def setUp(self):
self.dbconfig = 'data/dbconfig.json'
self.platform_json = 'data/platform_db_data.json'
self.seqrun_json = 'data/seqrun_db_data.json'
self.pipeline_json = 'data/pipeline_data.json'
dbparam = read_dbconf_json(self.dbconfig)
base = BaseAdaptor(**dbparam)
self.engine = base.engine
self.dbname = dbparam['dbname']
Base.metadata.create_all(self.engine)
self.session_class = base.get_session_class()
base.start_session()
# load platform data
pl = PlatformAdaptor(**{'session': base.session})
pl.store_platform_data(data=read_json_data(self.platform_json))
# load seqrun data
sra = SeqrunAdaptor(**{'session': base.session})
sra.store_seqrun_and_attribute_data(
data=read_json_data(self.seqrun_json))
# load platform data
pla = PipelineAdaptor(**{'session': base.session})
pla.store_pipeline_data(data=read_json_data(self.pipeline_json))
pipeline_seed_data = [
{
'pipeline_name': 'demultiplexing_fastq',
'seed_id': '1',
'seed_table': 'seqrun'
},
]
pla.create_pipeline_seed(data=pipeline_seed_data)
base.close_session()
def setUp(self):
self.dbconfig = 'data/dbconfig.json'
dbparam=read_dbconf_json(self.dbconfig)
base = BaseAdaptor(**dbparam)
self.engine = base.engine
self.dbname=dbparam['dbname']
Base.metadata.drop_all(self.engine)
if os.path.exists(self.dbname):
os.remove(self.dbname)
Base.metadata.create_all(self.engine)
self.session_class=base.get_session_class()
base = BaseAdaptor(**{'session_class':self.session_class})
base.start_session()
platform_data=[{ "platform_igf_id" : "M001",
"model_name" : "MISEQ" ,
"vendor_name" : "ILLUMINA" ,
"software_name" : "RTA",
"software_version" : "RTA1.18.54"}] # platform data
flowcell_rule_data=[{"platform_igf_id":"M001",
"flowcell_type":"MISEQ",
"index_1":"NO_CHANGE",
"index_2":"NO_CHANGE"}] # flowcell rule data
pl=PlatformAdaptor(**{'session':base.session})
pl.store_platform_data(data=platform_data) # loading platform data
pl.store_flowcell_barcode_rule(data=flowcell_rule_data) # loading flowcell rules data
project_data=[{'project_igf_id':'ProjectA'}] # project data
pa=ProjectAdaptor(**{'session':base.session})
pa.store_project_and_attribute_data(data=project_data) # load project data
sample_data=[{'sample_igf_id':'SampleA',
'project_igf_id':'ProjectA'}] # sample data
sa=SampleAdaptor(**{'session':base.session})
sa.store_sample_and_attribute_data(data=sample_data) # store sample data
seqrun_data=[{'seqrun_igf_id':'SeqrunA',
'flowcell_id':'000000000-D0YLK',
'platform_igf_id':'M001',
'flowcell':'MISEQ'}] # seqrun data
sra=SeqrunAdaptor(**{'session':base.session})
sra.store_seqrun_and_attribute_data(data=seqrun_data) # load seqrun data
experiment_data=[{'experiment_igf_id':'ExperimentA',
'sample_igf_id':'SampleA',
'library_name':'SampleA',
'platform_name':'MISEQ',
'project_igf_id':'ProjectA'}] # experiment data
ea=ExperimentAdaptor(**{'session':base.session})
ea.store_project_and_attribute_data(data=experiment_data) # load experiment data
base.commit_session()
base.close_session()
def test_fetch_flowcell_barcode_rules_for_seqrun(self):
sra=SeqrunAdaptor(**{'session_class': self.session_class})
sra.start_session()
sra_data=sra.fetch_flowcell_barcode_rules_for_seqrun(seqrun_igf_id='170101_K00001_0001_AHABCDEFGH')
sra.close_session()
sra_data=sra_data.to_dict(orient='records')[0]
self.assertEqual(sra_data['index_2'],'REVCOMP')
def load_new_seqrun_data(data_file, dbconfig):
'''
A method for loading new data for seqrun table
'''
try:
formatted_data=read_json_data(data_file)
dbparam=read_dbconf_json(dbconfig)
sr=SeqrunAdaptor(**dbparam)
sr.start_session()
sr.store_seqrun_and_attribute_data(data=formatted_data)
sr.close_session()
except:
raise
def setUp(self):
self.dbconfig='data/dbconfig.json'
self.platform_json='data/platform_db_data.json'
self.seqrun_json='data/seqrun_db_data.json'
self.pipeline_json='data/pipeline_data.json'
self.flowcell_rules_json='data/flowcell_rules.json'
dbparam=read_dbconf_json(self.dbconfig)
base=BaseAdaptor(**dbparam)
self.engine=base.engine
self.dbname=dbparam['dbname']
Base.metadata.create_all(self.engine)
self.session_class=base.get_session_class()
base.start_session()
# load platform data
pl=PlatformAdaptor(**{'session':base.session})
pl.store_platform_data(data=read_json_data(self.platform_json))
pl.store_flowcell_barcode_rule(data=read_json_data(self.flowcell_rules_json))
# load seqrun data
sra=SeqrunAdaptor(**{'session':base.session})
sra.store_seqrun_and_attribute_data(data=read_json_data(self.seqrun_json))
base.close_session()
def test_reset_pipeline_seed_for_rerun(self):
base = BaseAdaptor(**{'session_class': self.session_class})
base.start_session()
sra = SeqrunAdaptor(**{'session': base.session})
seqrun = sra.fetch_seqrun_records_igf_id(
seqrun_igf_id='171003_M00001_0089_000000000-TEST')
pp = PipelineAdaptor(**{'session': base.session})
pipeline = pp.fetch_pipeline_records_pipeline_name(
'demultiplexing_fastq')
pipe_seed = pp.fetch_pipeline_seed(pipeline_id=pipeline.pipeline_id,
seed_id=seqrun.seqrun_id,
seed_table='seqrun')
self.assertEqual(pipe_seed.status, 'SEEDED')
pp.update_pipeline_seed(data=[{
'pipeline_id': pipeline.pipeline_id,
'seed_id': seqrun.seqrun_id,
'seed_table': 'seqrun',
'status': 'FINISHED',
}])
