diff --git a/run_workflow_phaeoexplorer.py b/run_workflow_phaeoexplorer.py
index 661ea13ca3b60fd6a14b9082b355b16f6141b025..36a44675f37f4a3a0f32836b543e3b41f9369793 100755
--- a/run_workflow_phaeoexplorer.py
+++ b/run_workflow_phaeoexplorer.py
@@ -495,20 +495,120 @@ class RunWorkflow(speciesData.SpeciesData):
# print({"org_id": org_id, "genome_analysis_id": genome_analysis_id, "ogs_analysis_id": ogs_analysis_id})
return({"org_id": org_id, "blastp_analysis_id": blastp_analysis_id})
- def add_interproscan_analysis(self):
+ def add_organism_interproscan_analysis(self):
"""
+ Add OGS and genome vX analyses to Chado database
+ Required for Chado Load Tripal Synchronize workflow (which should be ran as the first workflow)
+ Called outside workflow for practical reasons (Chado add doesn't have an input link for analysis or organism)
+
+ :return:
+
"""
- # Add Interpro analysis to chado
- logging.info("Adding Interproscan analysis to the instance's chado database")
- self.instance.tools.run_tool(
- tool_id="toolshed.g2.bx.psu.edu/repos/gga/chado_analysis_add_analysis/analysis_add_analysis/2.3.4+galaxy0",
+ self.connect_to_instance()
+ self.set_get_history()
+
+ tool_version = "2.3.4+galaxy0"
+
+ get_organism_tool = self.instance.tools.show_tool("toolshed.g2.bx.psu.edu/repos/gga/chado_organism_get_organisms/organism_get_organisms/2.3.4+galaxy0")
+
+ get_organisms = self.instance.tools.run_tool(
+ tool_id="toolshed.g2.bx.psu.edu/repos/gga/chado_organism_get_organisms/organism_get_organisms/%s" % tool_version,
+ history_id=self.history_id,
+ tool_inputs={})
+
+ time.sleep(10) # Ensure the tool has had time to complete
+ org_outputs = get_organisms["outputs"] # Outputs from the get_organism tool
+ org_job_out_id = org_outputs[0]["id"] # ID of the get_organism output dataset (list of dicts)
+ org_json_output = self.instance.datasets.download_dataset(dataset_id=org_job_out_id) # Download the dataset
+ org_output = json.loads(org_json_output) # Turn the dataset into a list for parsing
+
+ org_id = None
+
+ # Look up list of outputs (dictionaries)
+ for organism_output_dict in org_output:
+ if organism_output_dict["genus"] == self.genus and organism_output_dict["species"] == "{0} {1}".format(self.species, self.sex):
+ correct_organism_id = str(organism_output_dict["organism_id"]) # id needs to be a str to be recognized by chado tools
+ org_id = str(correct_organism_id)
+
+
+ if org_id is None:
+ if self.common == "" or self.common is None:
+ add_org_job = self.instance.tools.run_tool(
+ tool_id="toolshed.g2.bx.psu.edu/repos/gga/chado_organism_add_organism/organism_add_organism/%s" % tool_version,
+ history_id=self.history_id,
+ tool_inputs={"abbr": self.abbreviation,
+ "genus": self.genus_uppercase,
+ "species": self.chado_species_name,
+ "common": self.abbreviation})
+ org_job_out_id = add_org_job["outputs"][0]["id"]
+ org_json_output = self.instance.datasets.download_dataset(dataset_id=org_job_out_id)
+ org_output = json.loads(org_json_output)
+ org_id = str(org_output["organism_id"]) # id needs to be a str to be recognized by chado tools
+ else:
+ add_org_job = self.instance.tools.run_tool(
+ tool_id="toolshed.g2.bx.psu.edu/repos/gga/chado_organism_add_organism/organism_add_organism/%s" % tool_version,
+ history_id=self.history_id,
+ tool_inputs={"abbr": self.abbreviation,
+ "genus": self.genus_uppercase,
+ "species": self.chado_species_name,
+ "common": self.common})
+ org_job_out_id = add_org_job["outputs"][0]["id"]
+ org_json_output = self.instance.datasets.download_dataset(dataset_id=org_job_out_id)
+ org_output = json.loads(org_json_output)
+ org_id = str(org_output["organism_id"]) # id needs to be a str to be recognized by chado tools
+
+ # Synchronize newly added organism in Tripal
+ logging.info("Synchronizing organism %s in Tripal" % self.full_name)
+ time.sleep(60)
+ org_sync = self.instance.tools.run_tool(tool_id="toolshed.g2.bx.psu.edu/repos/gga/tripal_organism_sync/organism_sync/3.2.1.0",
+ history_id=self.history_id,
+ tool_inputs={"organism_id": org_id})
+
+
+ get_analyses = self.instance.tools.run_tool(
+ tool_id="toolshed.g2.bx.psu.edu/repos/gga/chado_analysis_get_analyses/analysis_get_analyses/%s" % tool_version,
