def test_table_context_manager_error_handling(self):
# In each case, the flush that happens at the close of the context handler should wait for
# the asynchronous requests and then raise the resulting error.
# Note that this test assumes that the error is a semantic error in the add_row data that
# is NOT caught by any local error checking.
# Use new_dxgtable
with self.assertRaises(DXAPIError):
with dxpy.new_dxgtable([dxpy.DXGTable.make_column_desc("a", "string"),
dxpy.DXGTable.make_column_desc("b", "int32")], mode='w') as table1:
table1.add_row(["", 68719476736]) # Not in int32 range
# Use open_dxgtable and close table
table2_id = dxpy.new_dxgtable([dxpy.DXGTable.make_column_desc("a", "string"),
dxpy.DXGTable.make_column_desc("b", "int32")], mode='w').get_id()
with self.assertRaises(DXAPIError):
with dxpy.open_dxgtable(table2_id) as table2:
table2.add_row(["", 68719476736]) # Not in int32 range
# TODO: why does the flush in this table's destructor fail? Nothing should be getting
# flushed then...
# Use open_dxgtable and leave table open
table3_id = dxpy.new_dxgtable([dxpy.DXGTable.make_column_desc("a", "string"),
dxpy.DXGTable.make_column_desc("b", "int32")])
with self.assertRaises(DXAPIError):
with dxpy.open_dxgtable(table3_id, mode='a') as table3:
table3.add_row(["", 68719476736]) # Not in int32 range
def test_table_context_manager(self):
# Writing a new_dxgtable with parts
with dxpy.new_dxgtable(
[dxpy.DXGTable.make_column_desc("a", "string"),
dxpy.DXGTable.make_column_desc("b", "int32")], mode='w') as self.dxgtable:
for i in range(64):
self.dxgtable.add_rows(data=[["row"+str(i), i]], part=i+1)
# Writing a new_dxgtable without parts
with dxpy.new_dxgtable([dxpy.DXGTable.make_column_desc("a", "string"),
dxpy.DXGTable.make_column_desc("b", "int32")], mode='w') as table2:
table2_id = table2.get_id()
for i in range(64):
table2.add_rows(data=[["row"+str(i), i]])
table2 = dxpy.open_dxgtable(table2_id)
self.assertEqual(table2.describe()["length"], 64)
table2.remove()
# Writing an open_dxgtable
table3_id = dxpy.new_dxgtable([dxpy.DXGTable.make_column_desc("a", "string"),
dxpy.DXGTable.make_column_desc("b", "int32")]).get_id()
with dxpy.open_dxgtable(table3_id, mode='a') as table3:
for i in range(64):
table3.add_rows(data=[["row"+str(i), i]])
with dxpy.open_dxgtable(table3_id, mode='w') as table3:
for i in range(64):
table3.add_rows(data=[["row"+str(i), i]])
table3 = dxpy.open_dxgtable(table3_id)
state = table3._get_state()
self.assertTrue(state in ['closing', 'closed'])
table3._wait_on_close()
self.assertEqual(table3.describe()["length"], 128)
table3.remove()
def process(input_gtable_id, start_row, end_row, output_gtable_id):
DX_APP_WIZARD_||_INPUT = dxpy.DXGTable(input_gtable_id)
# Using the context manager here is useful so that the flush()
# method is called once the context manager exits, and any rows
# added will be flushed to the platform. The mode is set to "a"
# for "append".
with dxpy.open_dxgtable(output_gtable_id, mode="a") as DX_APP_WIZARD_||_OUTPUT:
# The following loop iterates over each row from start_row to
# end_row (not including end_row). You can find documentation on
# other useful GTable methods (such as iterating over a genomic
# range query with iterate_query_rows) in the dxpy library here:
# http://autodoc.dnanexus.com/bindings/python/current/dxpy_dxgtable.html
for row in DX_APP_WIZARD_||_INPUT.iterate_rows(start_row, end_row):
# Fill in code here to perform whatever computation is
# necessary to process the row and compute the new row.
#
# *row* is an array where the first element is the row ID,
# and the rest of the elements appear in the same order as
# the GTable's column specification. You can retrieve the
# column specifications or names by using
# DX_APP_WIZARD_||_INPUT.get_columns() or DX_APP_WIZARD_||_INPUT.get_col_names().
new_row = []
