def downloadFiles(infiles, outfile):
infile = infiles
basefile = os.path.basename(infile)
filename = "temp_bams/%s" % basefile
baseoutfile = os.path.basename(outfile)
outdir = os.path.dirname(outfile)
if infile.endswith(".remote"):
for line in IOTools.open_file(infile):
repo, acc = line.strip().split("\t")[:2]
if repo == "SRA":
if not os.path.isfile(outfile + ".1.gz"):
statement = "; ".join(
[Sra.prefetch(acc),
Sra.extract(acc, outdir)])
P.run(statement)
else:
pass
elif repo == "GDC":
base = os.path.splitext(basefile)
outfile = "bam.dir/" + base[0] + ".bam"
token = glob.glob("gdc-user-token*")
if len(token) > 0:
token = token[0]
else:
token = None
s, infile = Sra.process_remote_BAM(
infile,
token,
filename,
filter_bed=os.path.join(
PARAMS["annotations_dir"],
PARAMS["annotations_interface_contigs_bed"]))
infile = " ".join(infile)
if not os.path.isfile(outfile):
statement = "; ".join([
"mkdir -p %(filename)s", s,
'''cp %(infile)s %(outfile)s;
rm -r %(filename)s'''
])
P.run(statement)
else:
pass
else:
raise ValueError("Unknown repository: %s" % repo)
else:
pass
def assembleWithStringTie(infiles, outfile):
infile, reference = infiles
job_threads = PARAMS["stringtie_threads"]
job_memory = PARAMS["stringtie_memory"]
statement = '''stringtie %(infile)s
-p %(stringtie_threads)s
-G <(zcat %(reference)s)
%(stringtie_options)s
2> %(outfile)s.log
| gzip > %(outfile)s '''
if infile.endswith(".remote"):
token = glob.glob("gdc-user-token*")
tmpfilename = P.getTempFilename()
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 = "; checkpoint ;".join(
["mkdir %(tmpfilename)s", s, statement, "rm -r %(tmpfilename)s"])
P.run()
def quantifyWithSalmon(infiles, outfile):
'''Quantify existing samples against genesets'''
job_threads = 2
job_memory = "16G"
infile, gtffile = infiles
basefile = os.path.basename(infile)
sample_name = basefile.split(os.extsep, 1)
sorted_bam = "sorted_bams/" + sample_name[0] + "_sorted.bam"
gtfbase = P.snip(os.path.basename(gtffile), ".gz")
salmonIndex = "salmon_index/" + gtfbase + ".salmon.index"
fastq1 = P.snip(outfile, "_agg-agg-agg") + ".1.fastq"
fastq2 = P.snip(outfile, "_agg-agg-agg") + ".2.fastq"
salmon_options = PARAMS["salmon_quantoptions"]
statement = '''
samtools sort -n %(infile)s -o %(sorted_bam)s;
samtools fastq
-1 %(fastq1)s
-2 %(fastq2)s
-0 /dev/null -s /dev/null -n -F 0x900
%(sorted_bam)s;
salmon quant -i %(salmonIndex)s
--libType IU
-1 %(fastq1)s
-2 %(fastq2)s
-o %(outfile)s
%(salmon_options)s;
mv %(outfile)s/quant.sf %(outfile)s.sf;
rm %(fastq1)s; rm %(fastq2)s; rm %(sorted_bam)s
'''
if infile.endswith(".remote"):
token = glob.glob("gdc-user-token*")
filename = "temp_bams/%s" % basefile
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,
filename,
filter_bed=os.path.join(
PARAMS["annotations_dir"],
PARAMS["annotations_interface_contigs_bed"]))
infile = " ".join(infile)
statement = "; ".join(
["mkdir %(filename)s", s, statement, "rm -r %(filename)s"])
P.run(statement)
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)
def quantifyWithSalmon(infiles, outfile):
'''Quantify existing samples against genesets'''
job_threads = 2
job_memory = "8G"
infile, gtffile = infiles
basefile = os.path.basename(infile)
gtfbase = P.snip(os.path.basename(gtffile), ".gz")
salmonIndex = "salmon_index/" + gtfbase + ".salmon.index"
fastq1 = P.snip(outfile, "_agg-agg-agg") + ".1.fastq"
fastq2 = P.snip(outfile, "_agg-agg-agg") + ".2.fastq"
salmon_options = PARAMS["salmon_quantoptions"]
statement = '''
samtools fastq
-1 %(fastq1)s
-2 %(fastq2)s
%(infile)s;
salmon quant -i %(salmonIndex)s
--libType A
-1 %(fastq1)s
-2 %(fastq2)s
-o %(outfile)s
--threads %(job_threads)s
%(salmon_options)s;
checkpoint;
mv %(outfile)s/quant.sf %(outfile)s.sf;
rm %(fastq1)s; rm %(fastq2)s
'''
if infile.endswith(".remote"):
token = glob.glob("gdc-user-token*")
filename = "temp_bams/%s" % basefile
tmpfilename = P.getTempFilename()
if len(token) > 0:
token = token[0]
else:
token = None
s, infile = Sra.process_remote_BAM(
infile,
token,
filename,
filter_bed=os.path.join(
PARAMS["annotations_dir"],
PARAMS["annotations_interface_contigs_bed"]))
infile = " ".join(infile)
statement = "; checkpoint; ".join(
["mkdir %(filename)s", s, statement, "rm -r %(filename)s"])
P.run()
def makeAdaptorFasta(infile, outfile, track, dbh, contaminants_file):
'''Generate a .fasta file of adaptor sequences that are
overrepresented in the reads from a sample.