pipe_seed2 = pp.fetch_pipeline_seed(pipeline_id=pipeline.pipeline_id,
seed_id=seqrun.seqrun_id,
seed_table='seqrun')
self.assertEqual(pipe_seed2.status, 'FINISHED')
base.close_session()
with open(self.seqrun_input_list, 'w') as fp:
fp.write('171003_M00001_0089_000000000-TEST')
mps = Modify_pipeline_seed(igf_id_list=self.seqrun_input_list,
table_name='seqrun',
pipeline_name='demultiplexing_fastq',
dbconfig_file=self.dbconfig,
log_slack=False,
log_asana=False,
clean_up=True)
mps.reset_pipeline_seed_for_rerun(seeded_label='SEEDED')
base.start_session()
sra = SeqrunAdaptor(**{'session': base.session})
seqrun = sra.fetch_seqrun_records_igf_id(
seqrun_igf_id='171003_M00001_0089_000000000-TEST')
pp = PipelineAdaptor(**{'session': base.session})
pipeline = pp.fetch_pipeline_records_pipeline_name(
'demultiplexing_fastq')
pipe_seed = pp.fetch_pipeline_seed(pipeline_id=pipeline.pipeline_id,
seed_id=seqrun.seqrun_id,
seed_table='seqrun')
self.assertEqual(pipe_seed.status, 'SEEDED')
base.close_session()
def test_load_seqrun_files_to_db(self):
valid_seqrun_dir = find_new_seqrun_dir(path=self.path,
dbconfig=self.dbconfig)
new_seqrun_and_md5 = calculate_file_md5(seqrun_info=valid_seqrun_dir,
md5_out=self.md5_out_path,
seqrun_path=self.path)
load_seqrun_files_to_db(seqrun_info=valid_seqrun_dir,
seqrun_md5_info=new_seqrun_and_md5,
dbconfig=self.dbconfig)
# check in db
dbparam = None
with open(self.dbconfig, 'r') as json_data:
dbparam = json.load(json_data)
sra = SeqrunAdaptor(**dbparam)
sra.start_session()
sra_data = sra.fetch_seqrun_records_igf_id(seqrun_igf_id='seqrun1')
sra.close_session()
self.assertEqual(sra_data.flowcell_id, 'HXXXXXXXX')
seed_pipeline_table_for_new_seqrun(
pipeline_name='demultiplexing_fastq', dbconfig=self.dbconfig)
# check in db
dbparam = None
with open(self.dbconfig, 'r') as json_data:
dbparam = json.load(json_data)
base = BaseAdaptor(**dbparam)
base.start_session()
seeds=base.fetch_records(query=base.session.query(Seqrun.seqrun_igf_id).\
join(Pipeline_seed, Pipeline_seed.seed_id==Seqrun.seqrun_id).\
join(Pipeline, Pipeline.pipeline_id==Pipeline_seed.pipeline_id).\
filter(Pipeline.pipeline_name=='demultiplexing_fastq').\
filter(Pipeline_seed.seed_table=='seqrun'), output_mode='object')
base.close_session()
self.assertTrue('seqrun1' in [s.seqrun_igf_id for s in seeds])
def setUp(self):
self.path = 'data/seqrun_dir'
self.dbconfig = 'data/dbconfig.json'
self.md5_out_path = 'data/md5_dir'
self.pipeline_name = 'demultiplexing_fastq'
seqrun_json = 'data/seqrun_db_data.json'
platform_json = 'data/platform_db_data.json'
pipeline_json = 'data/pipeline_data.json'
os.mkdir(self.md5_out_path)
dbparam = None
with open(self.dbconfig, 'r') as json_data:
dbparam = json.load(json_data)
base = BaseAdaptor(**dbparam)
self.engine = base.engine
self.dbname = dbparam['dbname']
self.pipeline_name = ''
Base.metadata.create_all(self.engine)
base.start_session()
user_data = [
{
'name': 'user1',
'email_id': '*****@*****.**',
'username': '******'
},
]
ua = UserAdaptor(**{'session': base.session})
ua.store_user_data(data=user_data)
project_data = [{
'project_igf_id': 'project_1',
'project_name': 'test_22-8-2017_rna',
'description': 'Its project 1',
'project_deadline': 'Before August 2017',
'comments': 'Some samples are treated with drug X',
}]
pa = ProjectAdaptor(**{'session': base.session})
pa.store_project_and_attribute_data(data=project_data)
project_user_data = [{
'project_igf_id': 'project_1',
'email_id': '*****@*****.**',
'data_authority': True
}]
pa.assign_user_to_project(data=project_user_data)
sample_data = [
{
'sample_igf_id': 'IGF0001',
'project_igf_id': 'project_1',
},
{
'sample_igf_id': 'IGF0002',
'project_igf_id': 'project_1',
},
{
'sample_igf_id': 'IGF0003',
'project_igf_id': 'project_1',
},
]
sa = SampleAdaptor(**{'session': base.session})
sa.store_sample_and_attribute_data(data=sample_data)
base.commit_session()
with open(pipeline_json,
'r') as json_data: # store pipeline data to db
pipeline_data = json.load(json_data)
pa = PipelineAdaptor(**{'session': base.session})
pa.store_pipeline_data(data=pipeline_data)
with open(platform_json,
'r') as json_data: # store platform data to db
platform_data = json.load(json_data)
pl = PlatformAdaptor(**{'session': base.session})
pl.store_platform_data(data=platform_data)
with open(seqrun_json, 'r') as json_data: # store seqrun data to db
seqrun_data = json.load(json_data)
sra = SeqrunAdaptor(**{'session': base.session})
sra.store_seqrun_and_attribute_data(data=seqrun_data)
base.close_session()
def run(self):
try:
igf_session_class = self.param_required('igf_session_class')
seqrun_igf_id = self.param_required('seqrun_igf_id')
seqrun_local_dir = self.param_required('seqrun_local_dir')
base_work_dir = self.param_required('base_work_dir')
samplesheet_filename = self.param('samplesheet_filename')
index2_label = self.param('index2_label_in_db')
revcomp_label = self.param('revcomp_label')
singlecell_tag = self.param('singlecell_tag')
adapter_trim_check = self.param('adapter_trim_check')
adapter_section = self.param('adapter_section')
read1_adapter_label = self.param('read1_adapter_label')
read2_adapter_label = self.param('read2_adapter_label')
project_type = self.param('project_type')
job_name = self.job_name()