# The following line queues up the array new_row as a row
# of data that should be added to the output GTable.
# Queued rows will be flushed to the platform periodically.
DX_APP_WIZARD_||_OUTPUT.add_row(new_row)
def write_reads_to_fasta(reads_id, filename, seq_col='sequence', start_row=0, end_row=None):
row_id = start_row
with open(filename, "w") as fh:
for row in dxpy.open_dxgtable(reads_id).iterate_rows(columns=[seq_col], start=start_row, end=end_row):
for line in '>%d' % row_id, row[0]:
print >>fh, line
row_id += 1
def reduceGatk(**job_inputs):
output = {}
t = dxpy.open_dxgtable(job_inputs['tableId'])
print "Closing Table"
t.close()
output['variants'] = dxpy.dxlink(t.get_id())
return output
def postprocess(process_outputs):
# Change the following to process whatever input this stage
# receives. You may also want to copy and paste the logic to download
# and upload files here as well if this stage receives file input
# and/or makes file output.
for output in process_outputs:
table = dxpy.open_dxgtable(output)
table.close()
break
return { "answer": None }
def main(**kwargs):
if len(kwargs) == 0:
kwargs = vars(parser.parse_args(sys.argv[1:]))
# Attempt to resolve variants gtable name
try:
project, folderpath, entity_result = resolve_existing_path(kwargs['path'], expected='entity')
except BaseException as details:
parser.exit(1, fill(unicode(details)) + '\n')
if entity_result is None:
parser.exit(1, fill('Could not resolve ' + kwargs['path'] + ' to a data object') + '\n')
filename = kwargs['output']
if filename is None:
filename = entity_result['describe']['name'].replace('/', '%2F') + ".vcf"
if kwargs['output'] == '-':
outputFile = sys.stdout
else:
outputFile = open(filename, 'w')
exportRef = kwargs['export_ref_calls']
exportNoCall = kwargs['export_no_calls']
variantsTable = dxpy.open_dxgtable(entity_result['id'])
try:
originalContigSet = variantsTable.get_details()['original_contigset']
except:
raise dxpy.AppError("The original reference genome must be attached as a detail")
contigDetails = dxpy.DXRecord(originalContigSet).get_details()
if kwargs['reference'] is not None:
refFileName = kwargs['reference']
if not os.path.isfile(refFileName):
raise dxpy.AppError("The reference expected by the variants to vcf script was not a valid file")
else:
refFileName = tempfile.NamedTemporaryFile(prefix='reference_', suffix='.txt', delete=False).name
dxpy.download_dxfile(contigDetails['flat_sequence_file']['$dnanexus_link'], refFileName)
if kwargs['write_header']:
infos = variantsTable.get_details().get('infos')
formats = variantsTable.get_details().get('formats')
alts = variantsTable.get_details().get('alts')
filters = variantsTable.get_details().get('filters')
samples = variantsTable.get_details().get('samples')
outputFile.write("##fileformat=VCFv4.1\n")
if infos is not None:
for k, v in collections.OrderedDict(sorted(infos.iteritems())).iteritems():
outputFile.write("##INFO=<ID="+k+",Number="+v['number']+",Type="+v['type']+",Description=\""+v['description']+"\">\n")
if len(samples) > 0:
outputFile.write("##FORMAT=<ID=GT,Number=1,Type=String,Description=\"Genotype\">\n")
outputFile.write("##FORMAT=<ID=AD,Number=.,Type=Integer,Description=\"Allelic depths for the ref and alt alleles in the order listed\">\n")
outputFile.write("##FORMAT=<ID=DP,Number=1,Type=String,Description=\"Approximate read depth (reads with MQ=255 or with bad mates are filtered)\">\n")
