def run_gnos_audit(args):
args = parser.parse_args()
syn = synapseclient.Synapse()
syn.login()
"""
r_map = synqueue.getValues(syn, "Normal_WGS_alignment_GNOS_repos", **config)
repos = {}
for i in r_map.values():
repos[i] = True
print repos.keys()
"""
server_list = ["https://gtrepo-osdc-tcga.annailabs.com"]
uuid_map = {}
uuid_map['broad'] = dict(
(a[1], a[0])
for a in synqueue.getValues(syn, "Broad_VCF_UUID", **config).items())
uuid_map['muse'] = dict(
(a[1], a[0])
for a in synqueue.getValues(syn, "Muse_VCF_UUID", **config).items())
uuid_map['broad_tar'] = dict(
(a[1], a[0])
for a in synqueue.getValues(syn, "Broad_TAR_UUID", **config).items())
analysis_re = re.compile(r'<analysis_id>(.*)</analysis_id>')
found = {}
for server in server_list:
handle = urlopen(server +
"/cghub/metadata/analysisId?study=tcga_pancancer_vcf")
for line in handle:
res = analysis_re.search(line)
if res:
gid = res.group(1)
for p in uuid_map:
if gid in uuid_map[p]:
pid = uuid_map[p][gid]
if pid not in found:
found[pid] = {}
found[pid][p] = [server, gid]
print "\n\n"
for p in found:
print p, "\t".join(
"%s (%s : %s)" % (a[0], a[1][0], a[1][1])
for a in sorted(found[p].items(), key=lambda x: x[0]))
def run_gnos_audit(args):
args = parser.parse_args()
syn = synapseclient.Synapse()
syn.login()
"""
r_map = synqueue.getValues(syn, "Normal_WGS_alignment_GNOS_repos", **config)
repos = {}
for i in r_map.values():
repos[i] = True
print repos.keys()
"""
server_list = ["https://gtrepo-osdc-tcga.annailabs.com"]
uuid_map = {}
uuid_map['broad'] = dict( (a[1], a[0]) for a in synqueue.getValues(syn, "Broad_VCF_UUID", **config).items() )
uuid_map['muse'] = dict( (a[1], a[0]) for a in synqueue.getValues(syn, "Muse_VCF_UUID", **config).items() )
uuid_map['broad_tar'] = dict( (a[1], a[0]) for a in synqueue.getValues(syn, "Broad_TAR_UUID", **config).items() )
analysis_re = re.compile(r'<analysis_id>(.*)</analysis_id>')
found = {}
for server in server_list:
handle = urlopen(server + "/cghub/metadata/analysisId?study=tcga_pancancer_vcf")
for line in handle:
res = analysis_re.search(line)
if res:
gid = res.group(1)
for p in uuid_map:
if gid in uuid_map[p]:
pid = uuid_map[p][gid]
if pid not in found:
found[pid] = {}
found[pid][p] = [server, gid]
print "\n\n"
for p in found:
print p, "\t".join( "%s (%s : %s)" % (a[0], a[1][0], a[1][1]) for a in sorted(found[p].items(), key=lambda x:x[0]) )
def run_uploadprep(args):
if not os.path.exists(args.workdir):
os.mkdir(args.workdir)
doc = from_url(args.out_base)
file_map = {'broad': {}, 'muse': {}, 'broad_tar': {}}
syn = synapseclient.Synapse()
syn.login()
wl_map = {}
job_map = {}
for ent in synqueue.listAssignments(syn, list_all=True, **config):
wl_map[ent['id']] = ent['meta']
uuid_map = {}
uuid_map['broad'] = synqueue.getValues(syn,
"Broad_VCF_UUID",
orSet=lambda x: str(uuid.uuid4()),
**config)
uuid_map['muse'] = synqueue.getValues(syn,
"Muse_VCF_UUID",
orSet=lambda x: str(uuid.uuid4()),
**config)
uuid_map['broad_tar'] = synqueue.getValues(
syn, "Broad_TAR_UUID", orSet=lambda x: str(uuid.uuid4()), **config)
#scan through all of the docs
for id, entry in doc.filter():
donor = None
#look for docs with donor tags
if 'tags' in entry and 'state' in entry and entry['state'] == 'ok':
for s in entry['tags']:
tmp = s.split(":")
if tmp[0] == 'donor':
donor = tmp[1]
if donor is not None and donor in wl_map:
if donor not in job_map:
job_map[donor] = {}
#scan out the job metrics for this job
if 'job' in entry and 'job_metrics' in entry['job']:
job_id = entry['job']['id']
tool_id = entry['job']['tool_id']
job_info = {tool_id: {}}
for met in entry['job']['job_metrics']:
job_info[tool_id][met['name']] = met['raw_value']
job_map[donor][job_id] = job_info
donor_tumor = wl_map[donor][