work_dir = \
os.path.join(\
base_work_dir,
seqrun_igf_id,
job_name) # get work directory name
if not os.path.exists(work_dir):
os.makedirs(work_dir, mode=0o770) # create work directory
output_file = \
os.path.join(\
work_dir,
samplesheet_filename) # get name of the output file
if os.path.exists(output_file):
raise IOError('seqrun: {0}, reformatted samplesheet {1} already present'.\
format(seqrun_igf_id,output_file))
samplesheet_file = \
os.path.join(\
seqrun_local_dir,
seqrun_igf_id,
samplesheet_filename)
if not os.path.exists(samplesheet_file):
raise IOError('seqrun: {0}, samplesheet file {1} not found'.\
format(seqrun_igf_id,samplesheet_file))
samplesheet_sc = \
SampleSheet(infile=samplesheet_file) # read samplesheet for single cell check
samplesheet_sc.\
filter_sample_data(\
condition_key='Description',
condition_value=singlecell_tag,
method='include') # get 10X samplesheet
if len(samplesheet_sc._data) > 0:
project_type = singlecell_tag # check if 10x samples are present in samplesheet
samplesheet = \
SampleSheet(infile=samplesheet_file) # read samplesheet
samplesheet.\
filter_sample_data(\
condition_key='Description',
condition_value=singlecell_tag,
method='exclude') # filter 10X samplesheet
if adapter_trim_check:
read1_val = \
samplesheet.\
check_sample_header(\
section=adapter_section,
condition_key=read1_adapter_label)
read2_val = \
samplesheet.\
check_sample_header(\
section=adapter_section,
condition_key=read2_adapter_label)
if read1_val == 0 or read2_val == 0:
message = \
'seqrun {0} samplesheet does not have adapter trim option, read1: {1}, read2:{2}'.\
format(seqrun_igf_id,read1_val,read2_val)
self.post_message_to_slack(message, reaction='pass')
self.comment_asana_task(
task_name=seqrun_igf_id, comment=message
) # send info about adapter trip to slack and asana
sa = SeqrunAdaptor(**{'session_class': igf_session_class})
sa.start_session()
rules_data = \
sa.fetch_flowcell_barcode_rules_for_seqrun(seqrun_igf_id) # convert index based on barcode rules
sa.close_session()
rules_data_set = rules_data.to_dict(
orient='records') # convert dataframe to dictionary
if len(rules_data_set) > 0:
rules_data = rules_data_set[0] # consider only the first rule
if rules_data[index2_label] == revcomp_label:
samplesheet.\
get_reverse_complement_index(index_field='index2') # reverse complement index2 based on the rules
else:
message = \
'no rules found for seqrun {0}, using original samplesheet'.\
format(seqrun_igf_id)
self.post_message_to_slack(message, reaction='pass')
self.comment_asana_task(task_name=seqrun_igf_id,
comment=message)
if len(samplesheet_sc._data) > 0: # merge 10x samplesheet
samplesheet._data.extend(samplesheet_sc._data)
message = \
'seqrun: {0}, merging 10X samples with reformatted samplesheet'.\
format(seqrun_igf_id)
self.post_message_to_slack(message, reaction='pass')
self.comment_asana_task(task_name=seqrun_igf_id,
comment=message)
samplesheet.print_sampleSheet(outfile=output_file)
self.param('dataflow_params', {
'samplesheet': output_file,
'project_type': project_type
})
message = \
'seqrun: {0}, reformatted samplesheet:{1}'.\
format(\
seqrun_igf_id,
output_file)
self.post_message_to_slack(message, reaction='pass')
self.comment_asana_task(task_name=seqrun_igf_id, comment=message)
except Exception as e:
message = \
'seqrun: {2}, Error in {0}: {1}'.\
format(\
self.__class__.__name__,
e,
seqrun_igf_id)
self.warning(message)
self.post_message_to_slack(
message, reaction='fail') # post msg to slack for failed jobs
raise
def setUp(self):
self.dbconfig = 'data/dbconfig.json'
dbparam = read_dbconf_json(self.dbconfig)
base = BaseAdaptor(**dbparam)
self.engine = base.engine
self.dbname = dbparam['dbname']
Base.metadata.create_all(self.engine)
self.session_class = base.get_session_class()
base.start_session()
platform_data = [{
"platform_igf_id": "M03291",
"model_name": "MISEQ",
"vendor_name": "ILLUMINA",
"software_name": "RTA",
"software_version": "RTA1.18.54"
}, {
"platform_igf_id": "NB501820",
"model_name": "NEXTSEQ",
"vendor_name": "ILLUMINA",
"software_name": "RTA",
"software_version": "RTA2"
}, {
"platform_igf_id": "K00345",
"model_name": "HISEQ4000",
"vendor_name": "ILLUMINA",
"software_name": "RTA",
"software_version": "RTA2"
}]
flowcell_rule_data = [{
"platform_igf_id": "K00345",
"flowcell_type": "HiSeq 3000/4000 SR",
"index_1": "NO_CHANGE",
"index_2": "NO_CHANGE"
}, {
"platform_igf_id": "K00345",
"flowcell_type": "HiSeq 3000/4000 PE",
"index_1": "NO_CHANGE",
"index_2": "REVCOMP"
}, {
"platform_igf_id": "NB501820",
"flowcell_type": "NEXTSEQ",
"index_1": "NO_CHANGE",
"index_2": "REVCOMP"
}, {
"platform_igf_id": "M03291",
"flowcell_type": "MISEQ",
"index_1": "NO_CHANGE",
"index_2": "NO_CHANGE"
}]
pl = PlatformAdaptor(**{'session': base.session})
pl.store_platform_data(data=platform_data)
pl.store_flowcell_barcode_rule(data=flowcell_rule_data)
seqrun_data = [{
'seqrun_igf_id': '180416_M03291_0139_000000000-BRN47',
'flowcell_id': '000000000-BRN47',
'platform_igf_id': 'M03291',
'flowcell': 'MISEQ',
}, {
'seqrun_igf_id': '180416_NB03291_013_000000001-BRN47',
'flowcell_id': '000000001-BRN47',
'platform_igf_id': 'NB501820',
'flowcell': 'NEXTSEQ',
}]
sra = SeqrunAdaptor(**{'session': base.session})
