def process_remote(infile):
repository, acc = iotools.open_file(infile).readlines()[0].strip().split()
if repository == "ENCODE":
location, filetype = get_encode_file(acc)
elif repository == "URL":
location = acc
if acc.endswith("gz"):
filetype = ".".join(acc.split(".")[-2])
else:
filetype = acc.split(".")[-1]
else:
raise ValueError("repository %s not yet supported" % repository)
tmpfile = P.get_temp_filename(shared=False, suffix="." + filetype)
preamble = "wget %(location)s -O %(tmpfile)s --quiet &&"
postamble = "&& rm %(tmpfile)s"
if filetype == "bam":
preamble += "samtools index %(tmpfile)s && "
postamble += " && rm %(tmpfile)s.bai "
elif filetype == "bed.gz":
tmp2 = P.get_temp_filename(shared=False)
preamble += ''' zcat %(tmpfile)s | sort -k1,1 -k2,2n | bgzip > %(tmp2)s &&
mv %(tmp2)s %(tmpfile)s &&
tabix -p bed %(tmpfile)s && '''
postamble += "&& rm %(tmpfile)s.tbi"
return preamble % locals(), postamble % locals(), tmpfile, filetype
def removeDuplicates(fastq1, outfile):
'''Filter exact duplicates, if specified in config file'''
if IS_PAIRED:
fastq2 = P.snip(fastq1, FASTQ1_SUFFIX) + FASTQ2_SUFFIX
outfile1 = P.snip(outfile, '.gz')
outfile2 = P.snip(outfile1, '.fastq.1') + '.fastq.2'
logfile = P.snip(outfile1, '.fastq.1') + '.log'
cluster_file = P.snip(outfile1, '1') + '*.clstr'
to_filter = PARAMS['cdhit_dedup']
if to_filter:
tmpf1 = P.get_temp_filename('.')
tmpf2 = P.get_temp_filename('.')
statement = ("zcat %(fastq1)s > %(tmpf1)s &&"
" zcat %(fastq2)s > %(tmpf2)s &&"
" cd-hit-dup"
" -i %(tmpf1)s"
" -i2 %(tmpf2)s"
" -o %(outfile1)s"
" -o2 %(outfile2)s"
" %(cdhit_options)s"
" &> %(logfile)s &&"
" gzip %(outfile1)s &&"
" gzip %(outfile2)s &&"
" gzip %(logfile)s &&"
" rm -f %(tmpf1)s &&"
" rm -f %(tmpf2)s &&"
" rm -f %(cluster_file)s")
P.run(statement, job_options=PARAMS['cdhit_run_options'])
else:
E.warn('Deduplication step is being skipped for: %s' % fastq1)
symlnk(fastq1, outfile)
symlnk(fastq2, outfile2 + '.gz')
else:
outfile1 = P.snip(outfile, '.gz')
logfile = P.snip(outfile1, '.fastq.1') + '.log'
cluster_file = P.snip(outfile1, '1') + '*.clstr'
to_filter = PARAMS['preprocess_dedup']
if to_filter:
tmpf1 = P.get_temp_filename('.')