'Tumour_WGS_alignment_GNOS_analysis_IDs']
#look for the vcf output files
if entry.get('visible', False) and entry.get(
'extension', None) in ["vcf", "vcf_bgzip"]:
pipeline = None
method = None
call_type = None
variant_type = None
#fill out the info depending on which caller created the file
if entry['name'].split('.')[0] in ['MUSE_1']:
pipeline = "muse"
method = entry['name'].replace(".", "-")
variant_type = 'somatic'
call_type = 'snv_mnv'
elif entry['name'].split(".")[0] in [
'broad-dRanger', 'broad-dRanger_snowman',
'broad-snowman', 'broad-mutect'
]:
pipeline = "broad"
method = entry['name'].split(".")[0]
if 'somatic' in entry['name']:
variant_type = 'somatic'
elif 'germline' in entry['name']:
variant_type = 'germline'
else:
raise Exception("Unknown variant type")
if 'snv_mnv.vcf' in entry['name']:
call_type = 'snv_mnv'
elif 'sv.vcf' in entry['name']:
call_type = 'sv'
elif 'indel.vcf' in entry['name']:
call_type = 'indel'
else:
raise Exception("Unknown call type: %s" %
(entry['name']))
else:
raise Exception("Unknown pipeline %s" % (entry['name']))
datestr = datetime.datetime.now().strftime("%Y%m%d")
name = "%s.%s.%s.%s.%s" % (donor_tumor, method, datestr,
variant_type, call_type)
name = re.sub(r'.vcf$', '', name)
if entry['extension'] == 'vcf':
file_name = name + ".vcf"
elif entry['extension'] == 'vcf_bgzip':
file_name = name + ".vcf.gz"
target = Target(uuid=entry['uuid'])
if doc.size(target) > 0:
src_file = doc.get_filename(target)
dst_file = os.path.join(args.workdir, file_name)
shutil.copy(src_file, dst_file)
#if the files wasn't compressed already, go ahead and do that
if entry['extension'] == 'vcf':
subprocess.check_call("bgzip -c %s > %s.gz" %
(dst_file, dst_file),
shell=True)
dst_file = dst_file + ".gz"
#add file to output map
if donor not in file_map[pipeline]:
file_map[pipeline][donor] = []
input_file = os.path.basename(dst_file)
file_map[pipeline][donor].append(input_file)
else:
if entry['name'] == "broad.tar.gz":
target = Target(uuid=entry['uuid'])
src_file = doc.get_filename(target)
file_map['broad_tar'][donor] = [src_file]
timing_map = {}
for donor in job_map:
timing_map[donor] = {}
for job_id in job_map[donor]:
for tool_id in job_map[donor][job_id]:
if tool_id not in timing_map[donor]:
timing_map[donor][tool_id] = []
timing_map[donor][tool_id].append(
job_map[donor][job_id][tool_id])
result_counts = {}
for pipeline, donors in file_map.items():
for donor in donors:
result_counts[donor] = result_counts.get(donor, 0) + 1
#go through every pipeline
for pipeline, donors in file_map.items():
#for that pipeline go through every donor
for donor, files in donors.items():
#we're only outputing data for donors on the work list
if donor in wl_map and result_counts[donor] == 3:
#output the timing json
timing_json = os.path.abspath(
os.path.join(args.workdir,
"%s.%s.timing.json" % (pipeline, donor)))
with open(timing_json, "w") as handle:
handle.write(json.dumps(timing_map[donor]))
#output the uploader script
with open(
os.path.join(args.workdir,
"%s.%s.sh" % (pipeline, donor)),
"w") as handle:
input_file = os.path.basename(dst_file)
urls = [
"%scghub/metadata/analysisFull/%s" %
(wl_map[donor]['Normal_WGS_alignment_GNOS_repos'],
wl_map[donor]['Normal_WGS_alignment_GNOS_analysis_ID']
),
"%scghub/metadata/analysisFull/%s" %
(wl_map[donor]['Tumour_WGS_alignment_GNOS_repos'],
wl_map[donor]
['Tumour_WGS_alignment_GNOS_analysis_IDs'])
]
donor_tumor = wl_map[donor][
'Tumour_WGS_alignment_GNOS_analysis_IDs']
if pipeline in ['broad', 'muse']:
prep_cmd_str = ""
for vcf in files:
prep_cmd_str += "tabix -p vcf %s\n" % (vcf)
prep_cmd_str += "mv %s.tbi %s.idx\n" % (vcf, vcf)
prep_cmd_str += "md5sum %s | awk '{print$1}' > %s.md5\n" % (
vcf, vcf)
prep_cmd_str += "md5sum %s.idx | awk '{print$1}' > %s.idx.md5\n\n" % (