sra.store_seqrun_and_attribute_data(data=seqrun_data)
project_data = [{'project_igf_id': 'projectA'}]
pa = ProjectAdaptor(**{'session': base.session})
pa.store_project_and_attribute_data(data=project_data)
sample_data = [
{
'sample_igf_id': 'sampleA',
'project_igf_id': 'projectA',
'species_name': 'HG38'
},
{
'sample_igf_id': 'sampleB',
'project_igf_id': 'projectA',
'species_name': 'UNKNOWN'
},
]
sa = SampleAdaptor(**{'session': base.session})
sa.store_sample_and_attribute_data(data=sample_data)
experiment_data = [
{
'project_igf_id': 'projectA',
'sample_igf_id': 'sampleA',
'experiment_igf_id': 'sampleA_MISEQ',
'library_name': 'sampleA',
'library_source': 'TRANSCRIPTOMIC_SINGLE_CELL',
'library_strategy': 'RNA-SEQ',
'experiment_type': 'TENX-TRANSCRIPTOME-3P',
'library_layout': 'PAIRED',
'platform_name': 'MISEQ',
},
{
'project_igf_id': 'projectA',
'sample_igf_id': 'sampleA',
'experiment_igf_id': 'sampleA_NEXTSEQ',
'library_name': 'sampleA',
'library_source': 'UNKNOWN',
'library_strategy': 'RNA-SEQ',
'experiment_type': 'TENX-TRANSCRIPTOME-3P',
'library_layout': 'PAIRED',
'platform_name': 'NEXTSEQ',
},
{
'project_igf_id': 'projectA',
'sample_igf_id': 'sampleB',
'experiment_igf_id': 'sampleB_MISEQ',
'library_name': 'sampleB',
'library_source': 'TRANSCRIPTOMIC_SINGLE_CELL',
'library_strategy': 'RNA-SEQ',
'experiment_type': 'TENX-TRANSCRIPTOME-3P',
'library_layout': 'PAIRED',
'platform_name': 'MISEQ',
},
]
ea = ExperimentAdaptor(**{'session': base.session})
ea.store_project_and_attribute_data(data=experiment_data)
run_data = [{
'experiment_igf_id': 'sampleA_MISEQ',
'seqrun_igf_id': '180416_M03291_0139_000000000-BRN47',
'run_igf_id': 'sampleA_MISEQ_000000000-BRN47_1',
'lane_number': '1'
}, {
'experiment_igf_id': 'sampleA_NEXTSEQ',
'seqrun_igf_id': '180416_NB03291_013_000000001-BRN47',
'run_igf_id': 'sampleA_NEXTSEQ_000000001-BRN47_2',
'lane_number': '2'
}, {
'experiment_igf_id': 'sampleB_MISEQ',
'seqrun_igf_id': '180416_M03291_0139_000000000-BRN47',
'run_igf_id': 'sampleB_MISEQ_HVWN7BBXX_1',
'lane_number': '1'
}]
ra = RunAdaptor(**{'session': base.session})
ra.store_run_and_attribute_data(data=run_data)
file_data = [
{
'file_path':
'/path/sampleA_MISEQ_000000000-BRN47_1_R1.fastq.gz',
'location': 'HPC_PROJECT',
'md5': 'fd5a95c18ebb7145645e95ce08d729e4',
'size': '1528121404',
},
{
'file_path':
'/path/sampleA_NEXTSEQ_000000001-BRN47_2_R1.fastq.gz',
'location': 'HPC_PROJECT',
'md5': 'fd5a95c18ebb7145645e95ce08d729e4',
'size': '1528121404',
},
{
'file_path': '/path/sampleB_MISEQ_HVWN7BBXX_1_R1.fastq.gz',
'location': 'HPC_PROJECT',
'md5': 'fd5a95c18ebb7145645e95ce08d729e4',
'size': '1528121404',
},
]
fa = FileAdaptor(**{'session': base.session})
fa.store_file_and_attribute_data(data=file_data)
collection_data = [{
'name': 'sampleA_MISEQ_000000000-BRN47_1',
'type': 'demultiplexed_fastq',
'table': 'run'
}, {
'name': 'sampleA_NEXTSEQ_000000001-BRN47_2',
'type': 'demultiplexed_fastq',
'table': 'run'
}, {
'name': 'sampleB_MISEQ_HVWN7BBXX_1',
'type': 'demultiplexed_fastq',
'table': 'run'
}]
collection_files_data = [{
'name':
'sampleA_MISEQ_000000000-BRN47_1',
'type':
'demultiplexed_fastq',
'file_path':
'/path/sampleA_MISEQ_000000000-BRN47_1_R1.fastq.gz'
}, {
'name':
'sampleA_NEXTSEQ_000000001-BRN47_2',
'type':
'demultiplexed_fastq',
'file_path':
'/path/sampleA_NEXTSEQ_000000001-BRN47_2_R1.fastq.gz'
}, {
'name':
'sampleB_MISEQ_HVWN7BBXX_1',
'type':
'demultiplexed_fastq',
'file_path':
'/path/sampleB_MISEQ_HVWN7BBXX_1_R1.fastq.gz'
}]
ca = CollectionAdaptor(**{'session': base.session})
ca.store_collection_and_attribute_data(data=collection_data)
ca.create_collection_group(data=collection_files_data)
base.close_session()
}, {
'run_igf_id': 'RunC',
'experiment_igf_id': 'ExperimentA',
'seqrun_igf_id': '180410_K00345_0063_AHWL7CBBXX',
'lane_number': '1'
}] # run data
base.start_session()
pl = PlatformAdaptor(**{'session': base.session})
pl.store_platform_data(data=platform_data) # loading platform data
pl.store_flowcell_barcode_rule(
data=flowcell_rule_data) # loading flowcell rules data
pa = ProjectAdaptor(**{'session': base.session})
pa.store_project_and_attribute_data(data=project_data) # load project data
sa = SampleAdaptor(**{'session': base.session})
sa.store_sample_and_attribute_data(data=sample_data) # store sample data
sra = SeqrunAdaptor(**{'session': base.session})
sra.store_seqrun_and_attribute_data(data=seqrun_data) # load seqrun data
ea = ExperimentAdaptor(**{'session': base.session})
ea.store_project_and_attribute_data(
data=experiment_data) # load experiment data
ra = RunAdaptor(**{'session': base.session})
ra.store_run_and_attribute_data(data=run_data) # load run data
pipeline_data = [{
"pipeline_name": "DemultiplexIlluminaFastq",
"pipeline_db": "sqlite:////bcl2fastq.db",
}]
pipeline_seed_data = [
{
'pipeline_name': 'DemultiplexIlluminaFastq',
'seed_id': 1,
def setUp(self):
self.dbconfig = 'data/dbconfig.json'
dbparam = read_dbconf_json(self.dbconfig)
base = BaseAdaptor(**dbparam)
self.engine = base.engine
self.dbname = dbparam['dbname']
Base.metadata.drop_all(self.engine)
if os.path.exists(self.dbname):
os.remove(self.dbname)
Base.metadata.create_all(self.engine)
self.session_class = base.get_session_class()
self.temp_work_dir = get_temp_dir()