statement = ("zcat %(fastq1)s > %(tmpf1)s"
" cd-hit-dup"
" -i %(tmpf1)s"
" -o %(outfile1)s"
" %(cdhit_options)s"
" &> %(logfile)s &&"
" gzip %(outfile1)s &&"
" gzip %(logfile)s &&"
" rm -f %(tmpf1)s &&"
" rm -f %(cluster_file)s")
P.run(statement, job_options=PARAMS['cdhit_run_options'])
else:
E.warn('Deduplication step is being skipped for: %s' % fastq1)
symlnk(fastq1, outfile)
def assembleWithStringTie(infiles, outfile):
infile, reference = infiles
basefile = os.path.basename(infile)
job_threads = PARAMS["stringtie_threads"]
job_memory = PARAMS["stringtie_memory"]
tmpfile = P.get_temp_filename()
if os.path.exists(tmpfile):
os.unlink(tmpfile)
statement = '''
portcullis full
-t 1
-o portcullis/%(basefile)s/
-r %(portcullis_bedref)s
-b
%(portcullis_fastaref)s
%(infile)s &&
mv portcullis/%(basefile)s/portcullis.filtered.bam %(tmpfile)s &&
rm -r portcullis/%(basefile)s/ &&
stringtie %(tmpfile)s
-p %(stringtie_threads)s
-G <(zcat %(reference)s)
%(stringtie_options)s
2> %(outfile)s.log
| gzip > %(outfile)s &&
rm %(tmpfile)s'''
if infile.endswith(".remote"):
token = glob.glob("gdc-user-token*")
tmpfilename = P.get_temp_filename()
if os.path.exists(tmpfilename):
os.unlink(tmpfilename)
if len(token) > 0:
token = token[0]
else:
token = None
s, infile = Sra.process_remote_BAM(
infile,
token,
tmpfilename,
filter_bed=os.path.join(
PARAMS["annotations_dir"],
PARAMS["annotations_interface_contigs_bed"]))
infile = " ".join(infile)
statement = "; ".join([
"mkdir -p %(tmpfilename)s", s, statement, "rm -r %(tmpfilename)s"
])
P.run(statement, job_condaenv="portcullis")
def merge_bw(infiles, outfile):
"""Merge bigWigs using mergeBigWig"""
infiles = " ".join(infiles)
tmpfile = P.get_temp_filename()
tmpfile2 = P.get_temp_filename()
statement = '''bigWigMerge %(infiles)s %(tmpfile)s &&
LC_COLLATE-C sort -k1,1 -k2,2n -o %(tmpfile2)s %(tmpfile)s &&
bedGraphToBigWig %(tmpfile2)s %(contig_file)s %(outfile)s'''
P.run(statement)
def remove_reads(infiles, outfile):
"""remove all of the reads mapping at least once to the genome"""
infile, pre_trna_genome = infiles
temp_file = P.get_temp_filename(".")
temp_file1 = P.get_temp_filename(".")
statement = """samtools view -h %(infile)s> %(temp_file)s &&
perl %(cribbslab)s/perl/removeGenomeMapper.pl %(pre_trna_genome)s %(temp_file)s %(temp_file1)s &&
samtools view -b %(temp_file1)s > %(outfile)s"""
job_memory = "50G"
P.run(statement)
os.unlink(temp_file)
os.unlink(temp_file1)
def buildGff(infile, outfile):
'''Creates a gff for DEXSeq
This takes the gtf and flattens it to an exon based input
required by DEXSeq. The required python script is provided by DEXSeq
and uses HTSeqCounts.
Parameters
----------
infile : string
Input filename in :term:`gtf` format
outfile : string
A :term:`gff` file for use in DEXSeq
annotations_interface_geneset_all_gtf : string
:term:`PARAMS`. Filename of :term:`gtf` file containing
all ensembl annotations
'''
tmpgff = P.get_temp_filename(".")
statement = "gunzip -c %(infile)s > %(tmpgff)s"
P.run(statement)
ps = PYTHONSCRIPTSDIR
statement = '''python %(ps)s/dexseq_prepare_annotation.py
%(tmpgff)s %(outfile)s'''
P.run(statement, job_condaenv="splicing")
os.unlink(tmpgff)
def runBioProspector(infiles, outfile, dbhandle):
'''run bioprospector for motif discovery.
Bioprospector is run on only the top 10% of peaks.
'''
# bioprospector currently not working on the nodes
to_cluster = False
# only use new nodes, as /bin/csh is not installed
# on the old ones.
# job_options = "-l mem_free=8000M"
tmpfasta = P.get_temp_filename(".")
track = outfile[:-len(".bioprospector")]
nseq = writeSequencesForIntervals(
track,
tmpfasta,
dbhandle,
full=True,
masker="dust",
proportion=P.get_params()["bioprospector_proportion"])
if nseq == 0:
E.warn("%s: no sequences - bioprospector skipped" % track)
iotools.touch_file(outfile)
else:
statement = '''
BioProspector -i %(tmpfasta)s %(bioprospector_options)s -o %(outfile)s > %(outfile)s.log
'''
P.run(statement)
os.unlink(tmpfasta)
def run(self, infiles, outfile, params):
files = " ".join(infiles)
