def prepare_organism_indices():
c_elegans = Organism.get_object_for_name("CAENORHABDITIS_ELEGANS")
# This is a lie, but this image doesn't have the dependencies for TRANSCRIPTOME_INDEX
computational_result_short = ComputationalResult(processor=utils.find_processor('SALMON_QUANT'))
computational_result_short.save()
organism_index = OrganismIndex()
organism_index.index_type = "TRANSCRIPTOME_SHORT"
organism_index.organism = c_elegans
organism_index.result = computational_result_short
organism_index.absolute_directory_path = "/home/user/data_store/salmon_tests/TRANSCRIPTOME_INDEX/SHORT"
organism_index.save()
comp_file = ComputedFile()
# This path will not be used because we already have the files extracted.
comp_file.absolute_file_path = "/home/user/data_store/salmon_tests/TRANSCRIPTOME_INDEX/SHORT/celgans_short.tar.gz"
comp_file.result = computational_result_short
comp_file.size_in_bytes=1337
comp_file.sha1="ABC"
comp_file.save()
# This is a lie, but this image doesn't have the dependencies for TX_IMPORT
computational_result_long = ComputationalResult(processor=utils.find_processor('SALMON_QUANT'))
computational_result_long.save()
def _populate_index_object(job_context: Dict) -> Dict:
""" """
result = ComputationalResult()
result.commands.append(job_context["salmon_formatted_command"])
try:
processor_key = "TX_INDEX"
result.processor = utils.find_processor(processor_key)
except Exception as e:
return utils.handle_processor_exception(job_context, processor_key, e)
result.is_ccdl = True
result.time_start = job_context["time_start"]
result.time_end = job_context["time_end"]
result.save()
job_context['pipeline'].steps.append(result.id)
computed_file = ComputedFile()
computed_file.absolute_file_path = job_context["computed_archive"]
computed_file.filename = os.path.split(job_context["computed_archive"])[-1]
computed_file.calculate_sha1()
computed_file.calculate_size()
computed_file.result = result
computed_file.is_smashable = False
computed_file.is_qc = False
computed_file.save()
organism_object = Organism.get_object_for_name(job_context['organism_name'])
index_object = OrganismIndex()
index_object.organism = organism_object
index_object.source_version = job_context["assembly_version"]
index_object.assembly_name = job_context["assembly_name"]
index_object.salmon_version = job_context["salmon_version"]
index_object.index_type = "TRANSCRIPTOME_" + job_context['length'].upper()
# This is where the index will be extracted to.
index_object.absolute_directory_path = LOCAL_ROOT_DIR + "/TRANSCRIPTOME_INDEX/" \
+ organism_object.name + "/" + job_context['length']
index_object.result = result
if S3_TRANSCRIPTOME_INDEX_BUCKET_NAME:
logger.info("Uploading %s %s to s3", job_context['organism_name'], job_context['length'], processor_job=job_context["job_id"])
timestamp = str(timezone.now().timestamp()).split('.')[0]
s3_key = organism_object.name + '_' + index_object.index_type + "_" + timestamp + '.tar.gz'
sync_result = computed_file.sync_to_s3(S3_TRANSCRIPTOME_INDEX_BUCKET_NAME, s3_key)
if sync_result:
computed_file.delete_local_file()
else:
logger.warn("S3_TRANSCRIPTOME_INDEX_BUCKET_NAME not configured, therefore %s %s will not be uploaded.",
job_context['organism_name'],
job_context['length'],
processor_job=job_context["job_id"])
index_object.save()
# We uploaded the file ourselves since we wanted it to go to a
# different bucket than end_job would put it in, therefore empty
# this list so end_job doesn't try to upload it again.
job_context['computed_files'] = []
job_context['result'] = result
job_context['computed_file'] = computed_file
job_context['index'] = index_object
# If there's not a long and a short index for this organism yet,
# don't delete the input.
# XXX: This will break once we introduce additional versions of these.
short_indices = OrganismIndex.objects.filter(organism=organism_object,
index_type="TRANSCRIPTOME_SHORT",
source_version=job_context["assembly_version"])
long_indices = OrganismIndex.objects.filter(organism=organism_object,
index_type="TRANSCRIPTOME_LONG",
source_version=job_context["assembly_version"])
if short_indices.count() < 1 or long_indices.count() < 1:
# utils.end_job deletes these, so remove them so it doesn't.
job_context["original_files"] = []
return job_context
def setUp(self):
# Saving this for if we have protected endpoints
# self.superuser = User.objects.create_superuser('john', '*****@*****.**', 'johnpassword')
# self.client.login(username='******', password='******')
# self.user = User.objects.create(username="******")
experiment = Experiment()
experiment.accession_code = "GSE000"
experiment.alternate_accession_code = "E-GEOD-000"
experiment.title = "NONONONO"
experiment.description = "Boooooourns. Wasabi."