vcf, vcf)
related_uuids = []
for p in uuid_map:
if p != pipeline:
related_uuids.append(uuid_map[p][donor])
submit_cmd_str = "perl -I /opt/gt-download-upload-wrapper/gt-download-upload-wrapper-2.0.11/lib"
submit_cmd_str += " /opt/vcf-uploader/vcf-uploader-2.0.5/gnos_upload_vcf.pl"
submit_cmd_str += " --metadata-urls %s" % (
",".join(urls))
submit_cmd_str += " --vcfs %s " % (",".join(files))
submit_cmd_str += " --vcf-md5sum-files %s " % (
(",".join(("%s.md5" % i for i in files))))
submit_cmd_str += " --vcf-idxs %s" % ((",".join(
("%s.idx" % i for i in files))))
submit_cmd_str += " --vcf-idx-md5sum-files %s" % (
(",".join(("%s.idx.md5" % i for i in files))))
submit_cmd_str += " --outdir %s.%s.dir" % (pipeline,
donor_tumor)
submit_cmd_str += " --key %s " % (args.keyfile)
submit_cmd_str += " --upload-url %s" % (
args.upload_url)
submit_cmd_str += " --study-refname-override %s" % (
args.study)
submit_cmd_str += " --workflow-url '%s'" % args.pipeline_src
submit_cmd_str += " --workflow-src-url '%s'" % args.pipeline_src
submit_cmd_str += " --workflow-name '%s'" % args.pipeline_name
submit_cmd_str += " --workflow-version '%s'" % args.pipeline_version
submit_cmd_str += " --vm-instance-type '%s'" % args.vm_instance_type
submit_cmd_str += " --vm-instance-cores %s" % args.vm_instance_cores
submit_cmd_str += " --vm-instance-mem-gb %s" % args.vm_instance_mem_gb
submit_cmd_str += " --vm-location-code %s" % args.vm_location_code
submit_cmd_str += " --timing-metrics-json %s" % (
timing_json)
submit_cmd_str += " --workflow-file-subset %s" % (
pipeline)
submit_cmd_str += " --related-file-subset-uuids %s" % (
",".join(related_uuids))
submit_cmd_str += " --uuid %s" % (
uuid_map[pipeline][donor])
#submit_cmd_str += " --skip-upload"
if pipeline in ['broad_tar']:
prep_cmd_str = ""
new_files = []
for tar in files:
basename = donor_tumor + ".broad.intermediate"
prep_cmd_str = "%s/remap_broad_tar.py %s %s %s --rename %s %s" % (
os.path.dirname(os.path.abspath(__file__)),
tar, "./", basename, donor, donor_tumor)
new_files.append(basename + ".tar")
related_uuids = []
for p in uuid_map:
if p != pipeline:
related_uuids.append(uuid_map[p][donor])
submit_cmd_str = "perl -I /opt/gt-download-upload-wrapper/gt-download-upload-wrapper-2.0.11/lib"
submit_cmd_str += " /opt/vcf-uploader/vcf-uploader-2.0.5/gnos_upload_vcf.pl"
submit_cmd_str += " --metadata-urls %s" % (
",".join(urls))
submit_cmd_str += " --tarballs %s " % (
",".join(new_files))
submit_cmd_str += " --tarball-md5sum-files %s " % (
(",".join(("%s.md5" % i for i in new_files))))
submit_cmd_str += " --outdir %s.%s.dir" % (pipeline,
donor_tumor)
submit_cmd_str += " --key %s " % (args.keyfile)
submit_cmd_str += " --upload-url %s" % (
args.upload_url)
submit_cmd_str += " --study-refname-override %s" % (
args.study)
submit_cmd_str += " --workflow-url '%s'" % args.pipeline_src
submit_cmd_str += " --workflow-src-url '%s'" % args.pipeline_src
submit_cmd_str += " --workflow-name '%s'" % args.pipeline_name
submit_cmd_str += " --workflow-version '%s'" % args.pipeline_version
submit_cmd_str += " --vm-instance-type '%s'" % args.vm_instance_type
submit_cmd_str += " --vm-instance-cores %s" % args.vm_instance_cores
submit_cmd_str += " --vm-instance-mem-gb %s" % args.vm_instance_mem_gb
submit_cmd_str += " --workflow-file-subset %s" % (
pipeline)
submit_cmd_str += " --timing-metrics-json %s" % (
timing_json)
submit_cmd_str += " --related-file-subset-uuids %s" % (
",".join(related_uuids))
submit_cmd_str += " --uuid %s" % (
uuid_map[pipeline][donor])
#submit_cmd_str += " --skip-upload"
handle.write(
string.Template("""#!/bin/bash
set -ex
${PREP}
${SUBMIT}
echo $$? > $$0.submitted
#pushd ${SUBMIT_DIR}
#gtupload -v -c ${KEY} -u ./manifest.xml
#ECODE=$$?