self.temp_base_dir = get_temp_dir()
self.input_list = ['a.cram', 'a.vcf.gz', 'b.tar.gz']
for file_name in self.input_list:
file_path = os.path.join(self.temp_work_dir, file_name)
with open(file_path, 'w') as fq:
fq.write('AAAA') # create input files
base = BaseAdaptor(**{'session_class': self.session_class})
base.start_session()
platform_data = [{
"platform_igf_id": "M001",
"model_name": "MISEQ",
"vendor_name": "ILLUMINA",
"software_name": "RTA",
"software_version": "RTA1.18.54"
}] # platform data
flowcell_rule_data = [{
"platform_igf_id": "M001",
"flowcell_type": "MISEQ",
"index_1": "NO_CHANGE",
"index_2": "NO_CHANGE"
}] # flowcell rule data
pl = PlatformAdaptor(**{'session': base.session})
pl.store_platform_data(data=platform_data) # loading platform data
pl.store_flowcell_barcode_rule(
data=flowcell_rule_data) # loading flowcell rules data
project_data = [{'project_igf_id': 'ProjectA'}] # project data
pa = ProjectAdaptor(**{'session': base.session})
pa.store_project_and_attribute_data(
data=project_data) # load project data
sample_data = [{
'sample_igf_id': 'SampleA',
'project_igf_id': 'ProjectA'
}] # sample data
sa = SampleAdaptor(**{'session': base.session})
sa.store_sample_and_attribute_data(
data=sample_data) # store sample data
seqrun_data = [{
'seqrun_igf_id': 'SeqrunA',
'flowcell_id': '000000000-D0YLK',
'platform_igf_id': 'M001',
'flowcell': 'MISEQ'
}] # seqrun data
sra = SeqrunAdaptor(**{'session': base.session})
sra.store_seqrun_and_attribute_data(
data=seqrun_data) # load seqrun data
experiment_data = [{
'experiment_igf_id': 'ExperimentA',
'sample_igf_id': 'SampleA',
'library_name': 'SampleA',
'platform_name': 'MISEQ',
'project_igf_id': 'ProjectA'
}] # experiment data
ea = ExperimentAdaptor(**{'session': base.session})
ea.store_project_and_attribute_data(
data=experiment_data) # load experiment data
run_data = [{
'run_igf_id': 'RunA',
'experiment_igf_id': 'ExperimentA',
'seqrun_igf_id': 'SeqrunA',
'lane_number': '1'
}] # run data
ra = RunAdaptor(**{'session': base.session})
ra.store_run_and_attribute_data(data=run_data) # load run data
base.commit_session()
base.close_session()
def setUp(self):
self.dbconfig = 'data/dbconfig.json'
dbparam = read_dbconf_json(self.dbconfig)
base = BaseAdaptor(**dbparam)
self.engine = base.engine
self.dbname = dbparam['dbname']
Base.metadata.create_all(self.engine)
self.session_class = base.get_session_class()
# load platform data
platform_data=\
[{"platform_igf_id" : "M03291" ,
"model_name" : "MISEQ" ,
"vendor_name" : "ILLUMINA" ,
"software_name" : "RTA" ,
"software_version" : "RTA1.18.54"
},
{"platform_igf_id" : "NB501820",
"model_name" : "NEXTSEQ",
"vendor_name" : "ILLUMINA",
"software_name" : "RTA",
"software_version" : "RTA2"
},
{"platform_igf_id" : "K00345",
"model_name" : "HISEQ4000",
"vendor_name" : "ILLUMINA",
"software_name" : "RTA",
"software_version" : "RTA2"
}]
flowcell_rule_data=\
[{"platform_igf_id":"K00345",
"flowcell_type":"HiSeq 3000/4000 SR",
"index_1":"NO_CHANGE",
"index_2":"NO_CHANGE"},
{"platform_igf_id":"K00345",
"flowcell_type":"HiSeq 3000/4000 PE",
"index_1":"NO_CHANGE",
"index_2":"REVCOMP"},
{"platform_igf_id":"NB501820",
"flowcell_type":"NEXTSEQ",
"index_1":"NO_CHANGE",
"index_2":"REVCOMP"},
{"platform_igf_id":"M03291",
"flowcell_type":"MISEQ",
"index_1":"NO_CHANGE",
"index_2":"NO_CHANGE"}]
pl = PlatformAdaptor(**{'session_class': base.session_class})
pl.start_session()
pl.store_platform_data(data=platform_data)
pl.store_flowcell_barcode_rule(data=flowcell_rule_data)
pl.close_session()
# load project data
project_data = [{'project_igf_id': 'IGFQ000472_avik_28-3-2018_RNA'}]
pa = ProjectAdaptor(**{'session_class': base.session_class})
pa.start_session()
pa.store_project_and_attribute_data(data=project_data)
pa.close_session()
# load samples
sample_data = [
{
'project_igf_id': 'IGFQ000472_avik_28-3-2018_RNA',
'sample_igf_id': 'IGF109792',
'expected_read': 40000000
},
{
'project_igf_id': 'IGFQ000472_avik_28-3-2018_RNA',
'sample_igf_id': 'IGF109793',
'expected_read': 40000000
},
{
'project_igf_id': 'IGFQ000472_avik_28-3-2018_RNA',
'sample_igf_id': 'IGF109794',
'expected_read': 40000000
},
{
'project_igf_id': 'IGFQ000472_avik_28-3-2018_RNA',
'sample_igf_id': 'IGF109795',
'expected_read': 40000000
},
{
'project_igf_id': 'IGFQ000472_avik_28-3-2018_RNA',
'sample_igf_id': 'IGF109796',
'expected_read': 40000000
},
{
'project_igf_id': 'IGFQ000472_avik_28-3-2018_RNA',
'sample_igf_id': 'IGF109797',
'expected_read': 40000000
},
{
'project_igf_id': 'IGFQ000472_avik_28-3-2018_RNA',
'sample_igf_id': 'IGF109797_1',
'expected_read': 40000000
},
]
sa = SampleAdaptor(**{'session_class': base.session_class})
sa.start_session()
sa.store_sample_and_attribute_data(data=sample_data)
sa.close_session()
# load seqrun data
seqrun_data = [{
'flowcell_id': 'HV2GJBBXX',
'platform_igf_id': 'K00345',
'seqrun_igf_id': '180518_K00345_0047_BHV2GJBBXX'
}]
sra = SeqrunAdaptor(**{'session_class': base.session_class})
sra.start_session()
sra.store_seqrun_and_attribute_data(data=seqrun_data)
sra.close_session()
# load experiment data
experiment_data=\
[{'experiment_igf_id': 'IGF109792_HISEQ4000',
'library_name': 'IGF109792',
'platform_name': 'HISEQ4000',