history_id=self.history_id,
- tool_inputs={"name": "InterproScan on OGS%s" % self.ogs_version,
- "program": "InterproScan",
- "programversion": "OGS%s" % self.ogs_version,
- "sourcename": "Genoscope",
- "date_executed": self.date})
+ tool_inputs={})
+
+ time.sleep(10)
+ analysis_outputs = get_analyses["outputs"]
+ analysis_job_out_id = analysis_outputs[0]["id"]
+ analysis_json_output = self.instance.datasets.download_dataset(dataset_id=analysis_job_out_id)
+ analysis_output = json.loads(analysis_json_output)
+
+ interpro_analysis_id = None
+
+ # Look up list of outputs (dictionaries)
+ for analysis_output_dict in analysis_output:
+ if analysis_output_dict["name"] == "Interproscan on " + self.full_name_lowercase + " OGS" + self.ogs_version:
+ interpro_analysis_id = str(analysis_output_dict["analysis_id"])
+
+
+ if interpro_analysis_id is None:
+ add_interproscan_analysis_job = self.instance.tools.run_tool(
+ tool_id="toolshed.g2.bx.psu.edu/repos/gga/chado_analysis_add_analysis/analysis_add_analysis/%s" % tool_version,
+ history_id=self.history_id,
+ tool_inputs={"name": "Interproscan on " + self.full_name_lowercase + " OGS" + self.ogs_version,
+ "program": "Performed by Genoscope",
+ "programversion": str(self.sex + " OGS" + self.ogs_version),
+ "sourcename": "Genoscope",
+ "date_executed": self.date})
+ analysis_outputs = add_interproscan_analysis_job["outputs"]
+ analysis_job_out_id = analysis_outputs[0]["id"]
+ analysis_json_output = self.instance.datasets.download_dataset(dataset_id=analysis_job_out_id)
+ analysis_output = json.loads(analysis_json_output)
+ interpro_analysis_id = str(analysis_output["analysis_id"])
+
+ # Synchronize blastp analysis
+ logging.info("Synchronizing Diamong blastp OGS%s analysis in Tripal" % self.ogs_version)
+ time.sleep(60)
+ interproscan_analysis_sync = self.instance.tools.run_tool(tool_id="toolshed.g2.bx.psu.edu/repos/gga/tripal_analysis_sync/analysis_sync/3.2.1.0",
+ history_id=self.history_id,
+ tool_inputs={"analysis_id": interpro_analysis_id})
+
+ # print({"org_id": org_id, "genome_analysis_id": genome_analysis_id, "ogs_analysis_id": ogs_analysis_id})
+ return({"org_id": org_id, "interpro_analysis_id": interpro_analysis_id})
def get_interpro_analysis_id(self):
@@ -628,6 +728,8 @@ class RunWorkflow(speciesData.SpeciesData):
proteins_hda_id = dataset_id
if dataset_name == "{0}_OGS{1}_blastp.xml".format(self.dataset_prefix, self.ogs_version):
blastp_hda_id = dataset_id
+ if dataset_name == "{0}_OGS{1}_interproscan.xml".format(self.dataset_prefix, self.ogs_version):
+ interproscan_hda_id = dataset_id
# Import each dataset into history if it is not imported
@@ -675,67 +777,6 @@ class RunWorkflow(speciesData.SpeciesData):
"interproscan_hda_id": interproscan_hda_id}
- def get_datasets_hda_ids(self):
- """
- Get the hda IDs of the datasets imported into an history
-
- As some tools will not work using the input datasets ldda IDs we need to retrieve the datasets IDs imported
- into an history
-
-
- :return:
- """
-
- # List of all datasets in the instance (including outputs from jobs)
- # "limit" and "offset" options *may* be used to restrict search to specific datasets but since
- # there is no way to know which imported datasets are the correct ones depending on history content
- # it's not currently used
- history_datasets_li = self.instance.datasets.get_datasets()
-
- genome_dataset_hda_id, gff_dataset_hda_id, transcripts_dataset_hda_id, proteins_datasets_hda_id = None, None, None, None
- interproscan_dataset_hda_id, blast_diamond_dataset_hda_id = None, None
-
- # Match files imported in history names vs library datasets names to assign their respective hda_id
- for dataset_dict in history_datasets_li:
- if dataset_dict["history_id"] == self.history_id:
- if dataset_dict["name"] == self.datasets_name["genome_file"] and dataset_dict["id"] not in imported_datasets_ids:
- genome_dataset_hda_id = dataset_dict["id"]
- elif dataset_dict["name"] == self.datasets_name["proteins_file"] and dataset_dict["id"] not in imported_datasets_ids:
- proteins_datasets_hda_id = dataset_dict["id"]
- elif dataset_dict["name"] == self.datasets_name["transcripts_file"] and dataset_dict["id"] not in imported_datasets_ids:
- transcripts_dataset_hda_id = dataset_dict["id"]
- elif dataset_dict["name"] == self.datasets_name["gff_file"] and dataset_dict["id"] not in imported_datasets_ids:
- gff_dataset_hda_id = dataset_dict["id"]
- if "interproscan_file" in self.datasets_name.keys():
- if dataset_dict["name"] == self.datasets_name["interproscan_file"] and dataset_dict["id"] not in imported_datasets_ids:
- interproscan_dataset_hda_id = dataset_dict["id"]
- if "blast_diamond_file" in self.datasets_name.keys():
- if dataset_dict["name"] == self.datasets_name["blastp_file"] and dataset_dict["id"] not in imported_datasets_ids:
- blastp_dataset_hda_id = dataset_dict["id"]
-
- logging.debug("Genome dataset hda id: %s" % genome_dataset_hda_id)
- logging.debug("Proteins dataset hda ID: %s" % proteins_datasets_hda_id)
- logging.debug("Transcripts dataset hda ID: %s" % transcripts_dataset_hda_id)
- logging.debug("GFF dataset hda ID: %s" % gff_dataset_hda_id)
- logging.debug("InterproScan dataset hda ID: %s" % gff_dataset_hda_id)
- logging.debug("Blastp Diamond dataset hda ID: %s" % blastp_dataset_hda_id)
-
- # Add datasets IDs to already imported IDs (so we don't assign all the wrong IDs to the next organism if there is one)
- imported_datasets_ids.append(genome_dataset_hda_id)
- imported_datasets_ids.append(transcripts_dataset_hda_id)
- imported_datasets_ids.append(proteins_datasets_hda_id)
- imported_datasets_ids.append(gff_dataset_hda_id)
- imported_datasets_ids.append(interproscan_dataset_hda_id)
- imported_datasets_ids.append(blastp_dataset_hda_id)
-
- # Return a dict made of the hda ids
- return {"genome_hda_id": genome_dataset_hda_id, "transcripts_hda_id": transcripts_dataset_hda_id,
- "proteins_hda_id": proteins_datasets_hda_id, "gff_hda_id": gff_dataset_hda_id,
- "interproscan_hda_id": interproscan_dataset_hda_id,
- "blastp_hda_id": blastp_dataset_hda_id,
- "imported_datasets_ids": imported_datasets_ids}
-
-
def run_workflow(workflow_path, workflow_parameters, datamap, config, input_species_number):
"""
Run a workflow in galaxy
@@ -906,6 +947,45 @@ def create_sp_workflow_dict(sp_dict, main_dir, config, workflow_type):
sp_workflow_dict[genus_species] = {strain_sex: attributes}
+
+ if workflow_type == "interpro":
+ run_workflow_for_current_organism.connect_to_instance()
+
+ history_id = run_workflow_for_current_organism.set_get_history()
+
+ run_workflow_for_current_organism.install_changesets_revisions_for_individual_tools()
+ ids = run_workflow_for_current_organism.add_organism_interproscan_analysis()
+
+ org_id = None
+ org_id = ids["org_id"]
+ interpro_analysis_id = None
+ interpro_analysis_id = ids["interpro_analysis_id"]
+ instance_attributes = run_workflow_for_current_organism.get_instance_attributes()
+ hda_ids = run_workflow_for_current_organism.import_datasets_into_history()
+
+ strain_sex = "{0}_{1}".format(run_workflow_for_current_organism.strain, run_workflow_for_current_organism.sex)
+ genus_species = run_workflow_for_current_organism.genus_species
+
+ # Create the dictionary holding all attributes needed to connect to the galaxy instance
+ attributes = {"genus": run_workflow_for_current_organism.genus,
+ "species": run_workflow_for_current_organism.species,
+ "genus_species": run_workflow_for_current_organism.genus_species,
+ "full_name": run_workflow_for_current_organism.full_name,
+ "species_folder_name": run_workflow_for_current_organism.species_folder_name,
+ "sex": run_workflow_for_current_organism.sex,
+ "strain": run_workflow_for_current_organism.strain,
+ "org_id": org_id,
+ "interpro_analysis_id": interpro_analysis_id,
+ "instance_attributes": instance_attributes,
+ "hda_ids": hda_ids,
+ "history_id": history_id,
+ "instance": run_workflow_for_current_organism.instance,
+ "instance_url": run_workflow_for_current_organism.instance_url,
+ "email": config["galaxy_default_admin_email"],
+ "password": config["galaxy_default_admin_password"]}
+
+ sp_workflow_dict[genus_species] = {strain_sex: attributes}
+
else:
logging.critical("The galaxy container for %s is not ready yet!" % run_workflow_for_current_organism.full_name)
sys.exit()
@@ -1164,13 +1244,6 @@ if __name__ == "__main__":
else:
jbrowse_menu_url_org = config["jbrowse_menu_url"] + "/sp/{genus_sp}/feature/{Genus}/{species}/mRNA/{id}".format(genus_sp=org_genus_species, Genus=org_genus[0].upper() + org_genus[1:], species=org_species, id="{id}")
- # show_tool_add_organism = instance.tools.show_tool(tool_id="toolshed.g2.bx.psu.edu/repos/gga/chado_organism_add_organism/organism_add_organism/2.3.4+galaxy0", io_details=True)
- # print(show_tool_add_organism)
- # show_jbrowse_tool = instance.tools.show_tool(tool_id="toolshed.g2.bx.psu.edu/repos/iuc/jbrowse/jbrowse/1.16.11+galaxy0", io_details=True)
- # print(show_jbrowse_tool)
- # show_jbrowse_container_tool = instance.tools.show_tool(tool_id="toolshed.g2.bx.psu.edu/repos/gga/jbrowse_to_container/jbrowse_to_container/0.5.1", io_details=True)
- # print(show_jbrowse_container_tool)
-
# Replace values in the workflow dictionary
workflow_dict["steps"]["4"]["tool_state"] = workflow_dict["steps"]["4"]["tool_state"].replace("__MENU_URL_ORG__", jbrowse_menu_url_org)
workflow_dict["steps"]["6"]["tool_state"] = workflow_dict["steps"]["6"]["tool_state"].replace("__DISPLAY_NAME_ORG__", org_full_name).replace("__UNIQUE_ID_ORG__", org_species_folder_name)
@@ -1190,7 +1263,7 @@ if __name__ == "__main__":
logging.warning("Error finding workflow %s" % workflow_name)
# Finally, invoke the workflow alogn with its datamap, parameters and the history in which to invoke it
- # instance.workflows.invoke_workflow(workflow_id=workflow_id, history_id=history_id, params=workflow_parameters, inputs=datamap, allow_tool_state_corrections=True)
+ instance.workflows.invoke_workflow(workflow_id=workflow_id, history_id=history_id, params=workflow_parameters, inputs=datamap, allow_tool_state_corrections=True)
logging.info("Successfully imported and invoked workflow {0}, check the galaxy instance ({1}) for the jobs state".format(workflow_name, instance_url))
@@ -1426,7 +1499,7 @@ if __name__ == "__main__":
logging.warning("Error finding workflow %s" % workflow_name)
# Finally, invoke the workflow alogn with its datamap, parameters and the history in which to invoke it
- # instance.workflows.invoke_workflow(workflow_id=workflow_id, history_id=history_id, params=workflow_parameters, inputs=datamap, allow_tool_state_corrections=True)
+ instance.workflows.invoke_workflow(workflow_id=workflow_id, history_id=history_id, params=workflow_parameters, inputs=datamap, allow_tool_state_corrections=True)
logging.info("Successfully imported and invoked workflow {0}, check the galaxy instance ({1}) for the jobs state".format(workflow_name, instance_url))
@@ -1541,7 +1614,7 @@ if __name__ == "__main__":
logging.warning("Error finding workflow %s" % workflow_name)
# Finally, invoke the workflow alogn with its datamap, parameters and the history in which to invoke it
- # instance.workflows.invoke_workflow(workflow_id=workflow_id, history_id=history_id, params=workflow_parameters, inputs=datamap, allow_tool_state_corrections=True)
+ instance.workflows.invoke_workflow(workflow_id=workflow_id, history_id=history_id, params=workflow_parameters, inputs=datamap, allow_tool_state_corrections=True)
logging.info("Successfully imported and invoked workflow {0}, check the galaxy instance ({1}) for the jobs state".format(workflow_name, instance_url))
@@ -1695,7 +1768,276 @@ if __name__ == "__main__":
logging.warning("Error finding workflow %s" % workflow_name)
# Finally, invoke the workflow alogn with its datamap, parameters and the history in which to invoke it
- # instance.workflows.invoke_workflow(workflow_id=workflow_id, history_id=history_id, params=workflow_parameters, inputs=datamap, allow_tool_state_corrections=True)
+ instance.workflows.invoke_workflow(workflow_id=workflow_id, history_id=history_id, params=workflow_parameters, inputs=datamap, allow_tool_state_corrections=True)
+
+ logging.info("Successfully imported and invoked workflow {0}, check the galaxy instance ({1}) for the jobs state".format(workflow_name, instance_url))
+
+
+ if workflow_type == "inteproscan":
+ for sp_dict in sp_dict_list:
+
+ # Add and retrieve all analyses/organisms for the current input species and add their IDs to the input dictionary
+ current_sp_workflow_dict = create_sp_workflow_dict(sp_dict, main_dir=args.main_directory, config=config, workfow_type="blast")