job_threads = params.job_threads
# todo:
# 1. add header.
# 2. do batch+merge sort in order to avoid hitting temporary space limits.
# 3. remove unnecessary info fields while sorting, add them later.
tmpdir = P.get_temp_filename()
retval = P.run(
"mkdir {tmpdir}; "
"bcftools view -h {infiles[0]} "
"| cut -f 1-10 "
"| bgzip > {outfile}; "
"zcat {files} "
"| awk -v OFS='\\t' "
"'!/^#/ && $5 != \"<NON_REF>\" "
"{{$8=\".\";$9=\".\";$6=\".\";$7=\"GT\";$10=\".\"; print}}' "
"2> {outfile}.filter.log "
"| sort -k1,1V -k2,2n "
"--parallel {job_threads} "
"-T {tmpdir} "
"2> {outfile}.sort.log "
"| uniq "
"| bgzip "
">> {outfile}; "
"tabix -p vcf {outfile}; "
"rm -rf {tmpdir} ".format(**locals()))
def map_with_bowtie(infiles, outfile):
"""
map reads with bowtie to get general alignment so features can be counted
over RNA gene_biotypes
"""
fastq, genome = infiles
tmp_fastq = P.get_temp_filename(".")
temp_file = P.get_temp_filename(".")
genome = genome.replace(".fa", "")
statement = """gzip -dc %(fastq)s > %(tmp_fastq)s && bowtie -k 10 -v 2 --best --strata --sam %(genome)s %(tmp_fastq)s 2> %(outfile)s_bowtie.log | samtools view -bS |
samtools sort -T %(temp_file)s -o %(outfile)s &&
samtools index %(outfile)s
"""
job_memory = "15G"
P.run(statement)
def run(self, infile, outfile, params):
if params.reference_bed is None:
raise ValueError("{} requires reference_bed to be set".format(
self.name))
# requires a consistent sort order, so sort both files.
# It also requires the chromosome content to be identical,
# so restrict output to common sets.
tmpf = P.get_temp_filename(clear=True)
tmpf_test, tmpf_truth = tmpf + "_a.bed.gz", tmpf + "_b.bed.gz"
stmnt = standardise_bed_files(tmpf_test, tmpf_truth, infile,
params.reference_bed)
statements = [stmnt]
statements.append("{params.path} intersect "
"-a {tmpf_test} "
"-b {tmpf_truth} "
"-wa "
"| bgzip "
"> {outfile}.shared.bed.gz")
statements.append("{params.path} intersect "
"-a {tmpf_test} "
"-b {tmpf_truth} "
"-wa -v"
"| bgzip "
"> {outfile}.unique_test.bed.gz")
statements.append("{params.path} intersect "
"-b {tmpf_test} "
"-a {tmpf_truth} "
"-wa -v"
"| bgzip "
"> {outfile}.unique_truth.bed.gz")
statements.append("rm -f {tmpf_test} {tmpf_truth}")
for section in self.sections:
statements.append(
"tabix -p bed {outfile}.{section}.bed.gz".format(**locals()))
statement = "; ".join(statements)
retval = P.run(statement.format(**locals()))
# these are small files, so doing it here. Implement tabix.count()
# method
counts = dict()
for section in self.sections:
# with pysam.Tabixfile(outfile + "." + section + ".bed.gz") as inf:
inf = pysam.Tabixfile(outfile + "." + section + ".bed.gz")
counts[section] = len(list(inf.fetch()))
inf.close()
with IOTools.open_file(outfile, "w") as outf:
outf.write("section\tcounts\n")
outf.write("\n".join(
["\t".join(map(str, x)) for x in list(counts.items())]) + "\n")
return retval
def buildBedGraph(infile, outfile):
'''build wiggle files from bam files.
Generate :term:`bigWig` format file from :term:`bam` alignment file
Parameters
----------
infile : str
Input filename in :term:`bam` format
outfile : str
Output filename in :term:`bigwig` format
annotations_interface_contigs : str
:term:`PARAMS`
Input filename in :term:`bed` format
'''
inf = infile[0]
inf_name = inf.replace(".bam", "")
idxstats = infile[1]
# scale by Million reads mapped
reads_mapped = Bamtools.getNumberOfAlignments(inf)
for idx in idxstats:
file_name = idx.replace(".idxstats", "")
if file_name == inf_name:
# pass to a function that extracts the number of reads aligned to
# spike in and human genome
regex = PARAMS['quant_regex'] + "*"
scale = ModuleQuantchip.getSpikeInReads(idx, str(regex))
contig_sizes = ModuleQuantchip.getContigSizes(idx)
else:
continue
tmpfile = P.get_temp_filename()
tmpfile2 = P.get_temp_filename()
job_memory = "30G"
statement = '''bedtools genomecov
-ibam %(inf)s
-g %(contig_sizes)s
-bg
-scale %(scale)f
> %(tmpfile)s &&
sort -k1,1 -k2,2n -o %(tmpfile2)s %(tmpfile)s &&
cat %(tmpfile2)s | grep chr > %(outfile)s &&
rm -f %(tmpfile)s %(tmpfile2)s
'''
P.run(statement)
def downsample(infile, outfile):
'''downsample fastq files using seqtk tool.'''