if formats is not None:
for k, v in collections.OrderedDict(sorted(formats.iteritems())).iteritems():
outputFile.write("##FORMAT=<ID="+k+",Number="+v['number']+",Type="+v['type']+",Description=\""+v['description']+"\">\n")
if alts is not None:
for k, v in collections.OrderedDict(sorted(alts.iteritems())).iteritems():
outputFile.write("##ALT=<ID="+k+",Description=\""+v['description']+"\">\n")
if filters is not None:
for k, v in collections.OrderedDict(sorted(filters.iteritems())).iteritems():
outputFile.write("##FILTER=<ID="+k+",Description=\""+v+"\">\n")
for i in range(len(contigDetails['contigs']['names'])):
outputFile.write("##contig=<ID="+contigDetails['contigs']['names'][i]+",length="+str(contigDetails['contigs']['sizes'][i])+">\n")
outputFile.write("#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO")
if len(samples) > 0:
outputFile.write("\tFORMAT")
for x in samples:
outputFile.write("\t"+x)
outputFile.write("\n")
chromosomeOffsets = {}
for i in range(len(contigDetails['contigs']['names'])):
chromosomeOffsets[contigDetails['contigs']['names'][i]] = contigDetails['contigs']['offsets'][i]
contigSequence = open(refFileName,'r').read()
col = {}
names = variantsTable.get_col_names()
for i in range(len(names)):
col[names[i]] = i+1
col = collections.OrderedDict(sorted(col.items()))
chromosomeList = contigDetails['contigs']['names']
if kwargs['chr'] is not None:
intersection = []
for x in chromosomeList:
if x in kwargs['chr']:
intersection.append(x)
chromosomeList = intersection[:]
for chromosome in chromosomeList:
lastPosition = -1
buff = []
query = variantsTable.genomic_range_query(chr=chromosome, lo=0, hi=sys.maxint)
for row in variantsTable.get_rows(query=query, limit=1)['data']:
startRow = row[0]
for row in variantsTable.iterate_rows(start=startRow):
if row[1] != chromosome:
break
if lastPosition < row[col["lo"]]:
writeBuffer(buff, col, outputFile, contigSequence, chromosomeOffsets, exportRef, exportNoCall)
buff = []
buff.append(row)
lastPosition = row[col["lo"]]
writeBuffer(buff, col, outputFile, contigSequence, chromosomeOffsets, exportRef, exportNoCall)
buff = []
def main(**job_inputs):
job_outputs = {}
mappingsTable = dxpy.open_dxgtable(job_inputs["mappings"]["$dnanexus_link"])
mappingsTableId = mappingsTable.get_id()
# This controls the degree of parallelism
chunks = int(mappingsTable.describe()["length"] / job_inputs["reads_per_job"]) + 1
try:
contigSetId = mappingsTable.get_details()["original_contigset"]["$dnanexus_link"]
originalContigSet = mappingsTable.get_details()["original_contigset"]
except:
raise Exception("The original reference genome must be attached as a detail")
# In the next major section of code, we construct a variants table. As regions of the genome are passed to each worker
# and variants are called on them, the workers will add rows to this table concurrently.
variants_schema = [
{"name": "chr", "type": "string"},
{"name": "lo", "type": "int32"},
{"name": "hi", "type": "int32"},
{"name": "ref", "type": "string"},
{"name": "alt", "type": "string"},
{"name": "qual", "type": "double"},
{"name": "ids", "type": "string"},
]
# The information in these tags is elevated into specific columns, so additional columns for these tags will not be created
elevatedTags = ["format_GT", "format_DP", "format_AD"]