Requires cutadapt >= 1.7.
Arguments
---------
infile : string
Input filename that has been QC'ed. The filename is used to
check if the input was a :term:`sra` file and guess the
number of tracks to check.
outfile : string
Output filename in :term:`fasta` format.
track : string
Track name, used to access FastQC results in database.
dbh : object
Database handle.
contaminants_file : string
Path of file containing contaminants used for screening by
Fastqc.
'''
tracks = [track]
if infile.endswith(".sra"):
# patch for SRA files, look at multiple tracks
f, fastq_format = Sra.peek(infile)
if len(f) == 2:
tracks = [track + "_fastq_1", track + "_fastq_2"]
found_contaminants = []
for t in tracks:
table = PipelineTracks.AutoSample(os.path.basename(t)).asTable()
query = '''SELECT Possible_Source, Sequence FROM
%s_fastqc_Overrepresented_sequences;''' % table
cc = dbh.cursor()
try:
found_contaminants.extend(cc.execute(query).fetchall())
except sqlite3.OperationalError, msg:
print msg
# empty table
continue
def runKalistoOnRemoteBAM(infiles, outfile):
'''running kalisto on .bam or .remote files'''
job_memory="6G"
infile, kallisto_index = infiles
outdir = os.path.dirname(outfile)
outfile = P.snip(outfile, ".gz")
statement = []
tempfastq1 = P.getTempFilename()
tempfastq2 = P.getTempFilename()
rm_files = [tempfastq1, tempfastq2]
if infile.endswith(".remote"):
tempbam = P.getTempFilename()
s, infiles = Sra.process_remote_BAM(infile, outdir=tempbam)
infile = " ".join(infiles)
statement.append(s)
rm_files.append(tempbam)
statement.append('''samtools fastq %(infile)s
-1 %(tempfastq1)s
-2 %(tempfastq2)s''')
statement.append('''kallisto quant -i %(kallisto_index)s
-o %(outdir)s
%(tempfastq1)s %(tempfastq2)s''')
rm_files = " ".join(rm_files)
statement.append('''rm -R %(rm_files)s''')
statement.append('''gzip %(outfile)s''')
statement = "; \n checkpoint;\n".join(statement)
P.run()
def downloadSequinsNeatData(outfile):
''' Download the neat Sequins data from NCBI'''
address_base = 'ftp://ftp-trace.ncbi.nlm.nih.gov/sra/sra-instant/reads/ByExp/sra/SRX/SRX189'
outfile2srr = {
'neat-A.fastq.1.gz': 'SRR3743147',
'neat-B.fastq.1.gz': 'SRR3743148'
}
srr2srx = {'SRR3743147': 'SRX1897294', 'SRR3743148': 'SRX1897295'}
outfile_base = os.path.basename(outfile)
srr = outfile2srr[outfile_base]
srx = srr2srx[srr]
outfile_name = P.snip(outfile_base, '.fastq.1.gz')
statement = '''
wget %(address_base)s/%(srx)s/%(srr)s/%(srr)s.sra
-O %(outfile_name)s.sra
'''
P.run()
outdir = os.path.dirname(outfile)
statement = Sra.extract(outfile_name + '.sra', outdir)
P.run()
statement = '''
mv %(outdir)s/%(outfile_name)s_1.fastq.gz
%(outdir)s/%(outfile_name)s.fastq.1.gz; checkpoint;
mv %(outdir)s/%(outfile_name)s_2.fastq.gz
%(outdir)s/%(outfile_name)s.fastq.2.gz'''
P.run()
os.unlink(outfile_name + '.sra')
def extractGSE65525(infile, outfile):
''' extract fastqs '''
statement = SRA.extract(infile, "GSE65525/fastqs.dir")
P.run()
def extractGGSE53638(infile, outfile):
''' extract the fastqs from the SRA '''
statement = SRA.extract(infile, "GSE53638/fastqs.dir")
P.run()
def makeAdaptorFasta(infile, outfile, track, dbh, contaminants_file):
'''Generate a .fasta file of adaptor sequences that are
overrepresented in the reads from a sample.
Requires cutadapt >= 1.7.
Arguments
---------
infile : string
Input filename that has been QC'ed. The filename is used to
check if the input was a :term:`sra` file and guess the
number of tracks to check.
outfile : string
Output filename in :term:`fasta` format.
track : string
Track name, used to access FastQC results in database.
dbh : object
Database handle.
contaminants_file : string
Path of file containing contaminants used for screening by
Fastqc.
