From 6ba67a53b4908cc004f6999067310b2d18463527 Mon Sep 17 00:00:00 2001
From: Arthur Le Bars <arthur.le-bars@sb-roscoff.fr>
Date: Fri, 30 Apr 2021 09:06:20 +0200
Subject: [PATCH] workflow 2org
---
run_workflow_phaeoexplorer.py | 1173 +++++++++++++++++++++------------
speciesData.py | 13 +-
2 files changed, 763 insertions(+), 423 deletions(-)
diff --git a/run_workflow_phaeoexplorer.py b/run_workflow_phaeoexplorer.py
index 753803f..36c4de9 100755
--- a/run_workflow_phaeoexplorer.py
+++ b/run_workflow_phaeoexplorer.py
@@ -115,7 +115,7 @@ class RunWorkflow(speciesData.SpeciesData):
"""
- logging.info("Connecting to the galaxy instance (%s)" % self.instance_url)
+ logging.debug("Connecting to the galaxy instance (%s)" % self.instance_url)
self.instance = galaxy.GalaxyInstance(url=self.instance_url,
email=self.config["galaxy_default_admin_email"],
password=self.config["galaxy_default_admin_password"]
@@ -128,54 +128,10 @@ class RunWorkflow(speciesData.SpeciesData):
logging.critical("Cannot connect to galaxy instance (%s) " % self.instance_url)
sys.exit()
else:
- logging.info("Successfully connected to galaxy instance (%s) " % self.instance_url)
+ logging.debug("Successfully connected to galaxy instance (%s) " % self.instance_url)
return 1
- def install_changesets_revisions_from_workflow(self, workflow_path):
- """
- Read a .ga file to extract the information about the different tools called.
- Check if every tool is installed via a "show_tool".
- If a tool is not installed (versions don't match), send a warning to the logger and install the required changeset (matching the tool version)
- Doesn't do anything if versions match
-
- :return:
- """
-
- logging.info("Validating that installed tools versions and changesets match workflow versions")
-
- # Load the workflow file (.ga) in a buffer
- with open(workflow_path, 'r') as ga_in_file:
-
- # Then store the decoded json dictionary
- workflow_dict = json.load(ga_in_file)
-
- # Look up every "step_id" looking for tools
- for k, v in workflow_dict["steps"].items():
- if v["tool_id"]:
-
- # Get the descriptive dictionary of the installed tool (using the tool id in the workflow)
- show_tool = self.instance.tools.show_tool(v["tool_id"])
-
- # Check if an installed version matches the workflow tool version
- # (If it's not installed, the show_tool version returned will be a default version with the suffix "XXXX+0")
- if show_tool["version"] != v["tool_version"]:
- # If it doesn't match, proceed to install of the correct changeset revision
- print(show_tool)
- # logging.warning("Tool versions don't match for {0} (changeset installed: {1} | changeset required: {2}). Installing changeset revision {3}...".format(v["tool_shed_repository"]["name"], show_tool["changeset_revision"], v["tool_shed_repository"]["changeset_revision"], v["tool_shed_repository"]["changeset_revision"]))
- toolshed = "https://" + v["tool_shed_repository"]["tool_shed"]
- name = v["tool_shed_repository"]["name"]
- owner = v["tool_shed_repository"]["owner"]
- changeset_revision = v["tool_shed_repository"]["changeset_revision"]
-
- self.instance.toolshed.install_repository_revision(tool_shed_url=toolshed, name=name, owner=owner,
- changeset_revision=changeset_revision,
- install_tool_dependencies=True,
- install_repository_dependencies=False,
- install_resolver_dependencies=True)
-
- logging.info("Tools versions and changesets from workflow validated")
-
def return_instance(self):
@@ -198,18 +154,18 @@ class RunWorkflow(speciesData.SpeciesData):
logging.info("Validating installed individual tools versions and changesets")
# Verify that the add_organism and add_analysis versions are correct in the toolshed
- add_organism_tool = self.instance.tools.show_tool("toolshed.g2.bx.psu.edu/repos/gga/chado_organism_add_organism/organism_add_organism/2.3.3")
- add_analysis_tool = self.instance.tools.show_tool("toolshed.g2.bx.psu.edu/repos/gga/chado_analysis_add_analysis/analysis_add_analysis/2.3.3")
- get_organism_tool = self.instance.tools.show_tool("toolshed.g2.bx.psu.edu/repos/gga/chado_organism_get_organisms/organism_get_organisms/2.3.3")
- get_analysis_tool = self.instance.tools.show_tool("toolshed.g2.bx.psu.edu/repos/gga/chado_analysis_get_analyses/analysis_get_analyses/2.3.3")
+ add_organism_tool = self.instance.tools.show_tool("toolshed.g2.bx.psu.edu/repos/gga/chado_organism_add_organism/organism_add_organism/2.3.4+galaxy0")
+ add_analysis_tool = self.instance.tools.show_tool("toolshed.g2.bx.psu.edu/repos/gga/chado_analysis_add_analysis/analysis_add_analysis/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_analysis_tool = self.instance.tools.show_tool("toolshed.g2.bx.psu.edu/repos/gga/chado_analysis_get_analyses/analysis_get_analyses/2.3.4+galaxy0")
- # changeset for 2.3.3 has to be manually found because there is no way to get the wanted changeset of a non installed tool via bioblend
+ # changeset for 2.3.4+galaxy0 has to be manually found because there is no way to get the wanted changeset of a non installed tool via bioblend