+
+ current_sp_key = list(current_sp_workflow_dict.keys())[0]
+ current_sp_value = list(current_sp_workflow_dict.values())[0]
+ current_sp_strain_sex_key = list(current_sp_value.keys())[0]
+ current_sp_strain_sex_value = list(current_sp_value.values())[0]
+
+ # Add the species dictionary to the complete dictionary
+ # This dictionary contains every organism present in the input file
+ # Its structure is the following:
+ # {genus species: {strain1_sex1: {variables_key: variables_values}, strain1_sex2: {variables_key: variables_values}}}
+ if not current_sp_key in all_sp_workflow_dict.keys():
+ all_sp_workflow_dict[current_sp_key] = current_sp_value
+ else:
+ all_sp_workflow_dict[current_sp_key][current_sp_strain_sex_key] = current_sp_strain_sex_value
+
+ if len(list(v.keys())) == 1:
+ logging.info("Input organism %s: 1 species detected in input dictionary" % k)
+
+ # Set workflow path (1 organism)
+ workflow_path = os.path.join(os.path.abspath(script_dir), "workflows_phaeoexplorer/Galaxy-Workflow-load_blast_results_1org_v1.ga")
+
+ # Instance object required variables
+ instance_url, email, password = None, None, None
+
+ # Set the galaxy instance variables
+ for k2, v2 in v.items():
+ instance_url = v2["instance_url"]
+ email = v2["email"]
+ password = v2["password"]
+
+ instance = galaxy.GalaxyInstance(url=instance_url, email=email, password=password)
+
+ # Check if the versions of tools specified in the workflow are installed in galaxy
+ install_changesets_revisions_from_workflow(workflow_path=workflow_path, instance=instance)
+
+ organism_key_name = list(v.keys())
+ org_dict = v[organisms_key_name[0]]
+
+ history_id = org_dict["history_id"]
+
+ # Organism attributes
+ org_genus = org_dict["genus"]
+ org_species = org_dict["species"]
+ org_genus_species = org_dict["genus_species"]
+ org_species_folder_name = org_dict["species_folder_name"]
+ org_full_name = org_dict["full_name"]
+ org_strain = org_dict["sex"]
+ org_sex = org_dict["strain"]
+ org_org_id = org_dict["org_id"]
+ org_inteproscan_analysis_id = org_dict["inteproscan_analysis_id"]
+ org_interproscan_hda_id = org_dict["hda_ids"]["interproscan_hda_id"]
+
+ # Store these values into a dict for parameters logging/validation
+ org_parameters_dict = {
+ "org_genus": org_genus,
+ "org_species": org_species,
+ "org_genus_species": org_genus_species,
+ "org_species_folder_name": org_species_folder_name,
+ "org_full_name": org_full_name,
+ "org_strain": org_strain,
+ "org_sex": org_sex,
+ "org_org_id": org_org_id,
+ "org_inteproscan_analysis_id": org_inteproscan_analysis_id,
+ "org_interproscan_hda_id": org_interproscan_hda_id,
+ }
+
+ # Look for empty parameters values, throw a critical error if a parameter value is invalid
+ for param_name, param_value in org_parameters_dict.items():
+ if param_value is None or param_value == "":
+ logging.critical("Empty parameter value found for organism {0} (parameter: {1}, parameter value: {2})".format(org_full_name, param_name, param_value))
+ sys.exit()
+
+ INTEPRO_FILE = "0"
+ LOAD_INTERPRO_FILE = "1"
+ POPULATE_MAT_VIEWS = "2"
+ INDEX_TRIPAL_DATA = "3"
+
+ # Set the workflow parameters (individual tools runtime parameters in the workflow)
+ workflow_parameters = {}
+ workflow_parameters[INTEPRO_FILE] = {}
+ workflow_parameters[LOAD_INTERPRO_FILE] = {"analysis_id": org_inteproscan_analysis_id, "organism_id": org_org_id}
+ workflow_parameters[POPULATE_MAT_VIEWS] = {}
+ workflow_parameters[INDEX_TRIPAL_DATA] = {}
+
+ datamap = {}
+ datamap[INTEPRO_FILE] = {"src": "hda", "id": org_interproscan_hda_id}
+
+ with open(workflow_path, 'r') as ga_in_file:
+ # Store the decoded json dictionary
+ workflow_dict = json.load(ga_in_file)
+ workflow_name = workflow_dict["name"]
+
+ # Import the workflow in galaxy as a dict
+ instance.workflows.import_workflow_dict(workflow_dict=workflow_dict)
+ # Get its attributes
+ workflow_attributes = instance.workflows.get_workflows(name=workflow_name)
+ # Then get its ID (required to invoke the workflow)
+ workflow_id = workflow_attributes[0]["id"] # Index 0 is the most recently imported workflow (the one we want)