tmp_file = P.get_temp_filename(".")
statement = '''zcat %(infile)s > %(tmp_file)s && seqtk sample -2 -s100 %(tmp_file)s %(downsample_read)s | gzip > %(outfile)s'''
job_memory = "30G"
P.run(statement)
os.unlink(tmp_file)
def map_tran_gene(outfile):
''''Add an identifier to the transcript IDs'''
tmp_cdna = P.get_temp_filename(".")
statement = '''zcat < %(cdna_fasta)s | awk '/^>/ {print $0}' | tr "_" " " | awk '{print $3}' > %(tmp_cdna)s &&
cat %(tmp_cdna)s | awk '{print $0"."NR}' > %(outfile)s'''
P.run(statement)
def map_tr2gene(infile, outfile):
'''Map the transcripts to genes.'''
tmp_cdna = P.get_temp_filename(".")
statement = '''zcat < %(cdna_fasta)s | awk '/^>/ {print $0}' | tr "_" " " | awk '{print $3}' > %(tmp_cdna)s &&
awk 'NR==FNR{a[$1]=$2; b[$1]=$3;next} {$2=a[$1];$3=b[$1]} 1' %(infile)s %(tmp_cdna)s > %(outfile)s'''
P.run(statement)
def remove_reads(infiles, outfile):
"""remove all of the reads mapping at least once to the genome"""
infile, pre_trna_genome = infiles
temp_file = P.get_temp_filename(".")
temp_file1 = P.get_temp_filename(".")
PY_SRC_PATH = os.path.abspath(os.path.dirname(__file__))
statement = """samtools view -h %(infile)s> %(temp_file)s &&
perl %(PY_SRC_PATH)s/perl/removeGenomeMapper.pl %(pre_trna_genome)s %(temp_file)s %(temp_file1)s &&
samtools view -b %(temp_file1)s > %(outfile)s""" % locals()
job_memory = "50G"
P.run(statement)
os.unlink(temp_file)
os.unlink(temp_file1)
def download(self, genes=None, fields=None, scope=None, species=None):
'''
download an up to date ontology file, parse the xml data into a
Python "ElementTree" and delete the ontology file.
'''
ontologyfile = P.get_temp_filename(".")
os.system("wget -O %s %s" % (ontologyfile, self.datasource))
tree = ET.parse(ontologyfile)
os.remove(ontologyfile)
self.dataset = tree
def bustools_sort(infile, outfile):
"""
Generate a sorted bus file
"""
tmp = P.get_temp_filename(".")
statement = """bustools sort -T %(tmp)s -t %(kallisto_threads)s -o %(outfile)s %(infile)s/output.bus"""
P.run(statement)
def capture_list(outfile):
'''Get the transcripts to capture list and transcripts to genes for cDNA'''
tmp_cdna = P.get_temp_filename(".")
statement = '''zcat < %(cdna_fasta)s cDNA.fa | awk '/^>/ {print $0}' | tr "_" " " | awk '{print $3}' > %(tmp_cdna)s &&
cat %(tmp_cdna)s | tr "." " " | awk '{print $1}' > %(outfile)s '''
P.run(statement)
os.unlink(tmp_cdna)
def intron_bed2fa(outfile):
'''This converts introns bed to introns fa'''
tmp_bed = P.get_temp_filename(".")
statement = '''zcat < %(intron_bed)s > %(tmp_bed)s &&
bedtools getfasta -name -fo %(outfile)s -fi %(genome_file)s -bed %(tmp_bed)s'''
P.run(statement)
os.unlink(tmp_bed)
def find_intron_fa_header(infile, outfile):
'''Fix the INTRONS FASTA header'''
tmp_cdna = P.get_temp_filename(".")