experiment.technology = "RNA-SEQ"
experiment.save()
experiment = Experiment()
experiment.accession_code = "GSE123"
experiment.title = "Hey Ho Let's Go"
experiment.description = (
"This is a very exciting test experiment. Faygo soda. Blah blah blah."
)
experiment.technology = "MICROARRAY"
experiment.save()
self.experiment = experiment
experiment_annotation = ExperimentAnnotation()
experiment_annotation.data = {"hello": "world", "123": 456}
experiment_annotation.experiment = experiment
experiment_annotation.save()
# Create 26 test organisms numbered 0-25 for pagination test, so there should be 29 organisms total (with the 3 others below)
for i in range(26):
Organism(name=("TEST_ORGANISM_{}".format(i)),
taxonomy_id=(1234 + i)).save()
ailuropoda = Organism(name="AILUROPODA_MELANOLEUCA",
taxonomy_id=9646,
is_scientific_name=True)
ailuropoda.save()
self.homo_sapiens = Organism(name="HOMO_SAPIENS",
taxonomy_id=9606,
is_scientific_name=True)
self.homo_sapiens.save()
self.danio_rerio = Organism(name="DANIO_RERIO",
taxonomy_id=1337,
is_scientific_name=True)
self.danio_rerio.save()
sample = Sample()
sample.title = "123"
sample.accession_code = "123"
sample.is_processed = True
sample.organism = ailuropoda
sample.save()
sample = Sample()
sample.title = "789"
sample.accession_code = "789"
sample.is_processed = True
sample.organism = ailuropoda
sample.save()
self.sample = sample
# add qn target for sample organism
result = ComputationalResult()
result.commands.append("create_qn_target.py")
result.is_ccdl = True
result.is_public = True
result.processor = None
result.save()
cra = ComputationalResultAnnotation()
cra.result = result
cra.data = {"organism_id": ailuropoda.id, "is_qn": True}
cra.save()
ailuropoda.qn_target = result
ailuropoda.save()
sample_annotation = SampleAnnotation()
sample_annotation.data = {"goodbye": "world", "789": 123}
sample_annotation.sample = sample
sample_annotation.save()
original_file = OriginalFile()
original_file.save()
original_file_sample_association = OriginalFileSampleAssociation()
original_file_sample_association.sample = sample
original_file_sample_association.original_file = original_file
original_file_sample_association.save()
downloader_job = DownloaderJob()
downloader_job.save()
download_assoc = DownloaderJobOriginalFileAssociation()
download_assoc.original_file = original_file
download_assoc.downloader_job = downloader_job
download_assoc.save()
processor_job = ProcessorJob()
processor_job.save()
processor_assoc = ProcessorJobOriginalFileAssociation()
processor_assoc.original_file = original_file
processor_assoc.processor_job = processor_job
processor_assoc.save()
experiment_sample_association = ExperimentSampleAssociation()
experiment_sample_association.sample = sample
experiment_sample_association.experiment = experiment
experiment_sample_association.save()
experiment.num_total_samples = 1
experiment.num_processed_samples = 1
experiment.save()
result = ComputationalResult()
result.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
result = ComputationalResult()
result.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
processor = Processor()
processor.name = "Salmon Quant"
processor.version = "v9.9.9"
processor.docker_image = "dr_salmon"
processor.environment = '{"some": "environment"}'
processor.save()
computational_result_short = ComputationalResult(processor=processor)
computational_result_short.save()
organism_index = OrganismIndex()
organism_index.index_type = "TRANSCRIPTOME_SHORT"
organism_index.organism = self.danio_rerio
organism_index.result = computational_result_short
organism_index.absolute_directory_path = (
"/home/user/data_store/salmon_tests/TRANSCRIPTOME_INDEX/SHORT")
organism_index.is_public = True
organism_index.s3_url = "not_blank"
organism_index.save()
return
def setup_experiment(new_version_accessions: List[str],
old_version_accessions: List[str]) -> Dict:
""" Create an experiment where some samples were processed with the newest version of salmon and
other with an older one.