# except for workflows (.ga) that already contain the changeset revisions inside the steps ids
- if get_organism_tool["version"] != "2.3.3":
+ if get_organism_tool["version"] != "2.3.4+galaxy0":
toolshed_dict = get_organism_tool["tool_shed_repository"]
logging.warning("Changeset for %s is not installed" % toolshed_dict["name"])
- changeset_revision = "b07279b5f3bf"
+ changeset_revision = "831229e6cda2"
name = toolshed_dict["name"]
owner = toolshed_dict["owner"]
toolshed = "https://" + toolshed_dict["tool_shed"]
@@ -220,10 +176,10 @@ class RunWorkflow(speciesData.SpeciesData):
install_repository_dependencies=False,
install_resolver_dependencies=True)
- if get_analysis_tool["version"] != "2.3.3":
+ if get_analysis_tool["version"] != "2.3.4+galaxy0":
toolshed_dict = changeset_revision["tool_shed_repository"]
logging.warning("Changeset for %s is not installed" % toolshed_dict["name"])
- changeset_revision = "c7be2feafd73"
+ changeset_revision = "a867923f555e"
name = toolshed_dict["name"]
owner = toolshed_dict["owner"]
toolshed = "https://" + toolshed_dict["tool_shed"]
@@ -234,10 +190,10 @@ class RunWorkflow(speciesData.SpeciesData):
install_repository_dependencies=False,
install_resolver_dependencies=True)
- if add_organism_tool["version"] != "2.3.3":
+ if add_organism_tool["version"] != "2.3.4+galaxy0":
toolshed_dict = add_organism_tool["tool_shed_repository"]
logging.warning("Changeset for %s is not installed" % toolshed_dict["name"])
- changeset_revision = "680a1fe3c266"
+ changeset_revision = "1f12b9650028"
name = toolshed_dict["name"]
owner = toolshed_dict["owner"]
toolshed = "https://" + toolshed_dict["tool_shed"]
@@ -248,10 +204,10 @@ class RunWorkflow(speciesData.SpeciesData):
install_repository_dependencies=False,
install_resolver_dependencies=True)
- if add_analysis_tool["version"] != "2.3.3":
+ if add_analysis_tool["version"] != "2.3.4+galaxy0":
toolshed_dict = add_analysis_tool["tool_shed_repository"]
logging.warning("Changeset for %s is not installed" % toolshed_dict["name"])
- changeset_revision = "43c36801669f"
+ changeset_revision = "10b2b1c70e69"
name = toolshed_dict["name"]
owner = toolshed_dict["owner"]
toolshed = "https://" + toolshed_dict["tool_shed"]
@@ -290,8 +246,7 @@ class RunWorkflow(speciesData.SpeciesData):
self.connect_to_instance()
self.set_get_history()
- # We want the tools version default to be 2.3.3 at the moment
- tool_version = "2.3.3"
+ tool_version = "2.3.4+galaxy0"
# Add organism (species) to chado
logging.info("Adding organism to the instance's chado database")
if self.common == "" or self.common is None:
@@ -371,7 +326,7 @@ class RunWorkflow(speciesData.SpeciesData):
# Get the ID for the genome analysis in chado
genome_analysis = self.instance.tools.run_tool(
- tool_id="toolshed.g2.bx.psu.edu/repos/gga/chado_analysis_get_analyses/analysis_get_analyses/2.3.3",
+ tool_id="toolshed.g2.bx.psu.edu/repos/gga/chado_analysis_get_analyses/analysis_get_analyses/2.3.4+galaxy0",
history_id=self.history_id,
tool_inputs={"name": self.full_name_lowercase + " genome v" + self.genome_version})
genome_analysis_job_out = genome_analysis["outputs"][0]["id"]
@@ -391,7 +346,7 @@ class RunWorkflow(speciesData.SpeciesData):
# Get the ID for the OGS analysis in chado
ogs_analysis = self.instance.tools.run_tool(
- tool_id="toolshed.g2.bx.psu.edu/repos/gga/chado_analysis_get_analyses/analysis_get_analyses/2.3.3",
+ tool_id="toolshed.g2.bx.psu.edu/repos/gga/chado_analysis_get_analyses/analysis_get_analyses/2.3.4+galaxy0",
history_id=self.history_id,
tool_inputs={"name": self.full_name_lowercase + " OGS" + self.ogs_version})
ogs_analysis_job_out = ogs_analysis["outputs"][0]["id"]
@@ -413,7 +368,7 @@ class RunWorkflow(speciesData.SpeciesData):
# 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/2.3.3",
+ tool_id="toolshed.g2.bx.psu.edu/repos/gga/chado_analysis_add_analysis/analysis_add_analysis/2.3.4+galaxy0",
history_id=self.history_id,
tool_inputs={"name": "InterproScan on OGS%s" % self.ogs_version,
"program": "InterproScan",
@@ -428,7 +383,7 @@ class RunWorkflow(speciesData.SpeciesData):
# Get interpro ID
interpro_analysis = self.instance.tools.run_tool(
- tool_id="toolshed.g2.bx.psu.edu/repos/gga/chado_analysis_get_analyses/analysis_get_analyses/2.3.3",
+ tool_id="toolshed.g2.bx.psu.edu/repos/gga/chado_analysis_get_analyses/analysis_get_analyses/2.3.4+galaxy0",
history_id=self.history_id,
tool_inputs={"name": "InterproScan on OGS%s" % self.ogs_version})
interpro_analysis_job_out = interpro_analysis["outputs"][0]["id"]
@@ -565,7 +520,7 @@ class RunWorkflow(speciesData.SpeciesData):
return 0
- def import_datasets_into_history(self):
+ def import_datasets_into_history(self, imported_datasets_ids):
"""
Find datasets in a library, get their ID and import them into the current history if they are not already
@@ -589,7 +544,6 @@ class RunWorkflow(speciesData.SpeciesData):
for i in instance_source_data_folders:
folders_ids[i["name"]] = i["id"]
-
# Iterating over the folders to find datasets and map datasets to their IDs
logging.debug("Datasets IDs: ")
for k, v in folders_ids.items():
@@ -598,7 +552,7 @@ class RunWorkflow(speciesData.SpeciesData):