#popd
#echo $$ECODE > $$0.uploaded
""").substitute(PREP=prep_cmd_str,
SUBMIT=submit_cmd_str,
SUBMIT_DIR=os.path.join(os.path.abspath(args.workdir), "vcf",
pipeline + "." + donor_tumor + ".dir",
uuid_map[pipeline][donor]),
KEY=args.keyfile))
def run_uploadprep(args):
if not os.path.exists(args.workdir):
os.mkdir(args.workdir)
doc = from_url(args.out_base)
file_map = {
'broad' : {},
'muse' : {},
'broad_tar' : {}
}
syn = synapseclient.Synapse()
syn.login()
wl_map = {}
job_map = {}
for ent in synqueue.listAssignments(syn, list_all=True, **config):
wl_map[ent['id']] = ent['meta']
uuid_map = {}
uuid_map['broad'] = synqueue.getValues(syn, "Broad_VCF_UUID", orSet=lambda x: str(uuid.uuid4()), **config)
uuid_map['muse'] = synqueue.getValues(syn, "Muse_VCF_UUID", orSet=lambda x: str(uuid.uuid4()), **config)
uuid_map['broad_tar'] = synqueue.getValues(syn, "Broad_TAR_UUID", orSet=lambda x: str(uuid.uuid4()), **config)
#scan through all of the docs
for id, entry in doc.filter():
donor = None
#look for docs with donor tags
if 'tags' in entry and 'state' in entry and entry['state'] == 'ok':
for s in entry['tags']:
tmp = s.split(":")
if tmp[0] == 'donor':
donor = tmp[1]
if donor is not None and donor in wl_map:
if donor not in job_map:
job_map[donor] = {}
#scan out the job metrics for this job
if 'job' in entry and 'job_metrics' in entry['job']:
job_id = entry['job']['id']
tool_id = entry['job']['tool_id']
job_info = { tool_id : {} }
for met in entry['job']['job_metrics']:
job_info[tool_id][met['name']] = met['raw_value']
job_map[donor][job_id] = job_info
donor_tumor = wl_map[donor]['Tumour_WGS_alignment_GNOS_analysis_IDs']
#look for the vcf output files
if entry.get('visible', False) and entry.get('extension', None) in ["vcf", "vcf_bgzip"]:
pipeline = None
method = None
call_type = None
variant_type = None
#fill out the info depending on which caller created the file
if entry['name'].split('.')[0] in ['MUSE_1']:
pipeline = "muse"
method = entry['name'].replace(".", "-")
variant_type = 'somatic'
call_type = 'snv_mnv'
elif entry['name'].split(".")[0] in ['broad-dRanger', 'broad-dRanger_snowman', 'broad-snowman', 'broad-mutect' ]:
pipeline = "broad"
method = entry['name'].split(".")[0]
if 'somatic' in entry['name']:
variant_type = 'somatic'
elif 'germline' in entry['name']:
variant_type = 'germline'
else:
raise Exception("Unknown variant type")
if 'snv_mnv.vcf' in entry['name']:
call_type = 'snv_mnv'
elif 'sv.vcf' in entry['name']:
call_type = 'sv'
elif 'indel.vcf' in entry['name']:
call_type = 'indel'
else:
raise Exception("Unknown call type: %s" % (entry['name']))
else:
raise Exception("Unknown pipeline %s" % (entry['name']))
datestr = datetime.datetime.now().strftime("%Y%m%d")
name = "%s.%s.%s.%s.%s" % (donor_tumor, method, datestr, variant_type, call_type )
name = re.sub(r'.vcf$', '', name)
if entry['extension'] == 'vcf':
file_name = name + ".vcf"
elif entry['extension'] == 'vcf_bgzip':
file_name = name + ".vcf.gz"
target = Target(uuid=entry['uuid'])
if doc.size(target) > 0:
src_file = doc.get_filename(target)
dst_file = os.path.join(args.workdir, file_name)