# The info and format tags are extracted from the header printed by samtools
# If additional code will add a tag to the output of the program, modify this header to include the tag.
# TODO: Allow the table to be created by the first job that finishes to avoid this step.
headerInfo = extractHeader("/tmp/header.txt", elevatedTags)
description = {}
samples = []
indices = [dxpy.DXGTable.genomic_range_index("chr", "lo", "hi", "gri")]
##The following section creates the sample-specific table columns
for k, v in headerInfo["tags"]["info"].iteritems():
variants_schema.append({"name": "info_" + k, "type": translateTagTypeToColumnType(v)})
description[k] = {"name": k, "description": v["description"], "type": v["type"], "number": v["number"]}
# For each sample, add the sample-specific columns to the schema, at present only one sample is supported
numSamples = 1
for i in range(numSamples):
variants_schema.extend(
[
{"name": "genotype_" + str(i), "type": "string"},
{"name": "phasing_" + str(i), "type": "string"},
{"name": "type_" + str(i), "type": "string"},
{"name": "variation_qual_" + str(i), "type": "double"},
{"name": "genotype_qual_" + str(i), "type": "double"},
{"name": "coverage_" + str(i), "type": "string"},
{"name": "total_coverage_" + str(i), "type": "int32"},
]
)
indices.append(dxpy.DXGTable.lexicographic_index([["type_" + str(i), "ASC"]], "type_" + str(i)))
samples.append("Sample_0")
for k, v in headerInfo["tags"]["format"].iteritems():
if "format_" + k not in elevatedTags:
variants_schema.append({"name": "format_" + k + "_" + str(i), "type": translateTagTypeToColumnType(v)})
# TODO: Add lexicographic indices when secondary indices are supported
variants = dxpy.new_dxgtable(variants_schema, indices=[dxpy.DXGTable.genomic_range_index("chr", "lo", "hi", "gri")])
tableId = variants.get_id()
variants = dxpy.open_dxgtable(tableId)
variants.add_types(["Variants", "gri"])
details = {
"samples": samples,
"original_contigset": job_inputs["reference"],
"original_mappings": job_inputs["mappings"],
"formats": headerInfo["tags"]["format"],
"infos": headerInfo["tags"]["info"],
}
# if headerInfo.get('filters') != {}:
# details['filters'] = headerInfo['filters']
variants.set_details(details)
if "output_name" in job_inputs:
variants.rename(job_inputs["output_name"])
else:
variants.rename(mappingsTable.describe()["name"] + " variant calls by Samtools mpileup")
# Split the genome into evenly sized regions
genomeRegions = splitGenomeLengthLargePieces(originalContigSet, chunks)
# Generate the command line arguments needed to run samtools and bcftools
samOptions = makeSamtoolsParameters(**job_inputs)
bcfOptions = makeBcftoolsParameters(**job_inputs)
# The rest of the main function contains the map-reduce functionality. For each genome chunk, an input spec is created for a new child job.
# Which specifies
reduce_job_inputs = {}
for i in range(len(genomeRegions)):
if len(genomeRegions[i]) > 0:
map_job_inputs = {
"mappings_table_id": mappingsTableId,
"original_contig_set": contigSetId,
"interval": genomeRegions[i],
"tableId": tableId,
"compress_reference": job_inputs["compress_reference"],
"compress_no_call": job_inputs["compress_no_call"],
"infer_no_call": job_inputs["infer_no_call"],
"sam_options": samOptions,
"bcf_options": bcfOptions,
"part_number": i,
}
# Run a "map" job for each chunk, passing in the inputspec from above and looking for a function entry point given as "map" (@dxpy.entry_point('map'))
map_job = dxpy.new_dxjob(map_job_inputs, "map")
reduce_job_inputs["mapJob" + str(i) + "TableId"] = {"job": map_job.get_id(), "field": "ok"}
reduce_job_inputs["tableId"] = tableId
# Run a "reduce" job, which only begins once all of the map jobs singal they have completed by sending 'ok':True
# The reduce job closes the table. This step is explicitly needed because table closing must wait till the completion of the map jobs
# By giving the reduce job the map jobs as input, the reduce job will wait to start.
reduce_job = dxpy.new_dxjob(reduce_job_inputs, "reduce")
job_outputs = {"variants": {"job": reduce_job.get_id(), "field": "variants"}}
return job_outputs
def reducePileup(**job_inputs):
t = dxpy.open_dxgtable(job_inputs["tableId"])
print "Closing Table"
t.close()
job_outputs = {"variants": dxpy.dxlink(t.get_id())}
return job_outputs
def mapPileup(**job_inputs):
print "Downloading Reference Genome"
subprocess.check_call("dx-contigset-to-fasta %s ref.fa" % (job_inputs["original_contig_set"]), shell=True)
# The mappings-to-sam script takes a file with a list of regions in the form -L chrX:lo-hi, as produced by the genome splitting region.