'project_igf_id': 'IGFQ000472_avik_28-3-2018_RNA',
'sample_igf_id': 'IGF109792',
},
{'experiment_igf_id': 'IGF109793_HISEQ4000',
'library_name': 'IGF109793',
'platform_name': 'HISEQ4000',
'project_igf_id': 'IGFQ000472_avik_28-3-2018_RNA',
'sample_igf_id': 'IGF109793',
},
{'experiment_igf_id': 'IGF109794_HISEQ4000',
'library_name': 'IGF109794',
'platform_name': 'HISEQ4000',
'project_igf_id': 'IGFQ000472_avik_28-3-2018_RNA',
'sample_igf_id': 'IGF109794',
},
{'experiment_igf_id': 'IGF109795_HISEQ4000',
'library_name': 'IGF109795',
'platform_name': 'HISEQ4000',
'project_igf_id': 'IGFQ000472_avik_28-3-2018_RNA',
'sample_igf_id': 'IGF109795',
},
{'experiment_igf_id': 'IGF109796_HISEQ4000',
'library_name': 'IGF109796',
'platform_name': 'HISEQ4000',
'project_igf_id': 'IGFQ000472_avik_28-3-2018_RNA',
'sample_igf_id': 'IGF109796',
},
{'experiment_igf_id': 'IGF109797_HISEQ4000',
'library_name': 'IGF109797',
'platform_name': 'HISEQ4000',
'project_igf_id': 'IGFQ000472_avik_28-3-2018_RNA',
'sample_igf_id': 'IGF109797',
},
]
ea = ExperimentAdaptor(**{'session_class': base.session_class})
ea.start_session()
ea.store_project_and_attribute_data(data=experiment_data)
ea.close_session()
# load run data
run_data=\
[{'experiment_igf_id': 'IGF109792_HISEQ4000',
'lane_number': '7',
'run_igf_id': 'IGF109792_HISEQ4000_H2N3MBBXY_7',
'seqrun_igf_id': '180518_K00345_0047_BHV2GJBBXX',
'R1_READ_COUNT':288046541
},
{'experiment_igf_id': 'IGF109793_HISEQ4000',
'lane_number': '7',
'run_igf_id': 'IGF109793_HISEQ4000_H2N3MBBXY_7',
'seqrun_igf_id': '180518_K00345_0047_BHV2GJBBXX',
'R1_READ_COUNT':14666330
},
{'experiment_igf_id': 'IGF109794_HISEQ4000',
'lane_number': '7',
'run_igf_id': 'IGF109794_HISEQ4000_H2N3MBBXY_7',
'seqrun_igf_id': '180518_K00345_0047_BHV2GJBBXX',
'R1_READ_COUNT':5009143
},
{'experiment_igf_id': 'IGF109795_HISEQ4000',
'lane_number': '7',
'run_igf_id': 'IGF109795_HISEQ4000_H2N3MBBXY_7',
'seqrun_igf_id': '180518_K00345_0047_BHV2GJBBXX',
'R1_READ_COUNT':1391747
},
{'experiment_igf_id': 'IGF109796_HISEQ4000',
'lane_number': '7',
'run_igf_id': ' IGF109796_HISEQ4000_H2N3MBBXY_7',
'seqrun_igf_id': '180518_K00345_0047_BHV2GJBBXX',
'R1_READ_COUNT':1318008
},
{'experiment_igf_id': 'IGF109797_HISEQ4000',
'lane_number': '7',
'run_igf_id': 'IGF109797_HISEQ4000_H2N3MBBXY_7',
'seqrun_igf_id': '180518_K00345_0047_BHV2GJBBXX',
'R1_READ_COUNT':1216324
},
]
ra = RunAdaptor(**{'session_class': base.session_class})
ra.start_session()
ra.store_run_and_attribute_data(data=run_data)
ra.close_session()
def setUp(self):
self.dbconfig = 'data/dbconfig.json'
dbparam = read_dbconf_json(self.dbconfig)
base = BaseAdaptor(**dbparam)
self.engine = base.engine
self.dbname = dbparam['dbname']
Base.metadata.create_all(self.engine)
self.session_class = base.get_session_class()
base.start_session()
platform_data = [{
"platform_igf_id": "M00001",
"model_name": "MISEQ",
"vendor_name": "ILLUMINA",
"software_name": "RTA",
"software_version": "RTA1.18.54"
}, {
"platform_igf_id": "NB500000",
"model_name": "NEXTSEQ",
"vendor_name": "ILLUMINA",
"software_name": "RTA",
"software_version": "RTA2"
}, {
"platform_igf_id": "K00000",
"model_name": "HISEQ4000",
"vendor_name": "ILLUMINA",
"software_name": "RTA",
"software_version": "RTA2"
}]
flowcell_rule_data = [{
"platform_igf_id": "K00000",
"flowcell_type": "HiSeq 3000/4000 SR",
"index_1": "NO_CHANGE",
"index_2": "NO_CHANGE"
}, {
"platform_igf_id": "K00000",
"flowcell_type": "HiSeq 3000/4000 PE",
"index_1": "NO_CHANGE",
"index_2": "REVCOMP"
}, {
"platform_igf_id": "NB500000",
"flowcell_type": "NEXTSEQ",
"index_1": "NO_CHANGE",
"index_2": "REVCOMP"
}, {
"platform_igf_id": "M00001",
"flowcell_type": "MISEQ",
"index_1": "NO_CHANGE",
"index_2": "NO_CHANGE"
}]
pl = PlatformAdaptor(**{'session': base.session})
pl.store_platform_data(data=platform_data)
pl.store_flowcell_barcode_rule(data=flowcell_rule_data)
seqrun_data = [{
'seqrun_igf_id': '171003_M00001_0089_000000000-TEST',
'flowcell_id': '000000000-D0YLK',
'platform_igf_id': 'M00001',
'flowcell': 'MISEQ',
}]
sra = SeqrunAdaptor(**{'session': base.session})
sra.store_seqrun_and_attribute_data(data=seqrun_data)
seqrun = sra.fetch_seqrun_records_igf_id(
seqrun_igf_id='171003_M00001_0089_000000000-TEST')
pipeline_data = [{
"pipeline_name": "demultiplexing_fastq",
"pipeline_db": "sqlite:////data/bcl2fastq.db",
"pipeline_init_conf": {
"input_dir": "data/seqrun_dir/",
"output_dir": "data"
},
"pipeline_run_conf": {
"output_dir": "data"
}
}]
pipeseed_data = [{
"pipeline_name": "demultiplexing_fastq",
"seed_table": "seqrun",
"seed_id": seqrun.seqrun_id
}]
pp = PipelineAdaptor(**{'session': base.session})
pp.store_pipeline_data(data=pipeline_data)
pp.create_pipeline_seed(
data=pipeseed_data,
required_columns=['pipeline_id', 'seed_id', 'seed_table'])
base.close_session()
self.seqrun_input_list = 'data/reset_samplesheet_md5/seqrun_pipeline_reset_list.txt'
with open(self.seqrun_input_list, 'w') as fp:
fp.write('')
def setUp(self):
self.dbconfig = 'data/dbconfig.json'
self.fastq_dir = 'data/collect_fastq_dir/sc_1_8'
self.model_name = 'NEXTSEQ'
self.flowcell_id = 'TESTABC'
self.seqrun_igf_id = '171003_NB500000_0089_TESTABC'
self.file_location = 'HPC_PROJECT'
self.samplesheet_file = 'data/collect_fastq_dir/sc_1_8/SampleSheet.csv'