for k2, v2 in sub_folder_content.items():
for e in v2:
if type(e) == dict:
- if e["name"].endswith(".fa"):
+ if e["name"].endswith(".fasta"):
self.datasets["genome_file"] = e["ldda_id"]
self.datasets_name["genome_file"] = e["name"]
logging.debug("\tGenome file:\t" + e["name"] + ": " + e["ldda_id"])
@@ -632,15 +586,15 @@ class RunWorkflow(speciesData.SpeciesData):
logging.debug("Uploading datasets into history %s" % self.history_id)
# Import each dataset into history if it is not imported
- first_hda_ids = self.get_datasets_hda_ids()
+ first_hda_ids = self.get_datasets_hda_ids(imported_datasets_ids=imported_datasets_ids)
- if first_hda_ids["genome_hda_id"] is None:
+ if first_hda_ids["genome_hda_id"] is not None:
self.instance.histories.upload_dataset_from_library(history_id=self.history_id, lib_dataset_id=self.datasets["genome_file"])
- if first_hda_ids["gff_hda_id"] is None:
+ if first_hda_ids["gff_hda_id"] is not None:
self.instance.histories.upload_dataset_from_library(history_id=self.history_id, lib_dataset_id=self.datasets["gff_file"])
- if first_hda_ids["transcripts_hda_id"] is None:
+ if first_hda_ids["transcripts_hda_id"] is not None:
self.instance.histories.upload_dataset_from_library(history_id=self.history_id, lib_dataset_id=self.datasets["transcripts_file"])
- if first_hda_ids["proteins_hda_id"] is None:
+ if first_hda_ids["proteins_hda_id"] is not None:
self.instance.histories.upload_dataset_from_library(history_id=self.history_id, lib_dataset_id=self.datasets["proteins_file"])
if first_hda_ids["interproscan_hda_id"] is None:
try:
@@ -658,10 +612,10 @@ class RunWorkflow(speciesData.SpeciesData):
# datasets_ids_outfile.write(str(_datasets))
# Return a dict made of the hda ids
- return self.get_datasets_hda_ids()
+ return self.get_datasets_hda_ids(imported_datasets_ids=first_hda_ids["imported_datasets_ids"])
- def get_datasets_hda_ids(self):
+ def get_datasets_hda_ids(self, imported_datasets_ids):
"""
Get the hda IDs of the datasets imported into an history
@@ -684,33 +638,43 @@ class RunWorkflow(speciesData.SpeciesData):
# 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"]:
+ 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"]
- logging.debug("Genome dataset hda id: %s" % genome_dataset_hda_id)
- elif dataset_dict["name"] == self.datasets_name["proteins_file"]:
+ logging.debug("Genome dataset hda id: %s" % genome_dataset_hda_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"]
- logging.debug("Proteins dataset hda ID: %s" % proteins_datasets_hda_id)
- elif dataset_dict["name"] == self.datasets_name["transcripts_file"]:
+ logging.debug("Proteins dataset hda ID: %s" % proteins_datasets_hda_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"]
- logging.debug("Transcripts dataset hda ID: %s" % transcripts_dataset_hda_id)
- elif dataset_dict["name"] == self.datasets_name["gff_file"]:
+ logging.debug("Transcripts dataset hda ID: %s" % transcripts_dataset_hda_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"]
- logging.debug("GFF dataset hda ID: %s" % gff_dataset_hda_id)
+ logging.debug("GFF dataset hda ID: %s" % gff_dataset_hda_id)
if "interproscan_file" in self.datasets_name.keys():
- if dataset_dict["name"] == self.datasets_name["interproscan_file"]:
+ 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"]
- logging.debug("InterproScan dataset hda ID: %s" % gff_dataset_hda_id)
+ logging.debug("InterproScan dataset hda ID: %s" % gff_dataset_hda_id)
if "blast_diamond_file" in self.datasets_name.keys():
- if dataset_dict["name"] == self.datasets_name["blast_diamond_file"]:
+ if dataset_dict["name"] == self.datasets_name["blast_diamond_file"] and dataset_dict["id"] not in imported_datasets_ids:
blast_diamond_dataset_hda_id = dataset_dict["id"]
- logging.debug("Blast Diamond dataset hda ID: %s" % gff_dataset_hda_id)
+ logging.debug("Blast Diamond dataset hda ID: %s" % gff_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(blast_diamond_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,
- "blast_diamond_hda_id": blast_diamond_dataset_hda_id}
+ "blast_diamond_hda_id": blast_diamond_dataset_hda_id,
+ "imported_datasets_ids": imported_datasets_ids}
+
def get_organism_id(self):
"""
@@ -720,7 +684,7 @@ class RunWorkflow(speciesData.SpeciesData):
:return:
"""
- tool_version = "2.3.3"
+ tool_version = "2.3.4+galaxy0"
time.sleep(3)
# # Get the ID for the current organism in chado
@@ -736,7 +700,7 @@ class RunWorkflow(speciesData.SpeciesData):
# Run tool again (sometimes the tool doesn't return anything despite the organism already being in the db)
org = self.instance.tools.run_tool(
- tool_id="toolshed.g2.bx.psu.edu/repos/gga/chado_organism_get_organisms/organism_get_organisms/2.3.3",
+ tool_id="toolshed.g2.bx.psu.edu/repos/gga/chado_organism_get_organisms/organism_get_organisms/2.3.4+galaxy0",
history_id=self.history_id,
tool_inputs={"abbr": self.abbreviation,
"genus": self.genus_uppercase,
@@ -768,7 +732,7 @@ class RunWorkflow(speciesData.SpeciesData):
"common": self.common})
# Run tool again (sometimes the tool doesn't return anything despite the organism already being in the db)
org = self.instance.tools.run_tool(