+ show_workflow = instance.workflows.show_workflow(workflow_id=workflow_id)
+ # Check if the workflow is found
+ try:
+ logging.debug("Workflow ID: %s" % workflow_id)
+ except bioblend.ConnectionError:
+ logging.warning("Error finding workflow %s" % workflow_name)
+
+ # Finally, invoke the workflow alogn with its datamap, parameters and the history in which to invoke it
+ instance.workflows.invoke_workflow(workflow_id=workflow_id, history_id=history_id, params=workflow_parameters, inputs=datamap, allow_tool_state_corrections=True)
logging.info("Successfully imported and invoked workflow {0}, check the galaxy instance ({1}) for the jobs state".format(workflow_name, instance_url))
+
+
+ if len(list(v.keys())) == 2:
+
+ logging.info("Input organism %s: 2 species detected in input dictionary" % k)
+
+ # Set workflow path (2 organisms)
+ workflow_path = os.path.join(os.path.abspath(script_dir), "workflows_phaeoexplorer/Galaxy-Workflow-load_blast_results_2org_v1.ga")
+
+ # Instance object required variables
+ instance_url, email, password = None, None, None
+
+ # Set the galaxy instance variables
+ for k2, v2 in v.items():
+ instance_url = v2["instance_url"]
+ email = v2["email"]
+ password = v2["password"]
+
+ instance = galaxy.GalaxyInstance(url=instance_url, email=email, password=password)
+
+ # Check if the versions of tools specified in the workflow are installed in galaxy
+ install_changesets_revisions_from_workflow(workflow_path=workflow_path, instance=instance)
+
+ organisms_key_names = list(v.keys())
+ org1_dict = v[organisms_key_names[0]]
+ org2_dict = v[organisms_key_names[1]]
+
+ history_id = org1_dict["history_id"]
+
+ # Organism 1 attributes
+ org1_genus = org1_dict["genus"]
+ org1_species = org1_dict["species"]
+ org1_genus_species = org1_dict["genus_species"]
+ org1_species_folder_name = org1_dict["species_folder_name"]
+ org1_full_name = org1_dict["full_name"]
+ org1_strain = org1_dict["sex"]
+ org1_sex = org1_dict["strain"]
+ org1_org_id = org1_dict["org_id"]
+ org1_interproscan_analysis_id = org1_dict["interproscan_analysis_id"]
+ org1_interproscan_hda_id = org1_dict["hda_ids"]["interproscan_hda_id"]
+
+ # Store these values into a dict for parameters logging/validation
+ org1_parameters_dict = {
+ "org1_genus": org1_genus,
+ "org1_species": org1_species,
+ "org1_genus_species": org1_genus_species,
+ "org1_species_folder_name": org1_species_folder_name,
+ "org1_full_name": org1_full_name,
+ "org1_strain": org1_strain,
+ "org1_sex": org1_sex,
+ "org1_org_id": org1_org_id,
+ "org1_interproscan_analysis_id": org1_interproscan_analysis_id,
+ "org1_interproscan_hda_id": org1_interproscan_hda_id,
+ }
+
+
+ # Look for empty parameters values, throw a critical error if a parameter value is invalid
+ for param_name, param_value in org1_parameters_dict.items():
+ if param_value is None or param_value == "":
+ logging.critical("Empty parameter value found for organism {0} (parameter: {1}, parameter value: {2})".format(org1_full_name, param_name, param_value))
+ sys.exit()
+
+ # Organism 2 attributes
+ org2_genus = org2_dict["genus"]
+ org2_species = org2_dict["species"]
+ org2_genus_species = org2_dict["genus_species"]
+ org2_species_folder_name = org2_dict["species_folder_name"]
+ org2_full_name = org2_dict["full_name"]
+ org2_strain = org2_dict["sex"]
+ org2_sex = org2_dict["strain"]
+ org2_org_id = org2_dict["org_id"]
+ org2_interproscan_analysis_id = org2_dict["interproscan_analysis_id"]
+ org2_interproscan_hda_id = org2_dict["hda_ids"]["interproscan_hda_id"]
+
+ # Store these values into a dict for parameters logging/validation
+ org2_parameters_dict = {
+ "org2_genus": org2_genus,
+ "org2_species": org2_species,
+ "org2_genus_species": org2_genus_species,
+ "org2_species_folder_name": orgé_species_folder_name,
+ "org2_full_name": org2_full_name,
+ "org2_strain": org2_strain,
+ "org2_sex": org2_sex,
+ "org2_org_id": org2_org_id,
+ "org2_interproscan_analysis_id": org2_interproscan_analysis_id,
+ "org2_interproscan_hda_id": org2_interproscan_hda_id,
+ }
+
+
+ # Look for empty parameters values, throw a critical error if a parameter value is invalid
+ for param_name, param_value in org2_parameters_dict.items():
+ if param_value is None or param_value == "":