statement = '''zcat %(cdna)s > %(tmp_cdna)s && awk '{print ">"$1"."NR" gene_id:"$2" gene_name:"$3}' %(infile)s > geneset.dir/cDNA_fasta_header.txt &&
awk -v var=1 'FNR==NR{a[NR]=$0;next}{ if ($0~/^>/) {print a[var], var++} else {print $0}}' geneset.dir/cDNA_fasta_header.txt %(tmp_cdna)s >
%(outfile)s'''
P.run(statement)
os.unlink(tmp_cdna)
def tss_gene_parse(infile, outfile):
"""Filter a gtf using gene lists and then outut them as gtf"""
bedfile = PARAMS['geneexpression_tss']
tmpfile = P.get_temp_filename()
statement = """zcat %(bedfile)s | grep -f %(infile)s > %(tmpfile)s &&
cat %(tmpfile)s | awk '{$4 = "TSS"; print}' OFS='\\t' | gzip > %(outfile)s"""
P.run(statement)
def buildRefFlat(infile, outfile):
'''build flat geneset for Picard RnaSeqMetrics.
'''
tmpflat = P.get_temp_filename(".")
statement = '''
gtfToGenePred -genePredExt -geneNameAsName2 %(infile)s %(tmpflat)s &&
paste <(cut -f 12 %(tmpflat)s) <(cut -f 1-10 %(tmpflat)s)
> %(outfile)s
'''
P.run(statement, job_memory=PARAMS["job_memory"])
os.unlink(tmpflat)
def run(self, outfile, params):
if "--threads" in params.options or "-t " in params.options:
job_threads = int(re.search("(-t|--threads)\s*(\d+)",
params.options).groups()[1])
fastq = resolve_argument(params.fastq, ",").split(",")
if len(fastq) == 1:
fastq = '-U "{}"'.format(fastq)
else:
fastq = '-1 "{}" -2 "{}"'.format(*fastq)
tmpdir = P.get_temp_filename(clear=True)
if "index" in params._fields:
index = params.index
else:
index = params.reference_fasta
if params.set_readgroup or params.readgroup_id_regex is not None:
readgroup_string, readgroup_id, readgroup_sample = build_readgroup_string(
outfile, params)
# pipes.quote needs to shlex.quote in py3
readgroup_option = "--rg-id {}".format(readgroup_id)
# add additional level of quoting and remove "ID:{}"
readgroup_string = re.sub("@RG\tID:\S+\t", "", readgroup_string)
readgroup_string = " ".join(["--rg {}".format(x)
for x in readgroup_string.split("\t")])
else:
readgroup_option = ""
readgroup_string = ""
return P.run(
"mkdir {tmpdir}; "
"{self.path} "
"{readgroup_option} "
"{readgroup_string} "
"{params.options} "
"-x {index} "
"{fastq} "
"2> {outfile}.log "
"| samtools view -b /dev/stdin "
"2> {outfile}.view.log "
"| samtools sort -T {tmpdir} -O bam /dev/stdin "
"2> {outfile}.sort.log "
"> {outfile}; "
"samtools index {outfile}; "
"rm -rf {tmpdir}".format(**locals()),
**params._asdict())
def buildReferenceKallisto(infiles, outfile):
'''
Builds a reference transcriptome and decoy sequneces for alevin and kallisto
Parameters
----------
infile: str
path to the GTF file containing transcript and gene level annotations
genome_dir: str
:term: `PARAMS` the directory of the reference genome
genome: str
:term: `PARAMS` the filename of the reference genome (without .fa)
outfile: str
path to output file
'''
prim_trans1, prim_trans2 = infiles
genome_file1 = PARAMS['genome1']
if PARAMS['mixed_species']:
genome_file2 = PARAMS['genome2']
tmp1 = P.get_temp_filename('.')
tmp2 = P.get_temp_filename('.')
statement = '''
grep "^>" <(gunzip -c %(genome_file1)s %(genome_file2)s) | cut -d " " -f 1 > decoys.txt &&
sed -i.bak -e 's/>//g' decoys.txt &&
cat %(prim_trans1)s %(prim_trans2)s %(genome_file1)s %(genome_file1)s > %(outfile)s
'''
else:
statement = '''
grep "^>" <(gunzip -c %(genome_file1)s) | cut -d " " -f 1 > decoys.txt &&
sed -i.bak -e 's/>//g' decoys.txt &&
cat %(prim_trans1)s %(genome_file1)s > %(outfile)s
'''
P.run(statement)
if PARAMS['mixed_species']:
os.unlink(tmp1)
os.unlink(tmp2)
def busText(infile, outfile):
'''
Sort the bus file produced by kallisto and then convert it to a text file.