- tool_id="toolshed.g2.bx.psu.edu/repos/gga/chado_organism_get_organisms/organism_get_organisms/2.3.3",
+ tool_id="toolshed.g2.bx.psu.edu/repos/gga/chado_organism_get_organisms/organism_get_organisms/2.3.4+galaxy0",
history_id=self.history_id,
tool_inputs={"abbr": self.abbreviation,
"genus": self.genus_uppercase,
@@ -787,6 +751,186 @@ class RunWorkflow(speciesData.SpeciesData):
return self.org_id
+def run_workflow(workflow_path, workflow_parameters, datamap, config, input_species_number):
+ """
+ Run a workflow in galaxy
+ Requires the .ga file to be loaded as a dictionary (optionally could be uploaded as a raw file)
+
+ :param workflow_name:
+ :param workflow_parameters:
+ :param datamap:
+ :return:
+ """
+
+ logging.info("Importing workflow %s" % str(workflow_path))
+
+ # Load the workflow file (.ga) in a buffer
+ with open(workflow_path, 'r') as ga_in_file:
+
+ # Then store the decoded json dictionary
+ workflow_dict = json.load(ga_in_file)
+
+ # In case of the Jbrowse workflow, we unfortunately have to manually edit the parameters instead of setting them
+ # as runtime values, using runtime parameters makes the tool throw an internal critical error ("replace not found" error)
+ # Scratchgmod test: need "http" (or "https"), the hostname (+ port)
+ if "menu_url" not in config.keys():
+ jbrowse_menu_url = "https://{hostname}/sp/{genus_sp}/feature/{Genus}/{species}/mRNA/{id}".format(hostname=self.config["hostname"], genus_sp=self.genus_species, Genus=self.genus_uppercase, species=self.species, id="{id}")
+ else:
+ jbrowse_menu_url = config["menu_url"]
+ if workflow_name == "Jbrowse":
+ workflow_dict["steps"]["2"]["tool_state"] = workflow_dict["steps"]["2"]["tool_state"].replace("__MENU_URL__", jbrowse_menu_url)
+ # The UNIQUE_ID is specific to a combination genus_species_strain_sex so every combination should have its unique workflow
+ # in galaxy --> define a naming method for these workflows
+ workflow_dict["steps"]["3"]["tool_state"] = workflow_dict["steps"]["3"]["tool_state"].replace("__FULL_NAME__", self.full_name).replace("__UNIQUE_ID__", self.species_folder_name)
+
+ # Import the workflow in galaxy as a dict
+ self.instance.workflows.import_workflow_dict(workflow_dict=workflow_dict)
+
+ # Get its attributes
+ workflow_attributes = self.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 = self.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 retrieving workflow attributes for workflow %s" % workflow_name)
+
+ # Finally, invoke the workflow alogn with its datamap, parameters and the history in which to invoke it
+ self.instance.workflows.invoke_workflow(workflow_id=workflow_id,
+ history_id=self.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, self.instance_url))
+
+
+
+
+
+
+def create_sp_workflow_dict(sp_dict, main_dir, config, imported_datasets_ids):
+ """
+ """
+
+ sp_workflow_dict = {}
+ run_workflow_for_current_organism = RunWorkflow(parameters_dictionary=sp_dict)
+
+ # Verifying the galaxy container is running
+ if utilities.check_galaxy_state(genus_lowercase=run_workflow_for_current_organism.genus_lowercase,
+ species=run_workflow_for_current_organism.species,
+ script_dir=run_workflow_for_current_organism.script_dir):
+
+ # Starting
+ logging.info("run_workflow.py called for %s" % run_workflow_for_current_organism.full_name)
+
+ # Setting some of the instance attributes
+ run_workflow_for_current_organism.main_dir = main_dir
+ run_workflow_for_current_organism.species_dir = os.path.join(run_workflow_for_current_organism.main_dir,
+ run_workflow_for_current_organism.genus_species +
+ "/")
+
+ # Parse the config yaml file
+ run_workflow_for_current_organism.config = config
+ # Set the instance url attribute --> TODO: the localhost rule in the docker-compose still doesn't work on scratchgmodv1
+ run_workflow_for_current_organism.instance_url = "http://localhost:{0}/sp/{1}_{2}/galaxy/".format(
+ run_workflow_for_current_organism.config["http_port"],
+ run_workflow_for_current_organism.genus_lowercase,
+ run_workflow_for_current_organism.species)
+
+
+ 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()
+ run_workflow_for_current_organism.add_organism_ogs_genome_analyses()
+
+ org_id = run_workflow_for_current_organism.get_organism_id()
+ genome_analysis_id = run_workflow_for_current_organism.get_genome_analysis_id()
+ ogs_analysis_id = run_workflow_for_current_organism.get_ogs_analysis_id()
+ instance_attributes = run_workflow_for_current_organism.get_instance_attributes()
+ hda_ids = run_workflow_for_current_organism.import_datasets_into_history(imported_datasets_ids=imported_datasets_ids)
+
+ 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,
+ "genome_analysis_id": genome_analysis_id,
+ "ogs_analysis_id": ogs_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()
+
+
+ return sp_workflow_dict
+
+
+def install_changesets_revisions_from_workflow(instance, workflow_path):
+ """
+ Read a .ga file to extract the information about the different tools called.
+ Check if every tool is installed via a "show_tool".