"""
# Create the experiment
experiment_accession = "SRP095529"
data_dir = "/home/user/data_store/"
experiment_dir = data_dir + experiment_accession
experiment = Experiment.objects.create(accession_code=experiment_accession,
technology="RNA-SEQ")
zebrafish = Organism.get_object_for_name("DANIO_RERIO")
# Create the transcriptome processor and result:
transcriptome_processor = Processor()
transcriptome_processor.name = "Transcriptome"
transcriptome_processor.version = "salmon 0.9.1"
transcriptome_processor.docker_image = "dr_transcriptome"
transcriptome_processor.environment = '{"some": "environment"}'
transcriptome_processor.save()
computational_result_short = ComputationalResult(
processor=transcriptome_processor)
computational_result_short.save()
organism_index = OrganismIndex()
organism_index.index_type = "TRANSCRIPTOME_SHORT"
organism_index.organism = zebrafish
organism_index.result = computational_result_short
organism_index.absolute_directory_path = "/home/user/data_store/ZEBRAFISH_INDEX/SHORT"
organism_index.salmon_version = "salmon 0.9.1"
organism_index.save()
comp_file = ComputedFile()
# This path will not be used because we already have the files extracted.
comp_file.absolute_file_path = (
"/home/user/data_store/ZEBRAFISH_INDEX/SHORT/zebrafish_short.tar.gz")
comp_file.result = computational_result_short
comp_file.size_in_bytes = 1337
comp_file.sha1 = "ABC"
comp_file.s3_key = "key"
comp_file.s3_bucket = "bucket"
comp_file.save()
quant_processor = Processor()
quant_processor.name = "Salmon Quant"
quant_processor.version = "salmon 0.9.1"
quant_processor.docker_image = "dr_salmon"
quant_processor.environment = '{"some": "environment"}'
quant_processor.save()
for accession_code in old_version_accessions:
sample = Sample.objects.create(
accession_code=accession_code,
organism=zebrafish,
source_database="SRA",
technology="RNA-SEQ",
platform_accession_code="IlluminaHiSeq1000",
)
ExperimentSampleAssociation.objects.create(experiment=experiment,
sample=sample)
original_file = OriginalFile()
original_file.filename = accession_code + ".SRA"
original_file.source_filename = accession_code + ".SRA"
original_file.save()
OriginalFileSampleAssociation.objects.get_or_create(
original_file=original_file, sample=sample)
# Create and associate quant result and files.
quant_result = ComputationalResult()
quant_result.is_ccdl = True
quant_result.processor = quant_processor
quant_result.organism_index = organism_index # associate with OLD organism index
quant_result.save()
kv = ComputationalResultAnnotation()
kv.data = {"index_length": "short"}
kv.result = quant_result
kv.is_public = True
kv.save()
# In prod the filename pattern will involve the timestamp
# but here we're using the accession code so we can find
# the archive file for the current sample.
archive_filename = "result-" + accession_code + ".tar.gz"
archive_file = ComputedFile()
archive_file.filename = archive_filename
archive_file.absolute_file_path = os.path.join(experiment_dir,
archive_filename)
archive_file.is_public = False
archive_file.is_smashable = False
archive_file.is_qc = False
archive_file.result = quant_result
archive_file.size_in_bytes = 12345
archive_file.save()
quant_file = ComputedFile()
quant_file.filename = "quant.sf"
quant_file.absolute_file_path = (experiment_dir + "/quant_files/" +
accession_code + "_output/quant.sf")
quant_file.is_public = False
quant_file.is_smashable = False
quant_file.is_qc = False
quant_file.result = quant_result
quant_file.size_in_bytes = 12345
quant_file.s3_bucket = "bucket"
quant_file.s3_key = "key"
quant_file.save()
SampleResultAssociation.objects.get_or_create(sample=sample,
result=quant_result)
# Create another OrganismIndex with a newer version of
transcriptome_processor = Processor()
transcriptome_processor.name = "Transcriptome"
transcriptome_processor.version = "salmon 0.13.1"
transcriptome_processor.docker_image = "dr_transcriptome"
transcriptome_processor.environment = '{"some": "environment"}'
transcriptome_processor.save()
computational_result_short = ComputationalResult(
processor=transcriptome_processor)
computational_result_short.save()
organism_index = OrganismIndex()
organism_index.index_type = "TRANSCRIPTOME_SHORT"
organism_index.organism = zebrafish
organism_index.result = computational_result_short
organism_index.absolute_directory_path = "/home/user/data_store/ZEBRAFISH_INDEX/SHORT"
organism_index.salmon_version = "salmon 0.13.1" # DIFFERENT SALMON VERSION
organism_index.save()
comp_file = ComputedFile()