self.samplesheet_filename = 'SampleSheet.csv'
self.manifest_name = 'file_manifest.csv'
dbparam = read_dbconf_json(self.dbconfig)
base = BaseAdaptor(**dbparam)
self.engine = base.engine
self.dbname = dbparam['dbname']
Base.metadata.create_all(self.engine)
self.session_class = base.session_class
base.start_session()
platform_data = [{
"platform_igf_id": "M00001",
"model_name": "MISEQ",
"vendor_name": "ILLUMINA",
"software_name": "RTA",
"software_version": "RTA1.18.54"
}, {
"platform_igf_id": "NB500000",
"model_name": "NEXTSEQ",
"vendor_name": "ILLUMINA",
"software_name": "RTA",
"software_version": "RTA2"
}, {
"platform_igf_id": "K00000",
"model_name": "HISEQ4000",
"vendor_name": "ILLUMINA",
"software_name": "RTA",
"software_version": "RTA2"
}]
flowcell_rule_data = [{
"platform_igf_id": "K00000",
"flowcell_type": "HiSeq 3000/4000 SR",
"index_1": "NO_CHANGE",
"index_2": "NO_CHANGE"
}, {
"platform_igf_id": "K00000",
"flowcell_type": "HiSeq 3000/4000 PE",
"index_1": "NO_CHANGE",
"index_2": "REVCOMP"
}, {
"platform_igf_id": "NB500000",
"flowcell_type": "NEXTSEQ",
"index_1": "NO_CHANGE",
"index_2": "REVCOMP"
}, {
"platform_igf_id": "M00001",
"flowcell_type": "MISEQ",
"index_1": "NO_CHANGE",
"index_2": "NO_CHANGE"
}]
pl = PlatformAdaptor(**{'session': base.session})
pl.store_platform_data(data=platform_data)
pl.store_flowcell_barcode_rule(data=flowcell_rule_data)
project_data = [{
'project_igf_id': 'IGFP0001_test_22-8-2017_rna_sc',
'project_name': 'test_22-8-2017_rna',
'description': 'Its project 1',
'project_deadline': 'Before August 2017',
'comments': 'Some samples are treated with drug X',
}]
pa = ProjectAdaptor(**{'session': base.session})
pa.store_project_and_attribute_data(data=project_data)
sample_data = [
{
'sample_igf_id': 'IGF00001',
'project_igf_id': 'IGFP0001_test_22-8-2017_rna_sc',
},
{
'sample_igf_id': 'IGF00002',
'project_igf_id': 'IGFP0001_test_22-8-2017_rna_sc',
},
]
sa = SampleAdaptor(**{'session': base.session})
sa.store_sample_and_attribute_data(data=sample_data)
seqrun_data = [{
'seqrun_igf_id': '171003_NB500000_0089_TESTABC',
'flowcell_id': 'TESTABC',
'platform_igf_id': 'NB500000',
'flowcell': 'NEXTSEQ',
}]
sra = SeqrunAdaptor(**{'session': base.session})
sra.store_seqrun_and_attribute_data(data=seqrun_data)
base.close_session()
def setUp(self):
self.dbconfig = 'data/dbconfig.json'
dbparam = read_dbconf_json(self.dbconfig)
base = BaseAdaptor(**dbparam)
self.engine = base.engine
self.dbname = dbparam['dbname']
Base.metadata.create_all(self.engine)
self.session_class = base.get_session_class()
base.start_session()
platform_data = [
{
"platform_igf_id": "M03291",
"model_name": "MISEQ",
"vendor_name": "ILLUMINA",
"software_name": "RTA",
"software_version": "RTA1.18.54"
},
]
flowcell_rule_data = [{
"platform_igf_id": "M03291",
"flowcell_type": "MISEQ",
"index_1": "NO_CHANGE",
"index_2": "NO_CHANGE"
}]
pl = PlatformAdaptor(**{'session': base.session})
pl.store_platform_data(data=platform_data)
pl.store_flowcell_barcode_rule(data=flowcell_rule_data)
project_data = [{'project_igf_id': 'IGFQ000123_avik_10-4-2018_Miseq'}]
pa = ProjectAdaptor(**{'session': base.session})
pa.store_project_and_attribute_data(data=project_data)
sample_data = [{
'sample_igf_id': 'IGF103923',
'project_igf_id': 'IGFQ000123_avik_10-4-2018_Miseq',
'species_name': 'HG38'
}]
sa = SampleAdaptor(**{'session': base.session})
sa.store_sample_and_attribute_data(data=sample_data)
seqrun_data = [
{
'seqrun_igf_id': '180416_M03291_0139_000000000-BRN47',
'flowcell_id': '000000000-BRN47',
'platform_igf_id': 'M03291',
'flowcell': 'MISEQ'
},
]
sra = SeqrunAdaptor(**{'session': base.session})
sra.store_seqrun_and_attribute_data(data=seqrun_data)
pipeline_data = [
{
"pipeline_name": "PrimaryAnalysis",
"pipeline_db": "sqlite:////bcl2fastq.db"
},
{
"pipeline_name": "DemultiplexIlluminaFastq",
"pipeline_db": "sqlite:////bcl2fastq.db"
},
]
pla = PipelineAdaptor(**{'session': base.session})
pla.store_pipeline_data(data=pipeline_data)
file_data = [
{
'file_path': '/path/S20180405S_S1_L001_R1_001.fastq.gz',
'location': 'HPC_PROJECT',
'md5': 'fd5a95c18ebb7145645e95ce08d729e4',
'size': '1528121404'
},
{
'file_path': '/path/S20180405S_S1_L001_R2_001.fastq.gz',
'location': 'HPC_PROJECT',
'md5': 'fd5a95c18ebb7145645e95ce08d729e4',
'size': '1467047580'
},
{
'file_path': '/path/S20180405S_S3_L001_R2_001.fastq.gz',
'location': 'HPC_PROJECT',
'md5': 'fd5a95c18ebb7145645e95ce08d729e4',
'size': '1467047580'
},
]
fa = FileAdaptor(**{'session': base.session})
fa.store_file_and_attribute_data(data=file_data)
collection_data = [
{
'name': 'IGF103923_MISEQ_000000000-BRN47_1',
'type': 'demultiplexed_fastq',
'table': 'run'
},
{
'name': 'IGF103923_MISEQ1_000000000-BRN47_1',
'type': 'demultiplexed_fastq',
'table': 'run'
},
]
collection_files_data = [
{
'name': 'IGF103923_MISEQ_000000000-BRN47_1',
'type': 'demultiplexed_fastq',
'file_path': '/path/S20180405S_S1_L001_R1_001.fastq.gz'
},
{
'name': 'IGF103923_MISEQ_000000000-BRN47_1',
'type': 'demultiplexed_fastq',
'file_path': '/path/S20180405S_S1_L001_R2_001.fastq.gz'
},
{
'name': 'IGF103923_MISEQ1_000000000-BRN47_1',
'type': 'demultiplexed_fastq',
'file_path': '/path/S20180405S_S3_L001_R2_001.fastq.gz'
},
]