shutil.copy(src_file, dst_file)
#if the files wasn't compressed already, go ahead and do that
if entry['extension'] == 'vcf':
subprocess.check_call( "bgzip -c %s > %s.gz" % (dst_file, dst_file), shell=True )
dst_file = dst_file + ".gz"
#add file to output map
if donor not in file_map[pipeline]:
file_map[pipeline][donor] = []
input_file = os.path.basename(dst_file)
file_map[pipeline][donor].append(input_file)
else:
if entry['name'] == "broad.tar.gz":
target = Target(uuid=entry['uuid'])
src_file = doc.get_filename(target)
file_map['broad_tar'][donor] = [ src_file ]
timing_map = {}
for donor in job_map:
timing_map[donor] = {}
for job_id in job_map[donor]:
for tool_id in job_map[donor][job_id]:
if tool_id not in timing_map[donor]:
timing_map[donor][tool_id] = []
timing_map[donor][tool_id].append( job_map[donor][job_id][tool_id] )
result_counts = {}
for pipeline, donors in file_map.items():
for donor in donors:
result_counts[donor] = result_counts.get(donor, 0) + 1
#go through every pipeline
for pipeline, donors in file_map.items():
#for that pipeline go through every donor
for donor, files in donors.items():
#we're only outputing data for donors on the work list
if donor in wl_map and result_counts[donor] == 3:
#output the timing json
timing_json = os.path.abspath(os.path.join(args.workdir, "%s.%s.timing.json" %(pipeline, donor)))
with open( timing_json, "w" ) as handle:
handle.write(json.dumps( timing_map[donor] ) )
#output the uploader script
with open( os.path.join(args.workdir, "%s.%s.sh" %(pipeline, donor)), "w" ) as handle:
input_file = os.path.basename(dst_file)
urls = [
"%scghub/metadata/analysisFull/%s" % (wl_map[donor]['Normal_WGS_alignment_GNOS_repos'], wl_map[donor]['Normal_WGS_alignment_GNOS_analysis_ID']),
"%scghub/metadata/analysisFull/%s" % (wl_map[donor]['Tumour_WGS_alignment_GNOS_repos'], wl_map[donor]['Tumour_WGS_alignment_GNOS_analysis_IDs'])
]
donor_tumor = wl_map[donor]['Tumour_WGS_alignment_GNOS_analysis_IDs']
if pipeline in ['broad', 'muse']:
prep_cmd_str = ""
for vcf in files:
prep_cmd_str += "tabix -p vcf %s\n" % (vcf)
prep_cmd_str += "mv %s.tbi %s.idx\n" % (vcf,vcf)
prep_cmd_str += "md5sum %s | awk '{print$1}' > %s.md5\n" % (vcf, vcf)
prep_cmd_str += "md5sum %s.idx | awk '{print$1}' > %s.idx.md5\n\n" % (vcf, vcf)
related_uuids = []
for p in uuid_map:
if p != pipeline:
related_uuids.append(uuid_map[p][donor])
submit_cmd_str = "perl -I /opt/gt-download-upload-wrapper/gt-download-upload-wrapper-2.0.11/lib"
submit_cmd_str += " /opt/vcf-uploader/vcf-uploader-2.0.5/gnos_upload_vcf.pl"
submit_cmd_str += " --metadata-urls %s" % (",".join(urls))
submit_cmd_str += " --vcfs %s " % (",".join(files))
submit_cmd_str += " --vcf-md5sum-files %s " % ((",".join( ("%s.md5" % i for i in files) )))
submit_cmd_str += " --vcf-idxs %s" % ((",".join( ("%s.idx" % i for i in files) )))
submit_cmd_str += " --vcf-idx-md5sum-files %s" % ((",".join( ("%s.idx.md5" % i for i in files) )))
submit_cmd_str += " --outdir %s.%s.dir" % (pipeline, donor_tumor)
submit_cmd_str += " --key %s " % (args.keyfile)
submit_cmd_str += " --upload-url %s" % (args.upload_url)