'''
tracks = [track]
if infile.endswith(".sra"):
# patch for SRA files, look at multiple tracks
f, fastq_format, datatype = Sra.peek(infile)
if len(f) == 2:
tracks = [track + "_fastq_1", track + "_fastq_2"]
elif infile.endswith(".fastq.1.gz"):
tracks = [track + "_fastq_1", track + "_fastq_2"]
elif infile.endswith(".fastq.gz"):
tracks = [track]
found_contaminants = []
for t in tracks:
table = PipelineTracks.AutoSample(os.path.basename(t)).asTable()
# if sample name starts with a number, sql table will have
# prepended "_"
if re.match("^\d+.*", table):
table = "_" + table
query = '''SELECT Possible_Source, Sequence FROM
%s_fastqc_Overrepresented_sequences;''' % table
cc = dbh.cursor()
# if there is no contamination table for even a single sample
# it will prevent the whole pipeline progressing
try:
found_contaminants.extend(cc.execute(query).fetchall())
except sqlite3.OperationalError:
E.warn("No table found for {}".format(t))
if len(found_contaminants) == 0:
P.touch(outfile)
return
# read contaminants from existing file
with IOTools.openFile(contaminants_file, "r") as inf:
known_contaminants = [l.split() for l in inf
if not l.startswith("#") and l.strip()]
known_contaminants = {" ".join(x[:-1]): x[-1]
for x in known_contaminants}
# output the full sequence of the contaminant if found
# in the list of known contaminants, otherwise don't report!
matched_contaminants = set()
with IOTools.openFile(outfile, "w") as outf:
for found_source, found_seq in found_contaminants:
possible_source = found_source.split(" (")[0]
if possible_source in known_contaminants:
matched_contaminants.update((possible_source,))
else:
pass
if len(matched_contaminants) > 0:
for match in matched_contaminants:
outf.write(">%s\n%s\n" % (match.replace(" ,", ""),
known_contaminants[match]))
def makeAdaptorFasta(infile, outfile, track, dbh, contaminants_file):
'''Generate a .fasta file of adaptor sequences that are
overrepresented in the reads from a sample.
Requires cutadapt >= 1.7.
Arguments
---------
infile : string
Input filename that has been QC'ed. The filename is used to
check if the input was a :term:`sra` file and guess the
number of tracks to check.
outfile : string
Output filename in :term:`fasta` format.
track : string
Track name, used to access FastQC results in database.
dbh : object
Database handle.
contaminants_file : string
Path of file containing contaminants used for screening by
Fastqc.
'''
tracks = [track]
if infile.endswith(".sra"):
# patch for SRA files, look at multiple tracks
f, fastq_format, datatype = Sra.peek(infile)
if len(f) == 2:
tracks = [track + "_fastq_1", track + "_fastq_2"]
elif infile.endswith(".fastq.1.gz"):
tracks = [track + "_fastq_1", track + "_fastq_2"]
elif infile.endswith(".fastq.gz"):
tracks = [track]
found_contaminants = []
for t in tracks:
table = PipelineTracks.AutoSample(os.path.basename(t)).asTable()
# if sample name starts with a number, sql table will have
# prepended "_"
if re.match("^\d+.*", table):
table = "_" + table
query = '''SELECT Possible_Source, Sequence FROM
%s_fastqc_Overrepresented_sequences;''' % table
cc = dbh.cursor()
# if there is no contamination table for even a single sample
# it will prevent the whole pipeline progressing
try:
found_contaminants.extend(cc.execute(query).fetchall())
except sqlite3.OperationalError:
E.warn("No table found for {}".format(t))
if len(found_contaminants) == 0:
P.touch(outfile)
return
# read contaminants from existing file
with IOTools.openFile(contaminants_file, "r") as inf:
known_contaminants = [l.split() for l in inf
if not l.startswith("#") and l.strip()]
known_contaminants = {" ".join(x[:-1]): x[-1]
for x in known_contaminants}
# output the full sequence of the contaminant if found
# in the list of known contaminants, otherwise don't report!
matched_contaminants = set()
with IOTools.openFile(outfile, "w") as outf:
for found_source, found_seq in found_contaminants:
possible_source = found_source.split(" (")[0]
if possible_source in known_contaminants:
matched_contaminants.update((possible_source,))
else:
pass
if len(matched_contaminants) > 0:
for match in matched_contaminants:
outf.write(">%s\n%s\n" % (match.replace(" ,", ""),
known_contaminants[match]))
def extractGSE65525(infile, outfile):
''' extract fastqs '''
statement = SRA.extract(infile, "GSE65525/fastqs.dir")
P.run()
def extractGGSE53638(infile, outfile):
''' extract the fastqs from the SRA '''
statement = SRA.extract(infile, "GSE53638/fastqs.dir")
P.run()