ca = CollectionAdaptor(**{'session': base.session})
ca.store_collection_and_attribute_data(data=collection_data)
ca.create_collection_group(data=collection_files_data)
experiment_data = [{
'project_igf_id': 'IGFQ000123_avik_10-4-2018_Miseq',
'sample_igf_id': 'IGF103923',
'experiment_igf_id': 'IGF103923_MISEQ',
'library_name': 'IGF103923',
'library_source': 'TRANSCRIPTOMIC_SINGLE_CELL',
'library_strategy': 'RNA-SEQ',
'experiment_type': 'TENX-TRANSCRIPTOME-3P',
'library_layout': 'PAIRED',
'platform_name': 'MISEQ'
}, {
'project_igf_id': 'IGFQ000123_avik_10-4-2018_Miseq',
'sample_igf_id': 'IGF103923',
'experiment_igf_id': 'IGF103923_MISEQ1',
'library_name': 'IGF103923_1',
'library_source': 'GENOMIC_SINGLE_CELL',
'library_strategy': 'WGS',
'experiment_type': 'UNKNOWN',
'library_layout': 'PAIRED',
'platform_name': 'MISEQ'
}]
ea = ExperimentAdaptor(**{'session': base.session})
ea.store_project_and_attribute_data(data=experiment_data)
run_data = [{
'experiment_igf_id': 'IGF103923_MISEQ',
'seqrun_igf_id': '180416_M03291_0139_000000000-BRN47',
'run_igf_id': 'IGF103923_MISEQ_000000000-BRN47_1',
'lane_number': '1'
}, {
'experiment_igf_id': 'IGF103923_MISEQ1',
'seqrun_igf_id': '180416_M03291_0139_000000000-BRN47',
'run_igf_id': 'IGF103923_MISEQ1_000000000-BRN47_1',
'lane_number': '1'
}]
ra = RunAdaptor(**{'session': base.session})
ra.store_run_and_attribute_data(data=run_data)
base.close_session()
def setUp(self):
self.dbconfig = 'data/dbconfig.json'
dbparam = read_dbconf_json(self.dbconfig)
base = BaseAdaptor(**dbparam)
self.engine = base.engine
self.dbname = dbparam['dbname']
Base.metadata.drop_all(self.engine)
if os.path.exists(self.dbname):
os.remove(self.dbname)
Base.metadata.create_all(self.engine)
self.session_class = base.get_session_class()
base.start_session()
# PLATFORM
platform_data = [{
"platform_igf_id": "M03291",
"model_name": "MISEQ",
"vendor_name": "ILLUMINA",
"software_name": "RTA",
"software_version": "RTA1.18.54"
}]
flowcell_rule_data = [{
"platform_igf_id": "M03291",
"flowcell_type": "MISEQ",
"index_1": "NO_CHANGE",
"index_2": "NO_CHANGE"
}]
pl = PlatformAdaptor(**{'session': base.session})
pl.store_platform_data(data=platform_data)
pl.store_flowcell_barcode_rule(data=flowcell_rule_data)
# SEQRUN
seqrun_data = [{
'seqrun_igf_id': '180416_M03291_0139_000000000-TEST',
'flowcell_id': '000000000-TEST',
'platform_igf_id': 'M03291',
'flowcell': 'MISEQ',
}, {
'seqrun_igf_id': '180416_M03291_0140_000000000-TEST',
'flowcell_id': '000000000-TEST',
'platform_igf_id': 'M03291',
'flowcell': 'MISEQ',
}]
sra = SeqrunAdaptor(**{'session': base.session})
sra.store_seqrun_and_attribute_data(data=seqrun_data)
# PROJECT
project_data = [{'project_igf_id': 'IGFQ000123_test_10-4-2018_Miseq'}]
pa = ProjectAdaptor(**{'session': base.session})
pa.store_project_and_attribute_data(data=project_data)
# SAMPLE
sample_data = [{
'sample_igf_id': 'IGF00123',
'project_igf_id': 'IGFQ000123_test_10-4-2018_Miseq'
}, {
'sample_igf_id': 'IGF00124',
'project_igf_id': 'IGFQ000123_test_10-4-2018_Miseq'
}]
sa = SampleAdaptor(**{'session': base.session})
sa.store_sample_and_attribute_data(data=sample_data)
# EXPERIMENT
experiment_data = [{
'project_igf_id': 'IGFQ000123_test_10-4-2018_Miseq',
'sample_igf_id': 'IGF00123',
'experiment_igf_id': 'IGF00123_MISEQ',
'library_name': 'IGF00123',
'library_source': 'TRANSCRIPTOMIC_SINGLE_CELL',
'library_strategy': 'RNA-SEQ',
'experiment_type': 'POLYA-RNA',
'library_layout': 'PAIRED',
'platform_name': 'MISEQ',
'singlecell_chemistry': 'TENX'
}, {
'project_igf_id': 'IGFQ000123_test_10-4-2018_Miseq',
'sample_igf_id': 'IGF00124',
'experiment_igf_id': 'IGF00124_MISEQ',
'library_name': 'IGF00124',
'library_source': 'TRANSCRIPTOMIC_SINGLE_CELL',
'library_strategy': 'RNA-SEQ',
'experiment_type': 'POLYA-RNA',
'library_layout': 'PAIRED',
'platform_name': 'MISEQ',
'singlecell_chemistry': 'TENX'
}]
ea = ExperimentAdaptor(**{'session': base.session})
ea.store_project_and_attribute_data(data=experiment_data)
# RUN
run_data = [{
'experiment_igf_id': 'IGF00123_MISEQ',
'seqrun_igf_id': '180416_M03291_0139_000000000-TEST',
'run_igf_id': 'IGF00123_MISEQ_000000000-TEST_1',
'lane_number': '1'
}]
ra = RunAdaptor(**{'session': base.session})
ra.store_run_and_attribute_data(data=run_data)
# PIPELINE
pipeline_data = [{
"pipeline_name": "PrimaryAnalysis",
"pipeline_db": "sqlite:////aln.db",
}, {
"pipeline_name": "DemultiplexingFastq",
"pipeline_db": "sqlite:////fastq.db",
}]
pipeline_seed_data = [
{
'pipeline_name': 'PrimaryAnalysis',
'seed_id': 1,
'seed_table': 'experiment'
},
{
'pipeline_name': 'PrimaryAnalysis',
'seed_id': 2,
'seed_table': 'experiment'
},
{
'pipeline_name': 'DemultiplexingFastq',
'seed_id': 1,
'seed_table': 'seqrun'
},
{
'pipeline_name': 'DemultiplexingFastq',
'seed_id': 2,
'seed_table': 'seqrun'
},
]
update_data = [{
'pipeline_name': 'PrimaryAnalysis',
'seed_id': 2,
'seed_table': 'experiment',
'status': 'FINISHED'
}, {
'pipeline_name': 'DemultiplexingFastq',
'seed_id': 2,
'seed_table': 'seqrun',
'status': 'FINISHED'
}]
pla = PipelineAdaptor(**{'session': base.session})
pla.store_pipeline_data(data=pipeline_data)
pla.create_pipeline_seed(data=pipeline_seed_data)
pla.update_pipeline_seed(update_data)
base.close_session()