]
read1_new = os.path.join(tempdir,
read1.replace(".fastq.1.gz", ".1.fastq"))
read2_new = os.path.join(tempdir,
read2.replace(".fastq.2.gz", ".2.fastq"))
files = " ".join([read1_new, read2_new])
else:
pair = paired
files = infile
raydir = os.path.join(os.getcwd(), "ray.dir")
# Ray picks up file types so should just have to
# say whether its paired or not
print files
# build statement
common_options = "-k %(kmer)s"
if pair == "interleaved":
filetype = "-i"
elif not pair:
filetype = "-s"
elif pair == "separate":
filetype = "-p"
else:
raise IOError, "do not support file of this type: %s" % infile
statement = '''gunzip -c %(read1)s > %(read1_new)s
; gunzip -c %(read2)s > %(read2_new)s
; %%(ray_executable)s %(common_options)s %(filetype)s %(files)s -o %(raydir)s
; checkpoint; mv %(raydir)s/Scaffolds.fa %(raydir)s/%(track)s.scaffolds.fa
; mv %(raydir)s/ScaffoldComponents.txt %(raydir)s/%(track)s.scaffold_components.txt
; mv %(raydir)s/ScaffoldLengths.txt %(raydir)s/%(track)s.scaffold_lengths.txt
; mv %(raydir)s/ScaffoldLinks.txt %(raydir)s/%(track)s.scaffold_links.txt
; mv %(raydir)s/Contigs.fa %(raydir)s/%(track)s.contigs.fa#
; mv %(raydir)s/OutputNumbers.txt %(raydir)s/%(track)s.numbers.txt
; mv %(raydir)s/CoverageDistribution.txt %(raydir)s/graph/%(track)s.coverage_distribution.txt
; mkdir %(raydir)s/graph
; mv %(raydir)s/CoverageDistributionAnalysis.txt %(raydir)s/graph/%(track)s.coverage_distribution_analysis.txt
; mv %(raydir)s/degreeDistribution.txt %(raydir)s/graph/%(track)s.degree_distribution.txt
; mv %(raydir)s/Kmers.txt %(raydir)s/graph/%(track)s.kmers.txt
; mkdir %(raydir)s/assembly
; mv %(raydir)s/SeedLengthDistribution.txt %(raydir)s/assembly/%(track)s.seed_length_distribution.txt
; mv %(raydir)s/LibraryStatistics.txt %(raydir)s/%(track)s.library_statistics.txt
; mv %(raydir)s/LibraryData.xml %(raydir)s/%(track)s.library_data.xml
; rm -rf %(tempdir)s''' % locals()
return statement