+ If a tool is not installed (versions don't match), send a warning to the logger and install the required changeset (matching the tool version)
+ Doesn't do anything if versions match
+
+ :return:
+ """
+
+ logging.info("Validating that installed tools versions and changesets match workflow versions")
+
+ # Load the workflow file (.ga) in a buffer
+ with open(workflow_path, 'r') as ga_in_file:
+
+ # Then store the decoded json dictionary
+ workflow_dict = json.load(ga_in_file)
+
+ # Look up every "step_id" looking for tools
+ for k, v in workflow_dict["steps"].items():
+ if v["tool_id"]:
+ # Get the descriptive dictionary of the installed tool (using the tool id in the workflow)
+ show_tool = instance.tools.show_tool(v["tool_id"])
+ # Check if an installed version matches the workflow tool version
+ # (If it's not installed, the show_tool version returned will be a default version with the suffix "XXXX+0")
+ if show_tool["version"] != v["tool_version"]:
+ # If it doesn't match, proceed to install of the correct changeset revision
+ # logging.warning("Tool versions don't match for {0} (changeset installed: {1} | changeset required: {2}). Installing changeset revision {3}...".format(v["tool_shed_repository"]["name"], show_tool["changeset_revision"], v["tool_shed_repository"]["changeset_revision"], v["tool_shed_repository"]["changeset_revision"]))
+ toolshed = "https://" + v["tool_shed_repository"]["tool_shed"]
+ name = v["tool_shed_repository"]["name"]
+ owner = v["tool_shed_repository"]["owner"]
+ changeset_revision = v["tool_shed_repository"]["changeset_revision"]
+
+ logging.debug("Installing changeset {0} for tool {1}".format(changeset_revision, name))
+
+ # Install changeset
+ instance.toolshed.install_repository_revision(tool_shed_url=toolshed, name=name, owner=owner,
+ changeset_revision=changeset_revision,
+ install_tool_dependencies=True,
+ install_repository_dependencies=False,
+ install_resolver_dependencies=True)
+
+ logging.info("Tools versions and changesets from workflow validated")
+
+
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Automatic data loading in containers and interaction "
"with galaxy instances for GGA"
@@ -835,365 +979,554 @@ if __name__ == "__main__":
sp_dict_list = utilities.parse_input(args.input)
+ # # Checking if user specified a workflow to run
+ # if not args.workflow:
+ # logging.critical("No workflow specified, exiting")
+ # sys.exit()
+ # else:
+ # workflow = os.path.abspath(args.workflow)
+
- org_ids = []
- genome_analysis_ids = []
- ogs_analysis_ids = []
- hda_ids_list = []
- instance_attributes_list = []
+ script_dir = os.path.dirname(os.path.realpath(sys.argv[0]))
+ config = utilities.parse_config(args.config)
+ all_sp_workflow_dict = {}
- instance_url_2org = None
+ # IDs of already imported datasets (useful in case there are several species within the same instance)
+ # TODO: Not a very smart way to filter datasets as the list will grow at every input species, whereas
+ # we only need to do the ID lookup for a single galaxy instance --> Possible issue where we encounter 2 identical IDs in
+ # different instances
+ imported_datasets_ids = []
for sp_dict in sp_dict_list:
- # Creating an instance of the RunWorkflow object for the current organism
- run_workflow_for_current_organism = RunWorkflow(parameters_dictionary=sp_dict)
+ # 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, imported_datasets_ids=imported_datasets_ids)
- # Checking if user specified a workflow to run
- if not args.workflow:
- logging.critical("No workflow specified, exiting")
- sys.exit()
+ 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:
- workflow = os.path.abspath(args.workflow)
+ all_sp_workflow_dict[current_sp_key][current_sp_strain_sex_key] = current_sp_strain_sex_value
+
+
+ for k, v in all_sp_workflow_dict.items():
+ 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/Galaxy-Workflow-chado_load_tripal_synchronize_jbrowse_1org_v2.ga")
+
+ # 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)
+
+ # Set datamap (mapping of input files in the workflow)
+ datamap = {}
+
+
+ # Set the workflow parameters (individual tools runtime parameters in the workflow)
+ workflow_parameters = {}
+
+ 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/Galaxy-Workflow-chado_load_tripal_synchronize_jbrowse_2org_v2.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)
+
+ # Get key names from the current organism (item 1 = organism 1, item 2 = organism 2)
+ 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_genome_analysis_id = org1_dict["genome_analysis_id"]
+ org1_ogs_analysis_id = org1_dict["ogs_analysis_id"]
+ org1_genome_hda_id = org1_dict["hda_ids"]["genome_hda_id"]
+ org1_transcripts_hda_id = org1_dict["hda_ids"]["transcripts_hda_id"]
+ org1_proteins_hda_id = org1_dict["hda_ids"]["proteins_hda_id"]
+ org1_gff_hda_id = org1_dict["hda_ids"]["gff_hda_id"]
+
+ # 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_genome_analysis_id = org2_dict["genome_analysis_id"]
+ org2_ogs_analysis_id = org2_dict["ogs_analysis_id"]
+ org2_genome_hda_id = org2_dict["hda_ids"]["genome_hda_id"]
+ org2_transcripts_hda_id = org2_dict["hda_ids"]["transcripts_hda_id"]
+ org2_proteins_hda_id = org2_dict["hda_ids"]["proteins_hda_id"]
+ org2_gff_hda_id = org2_dict["hda_ids"]["gff_hda_id"]
+
+
+ # Source files association (ordered by their IDs in the workflow)
+ GENOME_FASTA_FILE_ORG1 = "0"
+ GFF_FILE_ORG1 = "1"
+ PROTEINS_FASTA_FILE_ORG1 = "2"
+
+ GENOME_FASTA_FILE_ORG2 = "3"
+ GFF_FILE_ORG2 = "4"
+ PROTEINS_FASTA_FILE_ORG2 = "5"
+
+ LOAD_FASTA_ORG1 = "6"
+ JBROWSE_ORG1 = "7"
+ JRBOWSE_ORG2 = "8"
+
+ LOAD_GFF_ORG1 = "9"