'''
tmp_bus = P.get_temp_filename(".")
statement = '''
sleep 10
bustools sort -o %(tmp_bus)s %(infile)s ;
bustools text -o %(outfile)s %(tmp_bus)s
'''
P.run(statement)
def loadManualAnnotations(infile, outfile):
tmp = P.get_temp_filename(".")
annotation = P.snip(infile, "_annotations.tsv")
with iotools.open_file(tmp, "w") as outf:
outf.write("%s\tgene_id\n" % annotation)
with iotools.open_file(infile, "r") as inf:
for line in inf:
outf.write("%s\t%s" % (annotation, line))
P.load(tmp, outfile, options="--add-index=gene_id")
os.unlink(tmp)
def aggregateAdaptors(infiles, outfile):
'''
Collate fasta files into a single contaminants file for
adapter removal.
'''
tempfile = P.get_temp_filename()
infiles = " ".join(infiles)
statement = """
cat %(infiles)s | fastx_reverse_complement > %(tempfile)s &&
cat %(tempfile)s %(infiles)s | fastx_collapser > %(outfile)s &&
rm -f %(tempfile)s
"""
P.run(statement)
def create_fragment_bed(infile, outfile):
"""Take the clusterInfo and create a bed file containing all of the fragments of tRNAs"""
cluster_info = infile.replace("_cluster.fa","_clusterInfo.fa")
tmp_file = P.get_temp_filename(".")
PY_SRC_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__),
"python"))
statement = """python %(PY_SRC_PATH)s/trna_fragment_bed.py -I %(cluster_info)s -S %(tmp_file)s &&
sort %(tmp_file)s | uniq > %(outfile)s"""
P.run(statement)
os.unlink(tmp_file)
def run_rmats_pre(infiles, outfile, track):
infile, gtffile = infiles
od = os.path.abspath("rmats.dir")
statement = '''rmats.py
--task prep
--tmp rmats.dir/%(track)s.dir
--gtf <(zcat %(gtffile)s)
--readLength %(rmats_readLength)s
-t %(rmats_paired)s
--od rmats.dir
--b1 <(echo %(infile)s)
&> rmats.dir/%(track)s.prep.log
'''
if infile.endswith(".remote"):
token = glob("gdc-user-token*")
tmpfilename = P.get_temp_filename()
if os.path.exists(tmpfilename):
os.unlink(tmpfilename)
if len(token) > 0:
token = token[0]
else:
token = None
s, infile = Sra.process_remote_BAM(infile,
token,
tmpfilename,
filter_bed=PARAMS["contigs_bed"])
s = re.sub(";\n", " &&\n", s)
infile = ",".join(infile)
statement = " && ".join([
"mkdir -p %(tmpfilename)s", s, statement, "rm -r %(tmpfilename)s"
])
P.run(statement,
job_condaenv=PARAMS["rmats_env"],
job_memory=PARAMS["rmats_prep_memory"])
rmats_counter = ""
for f_path in glob("rmats.dir/%(track)s.dir/*.rmats" % locals()):
shutil.copy(f_path,
P.snip(outfile, ".rmats") + rmats_counter + ".rmats")
if rmats_counter == "":
rmats_counter = 1
else:
rmats_counter += 1
def fix_intron_fasta(infiles, outfile):
'''fix all of the headers for the introns FASTA file so that they
contain the transcript ID, an identifier specifying that the transcript
is an “intronic” transcript, and a unique number to avoid duplicates.'''
introns_t2g, introns = infiles
tmp_fasta = P.get_temp_filename(".")
statement = '''awk '{print ">"$1"."NR"-I"" gene_id:"$2" gene_name:"$3}' %(introns_t2g)s > %(tmp_fasta)s &&
awk -v var=1 'FNR==NR{a[NR]=$0;next}{ if ($0~/^>/) {print a[var], var++} else {print $0}}' %(tmp_fasta)s %(introns)s > %(outfile)s '''
P.run(statement)
os.unlink(tmp_fasta)