# This path will not be used because we already have the files extracted.
comp_file.absolute_file_path = (
"/home/user/data_store/ZEBRAFISH_INDEX/SHORT/zebrafish_short.tar.gz")
comp_file.result = computational_result_short
comp_file.size_in_bytes = 1337
comp_file.sha1 = "ABC"
comp_file.s3_key = "key"
comp_file.s3_bucket = "bucket"
comp_file.save()
for accession_code in new_version_accessions:
sample = Sample.objects.create(
accession_code=accession_code,
organism=zebrafish,
source_database="SRA",
technology="RNA-SEQ",
platform_accession_code="IlluminaHiSeq1000",
)
ExperimentSampleAssociation.objects.create(experiment=experiment,
sample=sample)
original_file = OriginalFile()
original_file.filename = accession_code + ".SRA"
original_file.source_filename = accession_code + ".SRA"
original_file.save()
OriginalFileSampleAssociation.objects.get_or_create(
original_file=original_file, sample=sample)
# Create and associate quant result and files.
quant_result = ComputationalResult()
quant_result.is_ccdl = True
quant_result.processor = quant_processor
quant_result.organism_index = organism_index # NEWER VERSION
quant_result.save()
kv = ComputationalResultAnnotation()
kv.data = {"index_length": "short"}
kv.result = quant_result
kv.is_public = True
kv.save()
# In prod the filename pattern will involve the timestamp
# but here we're using the accession code so we can find
# the archive file for the current sample.
archive_filename = "result-" + accession_code + ".tar.gz"
archive_file = ComputedFile()
archive_file.filename = archive_filename
archive_file.absolute_file_path = os.path.join(experiment_dir,
archive_filename)
archive_file.is_public = False
archive_file.is_smashable = False
archive_file.is_qc = False
archive_file.result = quant_result
archive_file.size_in_bytes = 12345
archive_file.save()
quant_file = ComputedFile()
quant_file.filename = "quant.sf"
quant_file.absolute_file_path = (experiment_dir + "/quant_files/" +
accession_code + "_output/quant.sf")
quant_file.is_public = False
quant_file.is_smashable = False
quant_file.is_qc = False
quant_file.result = quant_result
quant_file.size_in_bytes = 12345
quant_file.s3_bucket = "bucket"
quant_file.s3_key = "key"
quant_file.save()
SampleResultAssociation.objects.get_or_create(sample=sample,
result=quant_result)
return experiment
def prep_tximport_at_progress_point(complete_accessions: List[str],
incomplete_accessions: List[str]) -> Dict:
"""Create an experiment and associated objects that tximport needs to run on it.
Creates a sample for each accession contained in either input
list. The samples in complete_accessions will be simlulated as
already having salmon quant run on them. The samples in
incomplete_accessions won't.
"""
# Create the experiment
experiment_accession = "SRP095529"
data_dir = "/home/user/data_store/"
experiment_dir = data_dir + experiment_accession
experiment = Experiment.objects.create(accession_code=experiment_accession,
technology="RNA-SEQ")
zebrafish = Organism.get_object_for_name("DANIO_RERIO")
ExperimentOrganismAssociation.objects.get_or_create(experiment=experiment,
organism=zebrafish)
# Create the transcriptome processor and result:
transcriptome_processor = Processor()
transcriptome_processor.name = "Transcriptome"
transcriptome_processor.version = "salmon 0.13.1"
transcriptome_processor.docker_image = "dr_transcriptome"
transcriptome_processor.environment = '{"some": "environment"}'
transcriptome_processor.save()
computational_result_short = ComputationalResult(
processor=transcriptome_processor)
computational_result_short.save()
organism_index = OrganismIndex()
organism_index.index_type = "TRANSCRIPTOME_SHORT"
organism_index.organism = zebrafish
organism_index.result = computational_result_short
organism_index.absolute_directory_path = "/home/user/data_store/ZEBRAFISH_INDEX/SHORT"
organism_index.salmon_version = "salmon 0.13.1"
organism_index.save()
comp_file = ComputedFile()