+ logging.critical("Empty parameter value found for organism {0} (parameter: {1}, parameter value: {2})".format(org2_full_name, param_name, param_value))
+ sys.exit()
+
+ # Source files association (ordered by their IDs in the workflow)
+ # WARNING: Be very careful about how the workflow is "organized" (i.e the order of the steps/datasets, check the .ga if there is any error)
+ INTERPRO_FILE_ORG1 = "0"
+ INTERPRO_FILE_ORG2 = "1"
+ LOAD_INTERPRO_FILE_ORG1 = "2"
+ LOAD_INTERPRO_FILE_ORG2 = "3"
+ POPULATE_MAT_VIEWS = "4"
+ INDEX_TRIPAL_DATA = "5"
+
+ # Set the workflow parameters (individual tools runtime parameters in the workflow)
+ workflow_parameters = {}
+
+ # Input files have no parameters (they are set via assigning the hda IDs in the datamap parameter of the bioblend method)
+ workflow_parameters[INTERPRO_FILE_ORG1] = {}
+ workflow_parameters[INTERPRO_FILE_ORG2] = {}
+
+ # Organism 1
+ workflow_parameters[LOAD_INTERPRO_FILE_ORG1] = {"organism_id": org1_org_id,
+ "analysis_id": org1_interproscan_analysis_id}
+
+ # Organism 2
+ workflow_parameters[LOAD_INTERPRO_FILE_ORG2] = {"organism_id": org2_org_id,
+ "analysis_id": org2_interproscan_analysis_id}
+
+ workflow_parameters[POPULATE_MAT_VIEWS] = {}
+ workflow_parameters[INDEX_TRIPAL_DATA] = {}
+
+ # Set datamap (mapping of input files in the workflow)
+ datamap = {}
+
+ # Organism 1
+ datamap[BLASTP_FILE_ORG1] = {"src": "hda", "id": org1_interproscan_hda_id}
+
+ # Organism 2
+ datamap[BLASTP_FILE_ORG2] = {"src": "hda", "id": org2_interproscan_hda_id}
+
+ with open(workflow_path, 'r') as ga_in_file:
+ # Store the decoded json dictionary
+ workflow_dict = json.load(ga_in_file)
+ workflow_name = workflow_dict["name"]
+
+ # Import the workflow in galaxy as a dict
+ instance.workflows.import_workflow_dict(workflow_dict=workflow_dict)
+ # Get its attributes
+ workflow_attributes = instance.workflows.get_workflows(name=workflow_name)
+ # Then get its ID (required to invoke the workflow)
+ workflow_id = workflow_attributes[0]["id"] # Index 0 is the most recently imported workflow (the one we want)
+ show_workflow = instance.workflows.show_workflow(workflow_id=workflow_id)
+ # Check if the workflow is found
+ try:
+ logging.debug("Workflow ID: %s" % workflow_id)
+ except bioblend.ConnectionError:
+ logging.warning("Error finding workflow %s" % workflow_name)
+
+ # Finally, invoke the workflow alogn with its datamap, parameters and the history in which to invoke it
+ instance.workflows.invoke_workflow(workflow_id=workflow_id, history_id=history_id, params=workflow_parameters, inputs=datamap, allow_tool_state_corrections=True)
+
+ logging.info("Successfully imported and invoked workflow {0}, check the galaxy instance ({1}) for the jobs state".format(workflow_name, instance_url))
diff --git a/workflows_phaeoexplorer/Galaxy-Workflow-load_interproscan_1org_v1.ga b/workflows_phaeoexplorer/Galaxy-Workflow-load_interproscan_1org_v1.ga
new file mode 100644
index 0000000000000000000000000000000000000000..d05f0c843520ef23f3a476e4a870dd37268a118b
--- /dev/null
+++ b/workflows_phaeoexplorer/Galaxy-Workflow-load_interproscan_1org_v1.ga
@@ -0,0 +1,216 @@
+{
+ "a_galaxy_workflow": "true",
+ "annotation": "",
+ "format-version": "0.1",
+ "name": "load_inteproscan_1org1_v1",
+ "steps": {
+ "0": {
+ "annotation": "",
+ "content_id": null,
+ "errors": null,
+ "id": 0,
+ "input_connections": {},
+ "inputs": [
+ {
+ "description": "",
+ "name": "inteproscan file org1"
+ }
+ ],
+ "label": "inteproscan file org1",
+ "name": "Input dataset",
+ "outputs": [],
+ "position": {
+ "bottom": 319.1999969482422,
+ "height": 82.19999694824219,
+ "left": 287,
+ "right": 487,
+ "top": 237,
+ "width": 200,
+ "x": 287,
+ "y": 237
+ },
+ "tool_id": null,
+ "tool_state": "{\"optional\": false}",
+ "tool_version": null,
+ "type": "data_input",
+ "uuid": "3b13466d-1b81-475a-b652-183ed9d24bfa",
+ "workflow_outputs": [
+ {
+ "label": null,
+ "output_name": "output",
+ "uuid": "aff82f29-1971-4283-bfe8-cdab4857a215"
+ }
+ ]
+ },
+ "1": {
+ "annotation": "",
+ "content_id": "toolshed.g2.bx.psu.edu/repos/gga/chado_load_interpro/load_interpro/2.3.6+galaxy0",
+ "errors": null,
+ "id": 1,
+ "input_connections": {