+ JBROWSE_CONTAINER = "10"
+ SYNC_FEATURES_ORG1 = "11"
+
+ LOAD_FASTA_ORG2 = "12"
+ LOAD_GFF_ORG2 = "13"
+
+ SYNC_FEATURES_ORG2 = "14"
+ POPULATE_MAT_VIEWS = "15"
+ INDEX_TRIPAL_DATA = "16"
+
+ # 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[GENOME_FASTA_FILE_ORG1] = {}
+ workflow_parameters[GFF_FILE_ORG1] = {}
+ workflow_parameters[PROTEINS_FASTA_FILE_ORG1] = {}
+ workflow_parameters[GENOME_FASTA_FILE_ORG2] = {}
+ workflow_parameters[GFF_FILE_ORG2] = {}
+ workflow_parameters[PROTEINS_FASTA_FILE_ORG2] = {}
+
+ # Organism 1
+ workflow_parameters[LOAD_FASTA_ORG1] = {"organism": org1_org_id,
+ "analysis_id": org1_genome_analysis_id,
+ "do_update": "true"}
+ workflow_parameters[JBROWSE_ORG1] = {}
+ workflow_parameters[LOAD_GFF_ORG1] = {"organism": org1_org_id, "analysis_id": org1_ogs_analysis_id}
+ workflow_parameters[SYNC_FEATURES_ORG1] = {"organism_id": org1_org_id}
+ workflow_parameters[JBROWSE_CONTAINER] = {}
+
+
+ # Organism 2
+ workflow_parameters[LOAD_FASTA_ORG2] = {"organism": org2_org_id,
+ "analysis_id": org2_genome_analysis_id,
+ "do_update": "true"}
+ workflow_parameters[LOAD_GFF_ORG2] = {"organism": org2_org_id, "analysis_id": org2_ogs_analysis_id}
+ workflow_parameters[JRBOWSE_ORG2] = {}
+ workflow_parameters[SYNC_FEATURES_ORG2] = {"organism_id": org2_org_id}
+
+
+ # POPULATE + INDEX DATA
+ workflow_parameters[POPULATE_MAT_VIEWS] = {}
+ workflow_parameters[INDEX_TRIPAL_DATA] = {}
+
+
+ # Set datamap (mapping of input files in the workflow)
+ datamap = {}
+
+ # Organism 1
+ datamap[GENOME_FASTA_FILE_ORG1] = {"src": "hda", "id": org1_genome_hda_id}
+ datamap[GFF_FILE_ORG1] = {"src": "hda", "id": org1_gff_hda_id}
+ datamap[PROTEINS_FASTA_FILE_ORG1] = {"src": "hda", "id": org1_proteins_hda_id}
+
+ # Organism 2
+ datamap[GENOME_FASTA_FILE_ORG2] = {"src": "hda", "id": org2_genome_hda_id}
+ datamap[GFF_FILE_ORG2] = {"src": "hda", "id": org2_gff_hda_id}
+ datamap[PROTEINS_FASTA_FILE_ORG2] = {"src": "hda", "id": org2_proteins_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"]
+
+ # For the Jbrowse tool, we unfortunately have to manually edit the parameters instead of setting them
+ # as runtime values, using runtime parameters makes the tool throw an internal critical error ("replace not found" error)
+ # Scratchgmod test: need "http" (or "https"), the hostname (+ port)
+ if "menu_url" not in config.keys():
+ jbrowse_menu_url_org1 = "https://{hostname}/sp/{genus_sp}/feature/{Genus}/{species}/mRNA/{id}".format(hostname=config["hostname"], genus_sp=org1_genus_species, Genus=org1_genus[0].upper() + org1_genus[1:], species=org1_species, id="\{id\}")
+ jbrowse_menu_url_org2 = "https://{hostname}/sp/{genus_sp}/feature/{Genus}/{species}/mRNA/{id}".format(hostname=config["hostname"], genus_sp=org2_genus_species, Genus=org2_genus[0].upper() + org2_genus[1:], species=org2_species, id="\{id\}")
+ else:
+ jbrowse_menu_url_org1 = config["menu_url"]
+ jbrowse_menu_url_org2 = jbrowse_menu_url_org1
+
+ # Replace values in the workflow dictionary
+ workflow_dict["steps"]["7"]["tool_state"] = workflow_dict["steps"]["7"]["tool_state"].replace("__MENU_URL_ORG1__", jbrowse_menu_url_org1)
+ workflow_dict["steps"]["8"]["tool_state"] = workflow_dict["steps"]["8"]["tool_state"].replace("__MENU_URL_ORG2__", jbrowse_menu_url_org2)
+ # The UNIQUE_ID is specific to a combination genus_species_strain_sex so every combination should have its unique workflow
+ # in galaxy --> define a naming method for these workflows
+ workflow_dict["steps"]["10"]["tool_state"] = workflow_dict["steps"]["10"]["tool_state"].replace("__FULL_NAME_ORG1__", org1_full_name).replace("__UNIQUE_ID_ORG1__", org1_species_folder_name)
+ workflow_dict["steps"]["10"]["tool_state"] = workflow_dict["steps"]["10"]["tool_state"].replace("__FULL_NAME_ORG2__", org2_full_name).replace("__UNIQUE_ID_ORG2__", org2_species_folder_name)
- # Verifying the galaxy container is running
- if utilities.check_galaxy_state(genus_lowercase=run_workflow_for_current_organism.genus_lowercase,
- species=run_workflow_for_current_organism.species,
- script_dir=run_workflow_for_current_organism.script_dir):
+ # Import the workflow in galaxy as a dict
+ instance.workflows.import_workflow_dict(workflow_dict=workflow_dict)
- # Starting
- logging.info("run_workflow.py called for %s" % run_workflow_for_current_organism.full_name)
+ # 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)
- # Setting some of the instance attributes
- run_workflow_for_current_organism.main_dir = args.main_directory
- run_workflow_for_current_organism.species_dir = os.path.join(run_workflow_for_current_organism.main_dir,
- run_workflow_for_current_organism.genus_species +
- "/")
+ # 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)
- # Parse the config yaml file
- run_workflow_for_current_organism.config = utilities.parse_config(args.config)
- # Set the instance url attribute --> TODO: the localhost rule in the docker-compose still doesn't work on scratchgmodv1
- run_workflow_for_current_organism.instance_url = "http://localhost:{0}/sp/{1}_{2}/galaxy/".format(
- run_workflow_for_current_organism.config["http_port"],
- run_workflow_for_current_organism.genus_lowercase,
- run_workflow_for_current_organism.species)
+ logging.info("Successfully imported and invoked workflow {0}, check the galaxy instance ({1}) for the jobs state".format(workflow_name, instance_url))
- instance_url_2org = run_workflow_for_current_organism.instance_url
# Get the instance attribute from the object for future connections
- instance = run_workflow_for_current_organism.instance
+ # This is the GalaxyInstance object from bioblend (not the url!)