# This path will not be used because we already have the files extracted.
comp_file.absolute_file_path = (
"/home/user/data_store/ZEBRAFISH_INDEX/SHORT/zebrafish_short.tar.gz")
comp_file.result = computational_result_short
comp_file.size_in_bytes = 1337
comp_file.sha1 = "ABC"
comp_file.s3_key = "key"
comp_file.s3_bucket = "bucket"
comp_file.save()
for accession_code in incomplete_accessions:
sample = Sample.objects.create(
accession_code=accession_code,
organism=zebrafish,
source_database="SRA",
technology="RNA-SEQ",
)
ExperimentSampleAssociation.objects.create(experiment=experiment,
sample=sample)
original_file = OriginalFile()
original_file.filename = accession_code + ".SRA"
original_file.source_filename = accession_code + ".SRA"
original_file.save()
OriginalFileSampleAssociation.objects.get_or_create(
original_file=original_file, sample=sample)
quant_processor = Processor()
quant_processor.name = "Salmon Quant"
quant_processor.version = "salmon 0.13.1"
quant_processor.docker_image = "dr_salmon"
quant_processor.environment = '{"some": "environment"}'
quant_processor.save()
tximport_processor = Processor()
tximport_processor.name = "Tximport"
tximport_processor.version = "salmon 0.13.1"
tximport_processor.docker_image = "dr_salmon"
tximport_processor.environment = '{"some": "environment"}'
tximport_processor.save()
# Create the already processed samples along with their
# ComputationalResults and ComputedFiles. They don't need
# original files for this test because we aren't going to run
# salmon quant on them.
for accession_code in complete_accessions:
sample = Sample.objects.create(
accession_code=accession_code,
organism=zebrafish,
source_database="SRA",
technology="RNA-SEQ",
)
ExperimentSampleAssociation.objects.create(experiment=experiment,
sample=sample)
original_file = OriginalFile()
original_file.filename = accession_code + ".SRA"
original_file.source_filename = accession_code + ".SRA"
original_file.save()
OriginalFileSampleAssociation.objects.get_or_create(
original_file=original_file, sample=sample)
# Create and associate quant result and files.
quant_result = ComputationalResult()
quant_result.is_ccdl = True
quant_result.processor = quant_processor
quant_result.organism_index = organism_index
quant_result.save()
kv = ComputationalResultAnnotation()
kv.data = {"index_length": "short"}
kv.result = quant_result
kv.is_public = True
kv.save()
# In prod the filename pattern will involve the timestamp
# but here we're using the accession code so we can find
# the archive file for the current sample.
archive_filename = "result-" + accession_code + ".tar.gz"
archive_file = ComputedFile()
archive_file.filename = archive_filename
archive_file.absolute_file_path = os.path.join(experiment_dir,
archive_filename)
archive_file.is_public = False
archive_file.is_smashable = False
archive_file.is_qc = False
archive_file.result = quant_result
archive_file.size_in_bytes = 12345
archive_file.save()
quant_file = ComputedFile()
quant_file.filename = "quant.sf"
quant_file.absolute_file_path = (experiment_dir + "/quant_files/" +
accession_code + "_output/quant.sf")
quant_file.is_public = False
quant_file.is_smashable = False
quant_file.is_qc = False
quant_file.result = quant_result
quant_file.size_in_bytes = 12345
quant_file.s3_bucket = "bucket"
quant_file.s3_key = "key"
quant_file.save()
sample.most_recent_quant_file = quant_file
sample.save()
SampleResultAssociation.objects.get_or_create(sample=sample,
result=quant_result)
def run_tximport_at_progress_point(complete_accessions: List[str], incomplete_accessions: List[str]) -> Dict:
"""Create an experiment and associated objects and run tximport on it.
Creates a sample for each accession contained in either input
list. The samples in complete_accessions will be simlulated as
already having salmon quant run on them. The samples in
incomplete_accessions won't.