submit_cmd_str += " --study-refname-override %s" % (args.study)
submit_cmd_str += " --workflow-url '%s'" % args.pipeline_src
submit_cmd_str += " --workflow-src-url '%s'" % args.pipeline_src
submit_cmd_str += " --workflow-name '%s'" % args.pipeline_name
submit_cmd_str += " --workflow-version '%s'" % args.pipeline_version
submit_cmd_str += " --vm-instance-type '%s'" % args.vm_instance_type
submit_cmd_str += " --vm-instance-cores %s" % args.vm_instance_cores
submit_cmd_str += " --vm-instance-mem-gb %s" % args.vm_instance_mem_gb
submit_cmd_str += " --vm-location-code %s" % args.vm_location_code
submit_cmd_str += " --timing-metrics-json %s" % (timing_json)
submit_cmd_str += " --workflow-file-subset %s" % (pipeline)
submit_cmd_str += " --related-file-subset-uuids %s" % (",".join(related_uuids))
submit_cmd_str += " --uuid %s" % (uuid_map[pipeline][donor])
#submit_cmd_str += " --skip-upload"
if pipeline in ['broad_tar']:
prep_cmd_str = ""
new_files = []
for tar in files:
basename = donor_tumor + ".broad.intermediate"
prep_cmd_str = "%s/remap_broad_tar.py %s %s %s --rename %s %s" % (
os.path.dirname(os.path.abspath(__file__)),
tar,
"./",
basename,
donor, donor_tumor
)
new_files.append( basename + ".tar" )
related_uuids = []
for p in uuid_map:
if p != pipeline:
related_uuids.append(uuid_map[p][donor])
submit_cmd_str = "perl -I /opt/gt-download-upload-wrapper/gt-download-upload-wrapper-2.0.11/lib"
submit_cmd_str += " /opt/vcf-uploader/vcf-uploader-2.0.5/gnos_upload_vcf.pl"
submit_cmd_str += " --metadata-urls %s" % (",".join(urls))
submit_cmd_str += " --tarballs %s " % (",".join(new_files))
submit_cmd_str += " --tarball-md5sum-files %s " % ((",".join( ("%s.md5" % i for i in new_files) )))
submit_cmd_str += " --outdir %s.%s.dir" % (pipeline, donor_tumor)
submit_cmd_str += " --key %s " % (args.keyfile)
submit_cmd_str += " --upload-url %s" % (args.upload_url)
submit_cmd_str += " --study-refname-override %s" % (args.study)
submit_cmd_str += " --workflow-url '%s'" % args.pipeline_src
submit_cmd_str += " --workflow-src-url '%s'" % args.pipeline_src
submit_cmd_str += " --workflow-name '%s'" % args.pipeline_name
submit_cmd_str += " --workflow-version '%s'" % args.pipeline_version
submit_cmd_str += " --vm-instance-type '%s'" % args.vm_instance_type
submit_cmd_str += " --vm-instance-cores %s" % args.vm_instance_cores
submit_cmd_str += " --vm-instance-mem-gb %s" % args.vm_instance_mem_gb
submit_cmd_str += " --workflow-file-subset %s" % (pipeline)
submit_cmd_str += " --timing-metrics-json %s" % (timing_json)
submit_cmd_str += " --related-file-subset-uuids %s" % (",".join(related_uuids))
submit_cmd_str += " --uuid %s" % (uuid_map[pipeline][donor])
#submit_cmd_str += " --skip-upload"
handle.write(string.Template("""#!/bin/bash
set -ex
${PREP}
${SUBMIT}
echo $$? > $$0.submitted
#pushd ${SUBMIT_DIR}
#gtupload -v -c ${KEY} -u ./manifest.xml
#ECODE=$$?
#popd
#echo $$ECODE > $$0.uploaded
""").substitute(PREP=prep_cmd_str, SUBMIT=submit_cmd_str,
SUBMIT_DIR=os.path.join(os.path.abspath(args.workdir), "vcf", pipeline + "." + donor_tumor + ".dir", uuid_map[pipeline][donor] ),
KEY=args.keyfile
) )