# This generates the file
regionFile = open("regions.txt", "w")
regionFile.write(job_inputs["interval"])
regionFile.close()
print "Indexing Dictionary"
subprocess.check_call("samtools faidx ref.fa", shell=True)
# The sam-to-mappings script in dx-toolkit, the region_index_offset option is specified to convert from 1-indexed to 0-indexed coordinates
print "Converting Table to SAM"
command = "dx-mappings-to-sam %s --output input.sam --region_index_offset -1 --region_file regions.txt" % (
job_inputs["mappings_table_id"]
)
print "Running: " + command
subprocess.check_call(command, shell=True)
if checkSamContainsRead("input.sam"):
print "Converting to BAM"
subprocess.check_call("samtools view -bS input.sam > input.bam", shell=True)
print "Indexing"
subprocess.check_call("samtools index input.bam", shell=True)
variants = dxpy.open_dxgtable(job_inputs["tableId"])
command = "samtools mpileup -uf ref.fa"
# Since samtools takes a bed file to specify the regions, this takes the interval from the -L chrX:lo-hi format and puts it into BED
bedFile = open("regions.bed", "w")
intervalMatch = re.findall("-L ([^:]*):(\d+)-(\d+)", job_inputs["interval"])
if len(intervalMatch) > 0:
for x in intervalMatch:
bedFile.write(x[0] + "\t" + str(x[1]) + "\t" + str(x[2]) + "\n")
bedFile.close()
bedFile = open("regions.bed", "r")
bedFile.close()
command += " -l regions.bed "
command += job_inputs["sam_options"]
command += " input.bam | bcftools view "
command += job_inputs["bcf_options"]
command += " - > output.vcf"
print "Pileup Command: " + command
subprocess.check_call(command, shell=True)
# Convert the vcf file into variants. The dx_vcfToVariants is a script provided as resources as it has not yet been incorporated into dx-toolkit
command = "dx_vcfToVariants2 --table_id %s --vcf_file output.vcf --region_file regions.txt" % (
job_inputs["tableId"]
)
if job_inputs["compress_reference"]:
command += " --compress_reference"
if job_inputs["infer_no_call"]:
command += " --infer_no_call"
if job_inputs["compress_no_call"]:
command += " --compress_no_call"
print "Import variants command: " + command
subprocess.check_call(command, shell=True)
# Return 'ok', a signal which the reduce job looks for in order to know when it can begin closing the table
job_outputs = {"ok": True}
return job_outputs
def main(**kwargs):
if len(kwargs) == 0:
kwargs = vars(parser.parse_args(sys.argv[1:]))
# Attempt to resolve variants gtable name
try:
project, folderpath, entity_result = resolve_existing_path(
kwargs['path'], expected='entity')
except ResolutionError as details:
parser.exit(1, fill(str(details)) + '\n')
if entity_result is None:
parser.exit(
1,
fill('Could not resolve ' + kwargs['path'] + ' to a data object') +
'\n')
filename = kwargs['output']
if filename is None:
filename = entity_result['describe']['name'].replace('/',
'%2F') + ".vcf"
if kwargs['output'] == '-':
outputFile = sys.stdout
else:
outputFile = open(filename, 'w')
exportRef = kwargs['export_ref_calls']
exportNoCall = kwargs['export_no_calls']
variantsTable = dxpy.open_dxgtable(entity_result['id'])
try:
originalContigSet = variantsTable.get_details()['original_contigset']
except:
raise dxpy.AppError(
"The original reference genome must be attached as a detail")
contigDetails = dxpy.DXRecord(originalContigSet).get_details()
if kwargs['reference'] is not None:
refFileName = kwargs['reference']
if not os.path.isfile(refFileName):
raise dxpy.AppError(
"The reference expected by the variants to vcf script was not a valid file"
)
else:
refFileName = tempfile.NamedTemporaryFile(prefix='reference_',
suffix='.txt',
delete=False).name
dxpy.download_dxfile(
contigDetails['flat_sequence_file']['$dnanexus_link'], refFileName)
if kwargs['write_header']:
infos = variantsTable.get_details().get('infos')
formats = variantsTable.get_details().get('formats')
alts = variantsTable.get_details().get('alts')
filters = variantsTable.get_details().get('filters')
samples = variantsTable.get_details().get('samples')