+ # instance = run_workflow_for_current_organism.instance
- if "2org" in str(workflow):
- logging.info("Executing workflow %s" % workflow)
+ # if "2org" in str(workflow):
+ # logging.info("Executing workflow %s" % workflow)
- run_workflow_for_current_organism.connect_to_instance()
- run_workflow_for_current_organism.set_get_history()
- run_workflow_for_current_organism.install_changesets_revisions_for_individual_tools()
- run_workflow_for_current_organism.install_changesets_revisions_from_workflow(workflow_path=workflow)
- run_workflow_for_current_organism.add_organism_ogs_genome_analyses()
- org_id = run_workflow_for_current_organism.get_organism_id()
- org_ids.append(org_id)
- genome_analysis_id = run_workflow_for_current_organism.get_genome_analysis_id()
- genome_analysis_ids.append(genome_analysis_id)
- ogs_analysis_id = run_workflow_for_current_organism.get_ogs_analysis_id()
- ogs_analysis_ids.append(ogs_analysis_id)
- instance_attributes = run_workflow_for_current_organism.get_instance_attributes()
- instance_attributes_list.append(instance_attributes)
+ # run_workflow_for_current_organism.connect_to_instance()
+ # run_workflow_for_current_organism.set_get_history()
+ # # TODO: only do this once per instance (not at each iteration!)
+ # run_workflow_for_current_organism.install_changesets_revisions_for_individual_tools()
+ # run_workflow_for_current_organism.install_changesets_revisions_from_workflow(workflow_path=workflow)
+ # run_workflow_for_current_organism.add_organism_ogs_genome_analyses()
+ # org_id = run_workflow_for_current_organism.get_organism_id()
+ # genome_analysis_id = run_workflow_for_current_organism.get_genome_analysis_id()
+ # ogs_analysis_id = run_workflow_for_current_organism.get_ogs_analysis_id()
+ # instance_attributes = run_workflow_for_current_organism.get_instance_attributes()
- # Import datasets into history and retrieve their hda IDs
- # TODO: can be simplified with direct access to the folder contents via the full path (no loop required)
- hda_ids = run_workflow_for_current_organism.import_datasets_into_history()
- hda_ids_list.append(hda_ids)
+ # # Import datasets into history and retrieve their hda IDs
+ # # TODO: can be simplified with direct access to the folder contents via the full path (no loop required)
+ # hda_ids = run_workflow_for_current_organism.import_datasets_into_history()
+ # hda_ids_list.append(hda_ids)
- # TODO: Exlcude the workflow invocation from the loop
- # Extract instance url from one, attributes from both in lists ?
+ # # TODO: Exlcude the workflow invocation from the loop
+ # # Extract instance url from one, attributes from both in lists ?