"""
# Create the experiment
experiment_accession = 'SRP095529'
data_dir = '/home/user/data_store/salmon_tests/'
experiment_dir = data_dir + experiment_accession
experiment = Experiment.objects.create(accession_code=experiment_accession)
zebrafish = Organism.get_object_for_name("DANIO_RERIO")
# This is a lie, but this image doesn't have the dependencies for TRANSCRIPTOME_INDEX
computational_result_short = ComputationalResult(processor=utils.find_processor('SALMON_QUANT'))
computational_result_short.save()
organism_index = OrganismIndex()
organism_index.index_type = "TRANSCRIPTOME_SHORT"
organism_index.organism = zebrafish
organism_index.result = computational_result_short
organism_index.absolute_directory_path = "/home/user/data_store/salmon_tests/ZEBRAFISH_INDEX/SHORT"
organism_index.save()
comp_file = ComputedFile()
# This path will not be used because we already have the files extracted.
comp_file.absolute_file_path = "/home/user/data_store/salmon_tests/ZEBRAFISH_INDEX/SHORT/zebrafish_short.tar.gz"
comp_file.result = computational_result_short
comp_file.size_in_bytes=1337
comp_file.sha1="ABC"
comp_file.s3_key = "key"
comp_file.s3_bucket = "bucket"
comp_file.save()
for accession_code in incomplete_accessions:
last_sample = Sample.objects.create(
accession_code=accession_code,
organism=zebrafish,
source_database='SRA',
technology='RNA-SEQ'
)
ExperimentSampleAssociation.objects.create(experiment=experiment, sample=last_sample)
# Create tximport result and files
quant_processor = utils.find_processor("SALMON_QUANT")
tximport_processor = utils.find_processor("TXIMPORT")
# Create the already processed samples along with their
# ComputationalResults and ComputedFiles. They don't need
# original files for this test because we aren't going to run
# salmon quant on them.
for accession_code in complete_accessions:
sample = Sample.objects.create(
accession_code=accession_code,
organism=zebrafish,
source_database='SRA',
technology='RNA-SEQ'
)
ExperimentSampleAssociation.objects.create(experiment=experiment, sample=sample)
if accession_code == "SRR5125622":
current_sample = sample
# Create and associate quant result and files.
quant_result = ComputationalResult()
quant_result.is_ccdl = True
quant_result.processor = quant_processor
quant_result.save()
# In prod the filename pattern will involve the timestamp
# but here we're using the accession code so we can find
# the archive file for the current sample.
archive_filename = "result-" + accession_code + ".tar.gz"
archive_file = ComputedFile()
archive_file.filename = archive_filename
archive_file.absolute_file_path = os.path.join(experiment_dir, archive_filename)
archive_file.is_public = False
archive_file.is_smashable = False
archive_file.is_qc = False
archive_file.result = quant_result
archive_file.size_in_bytes = 12345
archive_file.save()
quant_file = ComputedFile()
quant_file.filename = "quant.sf"
quant_file.absolute_file_path = experiment_dir + "/quant_files/" + accession_code + "_output/quant.sf"
quant_file.is_public = False
quant_file.is_smashable = False
quant_file.is_qc = False
quant_file.result = quant_result
quant_file.size_in_bytes = 12345
quant_file.s3_bucket = "bucket"
quant_file.s3_key = "key"
quant_file.save()
SampleResultAssociation.objects.get_or_create(
sample=sample,
result=quant_result
)
# Processor jobs need at least one original file associated with
# them so they know what they're processing.
current_og = OriginalFile()
current_og.absolute_file_path = os.path.join(experiment_dir, 'SRR5125622.fastq.gz')
current_og.filename = "SRR5125622.fastq.gz"
current_og.save()
OriginalFileSampleAssociation.objects.create(original_file=current_og, sample=current_sample).save()
pj = ProcessorJob()
pj.pipeline_applied = "TXIMPORT"
pj.save()
assoc1 = ProcessorJobOriginalFileAssociation()
assoc1.original_file = current_og
assoc1.processor_job = pj
assoc1.save()
# Prep our job context
job_context = tximport._prepare_files({"job_dir_prefix": "TEST3",
"job_id": "TEST3",
"job": pj,
"index_directory": organism_index.absolute_directory_path,
"pipeline": Pipeline(name="Salmon"),
"computed_files": [],
"original_files": [current_og]})
# We don't have the raw file to run _determine_index_length so
# just pick one, it doesn't matter that much because we aren't
# checking the output data.
job_context["index_length"] = "short"
job_context = salmon._find_or_download_index(job_context)
job_context = salmon.get_tximport_inputs(job_context)
job_context = salmon.tximport(job_context)
return job_context