outputFile.write("##fileformat=VCFv4.1\n")
if infos is not None:
for k, v in collections.OrderedDict(sorted(
infos.iteritems())).iteritems():
outputFile.write("##INFO=<ID=" + k + ",Number=" + v['number'] +
",Type=" + v['type'] + ",Description=\"" +
v['description'] + "\">\n")
if len(samples) > 0:
outputFile.write(
"##FORMAT=<ID=GT,Number=1,Type=String,Description=\"Genotype\">\n"
)
outputFile.write(
"##FORMAT=<ID=AD,Number=.,Type=Integer,Description=\"Allelic depths for the ref and alt alleles in the order listed\">\n"
)
outputFile.write(
"##FORMAT=<ID=DP,Number=1,Type=String,Description=\"Approximate read depth (reads with MQ=255 or with bad mates are filtered)\">\n"
)
if formats is not None:
for k, v in collections.OrderedDict(sorted(
formats.iteritems())).iteritems():
outputFile.write("##FORMAT=<ID=" + k + ",Number=" +
v['number'] + ",Type=" + v['type'] +
",Description=\"" + v['description'] +
"\">\n")
if alts is not None:
for k, v in collections.OrderedDict(sorted(
alts.iteritems())).iteritems():
outputFile.write("##ALT=<ID=" + k + ",Description=\"" +
v['description'] + "\">\n")
if filters is not None:
for k, v in collections.OrderedDict(sorted(
filters.iteritems())).iteritems():
outputFile.write("##FILTER=<ID=" + k + ",Description=\"" + v +
"\">\n")
for i in range(len(contigDetails['contigs']['names'])):
outputFile.write("##contig=<ID=" +
contigDetails['contigs']['names'][i] +
",length=" +
str(contigDetails['contigs']['sizes'][i]) + ">\n")
outputFile.write("#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO")
if len(samples) > 0:
outputFile.write("\tFORMAT")
for x in samples:
outputFile.write("\t" + x)
outputFile.write("\n")
chromosomeOffsets = {}
for i in range(len(contigDetails['contigs']['names'])):
chromosomeOffsets[contigDetails['contigs']['names']
[i]] = contigDetails['contigs']['offsets'][i]
contigSequence = open(refFileName, 'r').read()
col = {}
names = variantsTable.get_col_names()
for i in range(len(names)):
col[names[i]] = i + 1
col = collections.OrderedDict(sorted(col.items()))
chromosomeList = contigDetails['contigs']['names']
if kwargs['chr'] is not None:
intersection = []
for x in chromosomeList:
if x in kwargs['chr']:
intersection.append(x)
chromosomeList = intersection[:]
for chromosome in chromosomeList:
buff = []
lastPosition = -1
query = variantsTable.genomic_range_query(chr=chromosome,
lo=0,
hi=sys.maxsize)
for row in variantsTable.get_rows(query=query, limit=1)['data']:
startRow = row[0]
for row in variantsTable.iterate_rows(start=startRow):
if row[1] != chromosome:
break
if lastPosition < row[col["lo"]]:
writeBuffer(buff, col, outputFile, contigSequence,
chromosomeOffsets, exportRef, exportNoCall)
buff = []
buff.append(row)
lastPosition = row[col["lo"]]
writeBuffer(buff, col, outputFile, contigSequence, chromosomeOffsets,
exportRef, exportNoCall)
buff = []
def main(**job_inputs):
os.environ['CLASSPATH'] = '/opt/jar/AddOrReplaceReadGroups.jar:/opt/jar/GenomeAnalysisTK.jar'
if job_inputs['output_mode'] == "EMIT_VARIANTS_ONLY":
job_inputs['infer_no_call'] = False
mappingsTable = dxpy.open_dxgtable(job_inputs['mappings'][0]['$dnanexus_link'])
mappingsTableId = mappingsTable.get_id()
#This controls the degree of parallelism in GATK
reads = 0
for x in job_inputs['mappings']:
reads += int(dxpy.DXGTable(x).describe()['length'])
chunks = int(reads/job_inputs['reads_per_job'])+1
command = buildCommand(job_inputs)
#callVariantsOnSample(job_inputs, mappingsTable, command)
try:
contigSetId = mappingsTable.get_details()['original_contigset']['$dnanexus_link']
originalContigSet = mappingsTable.get_details()['original_contigset']
except:
raise Exception("The original reference genome must be attached as a detail")
variants_schema = [
{"name": "chr", "type": "string"},
{"name": "lo", "type": "int32"},
{"name": "hi", "type": "int32"},
{"name": "ref", "type": "string"},
{"name": "alt", "type": "string"},
{"name": "qual", "type": "double"},
{"name": "ids", "type": "string"}
]