+ "input": {
+ "id": 0,
+ "output_name": "output"
+ }
+ },
+ "inputs": [
+ {
+ "description": "runtime parameter for tool Chado load InterProScan results",
+ "name": "analysis_id"
+ },
+ {
+ "description": "runtime parameter for tool Chado load InterProScan results",
+ "name": "input"
+ },
+ {
+ "description": "runtime parameter for tool Chado load InterProScan results",
+ "name": "organism_id"
+ },
+ {
+ "description": "runtime parameter for tool Chado load InterProScan results",
+ "name": "wait_for"
+ }
+ ],
+ "label": "interproscan load org 1",
+ "name": "Chado load InterProScan results",
+ "outputs": [
+ {
+ "name": "results",
+ "type": "json"
+ }
+ ],
+ "position": {
+ "bottom": 371.3999938964844,
+ "height": 164.39999389648438,
+ "left": 595,
+ "right": 795,
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+ "width": 200,
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+ "post_job_actions": {},
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+ "tool_shed_repository": {
+ "changeset_revision": "1e54f2717e74",
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+ "outputs": [
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+ "type": "txt"
+ }
+ ],
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+ "type": "tool",
+ "uuid": "2acbe412-1318-46e8-a178-72f50df36a07",
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+ "uuid": "08a4eec2-b95a-4c75-8116-b0fc9477812f"
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+ ]
+ },
+ "3": {
+ "annotation": "",
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+ "id": 2,
+ "output_name": "results"
+ }
+ },
+ "inputs": [],
+ "label": null,
+ "name": "Index Tripal data",
+ "outputs": [
+ {
+ "name": "results",
+ "type": "txt"
+ }
+ ],
+ "position": {
+ "bottom": 340.6000061035156,
+ "height": 113.60000610351562,
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+ "width": 200,
+ "x": 1150,
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+ },
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+ "tool_shed_repository": {
+ "changeset_revision": "d55a39f12dda",
+ "name": "tripal_db_index",
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+ },
+ "tool_state": "{\"expose\": {\"do_expose\": \"no\", \"__current_case__\": 0}, \"queues\": \"10\", \"table\": {\"mode\": \"website\", \"__current_case__\": 0}, \"tokenizer\": \"standard\", \"wait_for\": {\"__class__\": \"ConnectedValue\"}, \"__page__\": null, \"__rerun_remap_job_id__\": null}",
+ "tool_version": "3.2.1.1",
+ "type": "tool",
+ "uuid": "42d782ff-9e6b-49d8-bd17-37dc67f31a18",
+ "workflow_outputs": [
+ {
+ "label": "Index Tripal data",
+ "output_name": "results",
+ "uuid": "8b38b48f-5d4e-49e6-ad70-748ad64dc901"
+ }
+ ]
+ }
+ },
+ "tags": [],
+ "uuid": "9912daac-c00e-4d5e-a93e-a747bd1b3499",
+ "version": 3
+}
\ No newline at end of file
diff --git a/workflows_phaeoexplorer/Galaxy-Workflow-load_interproscan_2org_v1.ga b/workflows_phaeoexplorer/Galaxy-Workflow-load_interproscan_2org_v1.ga
new file mode 100644
index 0000000000000000000000000000000000000000..e15b1885b432f45ae64e9f3d9079537dd602d5ae
--- /dev/null
+++ b/workflows_phaeoexplorer/Galaxy-Workflow-load_interproscan_2org_v1.ga
@@ -0,0 +1,335 @@
+{
+ "a_galaxy_workflow": "true",
+ "annotation": "",
+ "format-version": "0.1",
+ "name": "load_interproscan_2org_v1",
+ "steps": {
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+ "annotation": "",
+ "content_id": null,
+ "errors": null,
+ "id": 0,
+ "input_connections": {},
+ "inputs": [
+ {
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+ }
+ ],
+ "label": "interproscan file org 1",
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+ "position": {
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+ "height": 82.19999694824219,
+ "left": 414,
+ "right": 614,
+ "top": 268,
+ "width": 200,
+ "x": 414,
+ "y": 268
+ },
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+ "tool_state": "{\"optional\": false}",
+ "tool_version": null,
+ "type": "data_input",
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+ "workflow_outputs": [
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+ "output_name": "output",
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