elevatedTags = ['format_GT', 'format_DP', 'format_AD']
headerInfo = extractHeader("/tmp/header.txt", elevatedTags)
description = {}
samples = []
indices = [dxpy.DXGTable.genomic_range_index("chr","lo","hi", 'gri')]
formats = {}
infos = {}
filters = {}
for k, v in headerInfo['tags']['info'].iteritems():
variants_schema.append({"name": "info_"+k, "type":translateTagTypeToColumnType(v)})
description[k] = {'name' : k, 'description' : v['description'], 'type' : v['type'], 'number' : v['number']}
samples = []
for i in range(len(job_inputs['mappings'])):
samples.append(dxpy.DXGTable(job_inputs['mappings'][i]).describe()['name'].replace(" ", ""))
numSamples = len(job_inputs['mappings'])
if job_inputs['call_multiple_samples'] == False:
numSamples = 1
samples = ["Sample_0"]
#For each sample, write the sample-specific columns
for i in range(numSamples):
variants_schema.extend([
{"name": "genotype_"+str(i), "type": "string"},
{"name": "phasing_"+str(i), "type": "string"},
{"name": "type_"+str(i), "type": "string"},
{"name": "variation_qual_"+str(i), "type": "double"},
{"name": "genotype_qual_"+str(i), "type": "double"},
{"name": "coverage_"+str(i), "type": "string"},
{"name": "total_coverage_"+str(i), "type": "int32"}
])
indices.append(dxpy.DXGTable.lexicographic_index([["type_"+str(i), "ASC"]], 'type_'+str(i)))
for k, v in headerInfo['tags']['format'].iteritems():
if "format_"+k not in elevatedTags:
variants_schema.append({"name": "format_"+k+"_"+str(i), "type":translateTagTypeToColumnType(v)})
variantsTable = dxpy.new_dxgtable(variants_schema, indices=[dxpy.DXGTable.genomic_range_index("chr", "lo", "hi", "gri")])
tableId = variantsTable.get_id()
variantsTable = dxpy.open_dxgtable(tableId)
variantsTable.add_types(["Variants", "gri"])
details = {'samples':samples, 'original_contigset':job_inputs['reference'], 'original_mappings':job_inputs['mappings'], 'formats':headerInfo['tags']['format'], 'infos':headerInfo['tags']['info']}
#if headerInfo.get('filters') != {}:
# details['filters'] = headerInfo['filters']
variantsTable.set_details(details)
if 'output_name' in job_inputs:
variantsTable.rename(job_inputs['output_name'])
elif (job_inputs['genotype_likelihood_model'] == "SNP"):
variantsTable.rename(mappingsTable.describe()['name'] + " SNP calls by GATK")
elif (job_inputs['genotype_likelihood_model'] == "INDEL"):
variantsTable.rename(mappingsTable.describe()['name'] + " indel calls by GATK")
elif (job_inputs['genotype_likelihood_model'] == "BOTH"):
variantsTable.rename(mappingsTable.describe()['name'] + " SNP and indel calls by GATK")
else:
variantsTable.rename(mappingsTable.describe()['name'] + " variant calls by GATK")
reduceInput = {}
#commandList = splitGenomeLengthLargePieces(originalContigSet, job_inputs['intervals_to_process'], job_inputs['intervals_to_exclude'], job_inputs['minimum_chunk_size'], job_inputs['maximum_chunks'])
commandList = splitGenomeLengthLargePieces(originalContigSet, chunks)
for i in range(len(commandList)):
if len(commandList[i]) > 0:
mapInput = {
'mappings_tables': job_inputs['mappings'],
'original_contig_set': contigSetId,
'interval': commandList[i],
'tableId': tableId,
'command': buildCommand(job_inputs),
'compress_reference': job_inputs['compress_reference'],
'infer_no_call': job_inputs['infer_no_call'],
'compress_no_call': job_inputs['compress_no_call'],
'intervals_to_include': job_inputs.get('intervals_to_process'),
'intervals_to_exclude': job_inputs.get('intervals_to_exclude'),
'intervals_merging': job_inputs['intervals_merging'],
'part_number': i,
'samples': samples,
'call_multiple_samples': job_inputs['call_multiple_samples']
}
# Run a "map" job for each chunk
mapJobId = dxpy.new_dxjob(fn_input=mapInput, fn_name="mapGatk").get_id()
reduceInput["mapJob" + str(i) + "TableId"] = {'job': mapJobId, 'field': 'id'}
reduceInput['tableId'] = tableId
reduceJobId = dxpy.new_dxjob(fn_input=reduceInput, fn_name="reduceGatk").get_id()
output = {'variants': {'job': reduceJobId, 'field': 'variants'}}
return output