- # Source files association (ordered by their IDs in the workflow)
- GENOME_FASTA_FILE_ORG1 = "0"
- GFF_FILE_ORG1 = "1"
- PROTEINS_FASTA_FILE_ORG1 = "2"
- GENOME_FASTA_FILE_ORG2 = "3"
- GFF_FILE_ORG2 = "4"
- PROTEINS_FASTA_FILE_ORG2 = "5"
- LOAD_FASTA_ORG1 = "6"
- JBROWSE_ORG1 = "7"
- JRBOWSE_ORG2 = "8"
+ # # Source files association (ordered by their IDs in the workflow)
+ # GENOME_FASTA_FILE_ORG1 = "0"
+ # GFF_FILE_ORG1 = "1"
+ # PROTEINS_FASTA_FILE_ORG1 = "2"
+ # GENOME_FASTA_FILE_ORG2 = "3"
+ # GFF_FILE_ORG2 = "4"
+ # PROTEINS_FASTA_FILE_ORG2 = "5"
- LOAD_GFF_ORG1 = "9"
- JBROWSE_CONTAINER = "10"
- SYNC_FEATURES_ORG1 = "11"
+ # LOAD_FASTA_ORG1 = "6"
+ # JBROWSE_ORG1 = "7"
+ # JRBOWSE_ORG2 = "8"
- LOAD_FASTA_ORG2 = "12"
- LOAD_GFF_ORG2 = "13"
+ # LOAD_GFF_ORG1 = "9"
+ # JBROWSE_CONTAINER = "10"
+ # SYNC_FEATURES_ORG1 = "11"
- SYNC_FEATURES_ORG2 = "14"
- POPULATE_MAT_VIEWS = "15"
- INDEX_TRIPAL_DATA = "16"
+ # LOAD_FASTA_ORG2 = "12"
+ # LOAD_GFF_ORG2 = "13"
- workflow_parameters = {}
+ # SYNC_FEATURES_ORG2 = "14"
+ # POPULATE_MAT_VIEWS = "15"
+ # INDEX_TRIPAL_DATA = "16"
- workflow_parameters[GENOME_FASTA_FILE_ORG1] = {}
- workflow_parameters[GFF_FILE_ORG1] = {}
- workflow_parameters[PROTEINS_FASTA_FILE_ORG1] = {}
- workflow_parameters[GENOME_FASTA_FILE_ORG2] = {}
- workflow_parameters[GFF_FILE_ORG2] = {}
- workflow_parameters[PROTEINS_FASTA_FILE_ORG2] = {}
+ # workflow_parameters = {}
-
- # ORGANISM 1
- workflow_parameters[LOAD_FASTA_ORG1] = {"organism": org_ids[0],
- "analysis_id": genome_analysis_ids[0],
- "do_update": "true"}
- # Change "do_update": "true" to "do_update": "false" in above parameters to prevent appending/updates to the fasta file in chado
- # WARNING: It is safer to never update it and just change the genome/ogs versions in the config
- workflow_parameters[JBROWSE_ORG1] = {}
- workflow_parameters[LOAD_GFF_ORG1] = {"organism": org_ids[0], "analysis_id": ogs_analysis_ids[0]}
- workflow_parameters[SYNC_FEATURES_ORG1] = {"organism_id": org_ids[0]}
- workflow_parameters[JBROWSE_CONTAINER] = {}
-
-
- # ORGANISM 2
- workflow_parameters[LOAD_FASTA_ORG2] = {"organism": org_ids[1],
- "analysis_id": genome_analysis_ids[1],
- "do_update": "true"}
- # Change "do_update": "true" to "do_update": "false" in above parameters to prevent appending/updates to the fasta file in chado
- # WARNING: It is safer to never update it and just change the genome/ogs versions in the config
- workflow_parameters[LOAD_GFF_ORG2] = {"organism": org_ids[1], "analysis_id": ogs_analysis_ids[1]}
- workflow_parameters[JRBOWSE_ORG2] = {}
- workflow_parameters[SYNC_FEATURES_ORG2] = {"organism_id": org_ids[1]}
-
- workflow_parameters[SYNC_GENOME_ANALYSIS_INTO_TRIPAL] = {"analysis_id": ogs_analysis_ids[0]}
- workflow_parameters[SYNC_OGS_ANALYSIS_INTO_TRIPAL] = {"analysis_id": genome_analysis_ids[0]}
- workflow_parameters[SYNC_FEATURES_INTO_TRIPAL] = {"organism_id": org_ids[0]}
-
- # POPULATE + INDEX DATA
- workflow_parameters[POPULATE_MAT_VIEWS] = {}
- workflow_parameters[INDEX_TRIPAL_DATA] = {}
-
- # Datamap for input datasets - dataset source (type): ldda (LibraryDatasetDatasetAssociation)
- run_workflow_for_current_organism.datamap = {}
-
- run_workflow_for_current_organism.datamap[GENOME_FASTA_FILE_ORG1] = {"src": "hda", "id": hda_ids_list[0]["genome_hda_id"]}
- run_workflow_for_current_organism.datamap[GFF_FILE_ORG1] = {"src": "hda", "id": hda_ids_list[0]["gff_hda_id"]}
- run_workflow_for_current_organism.datamap[PROTEINS_FASTA_FILE_ORG1] = {"src": "hda", "id": hda_ids_list[0]["proteins_hda_id"]}
-
- run_workflow_for_current_organism.datamap[GENOME_FASTA_FILE_ORG2] = {"src": "hda", "id": hda_ids_list[1]["genome_hda_id"]}
- run_workflow_for_current_organism.datamap[GFF_FILE_ORG2] = {"src": "hda", "id": hda_ids_list[1]["gff_hda_id"]}
- run_workflow_for_current_organism.datamap[GFF_FILE_ORG2] = {"src": "hda", "id": hda_ids_list[1]["proteins_hda_id"]}
-
-
-
- logging.info("OK: Workflow invoked")
-
-
-
- # If input workflow is Chado_load_Tripal_synchronize.ga
- if "Chado_load_Tripal_synchronize" in str(workflow):
-
- logging.info("Executing workflow 'Chado_load_Tripal_synchronize'")
-
- run_workflow_for_current_organism.connect_to_instance()
- run_workflow_for_current_organism.set_get_history()
- # run_workflow_for_current_organism.get_species_history_id()
-
- run_workflow_for_current_organism.install_changesets_revisions_for_individual_tools()
- run_workflow_for_current_organism.install_changesets_revisions_from_workflow(workflow_path=workflow)
- run_workflow_for_current_organism.add_organism_ogs_genome_analyses()
- run_workflow_for_current_organism.get_organism_id()
- run_workflow_for_current_organism.get_genome_analysis_id()
- run_workflow_for_current_organism.get_ogs_analysis_id()
-
- # run_workflow_for_current_organism.tripal_synchronize_organism_analyses()
-
- # Get the attributes of the instance and project data files
- run_workflow_for_current_organism.get_instance_attributes()
-
- # Import datasets into history and retrieve their hda IDs
- # TODO: can be simplified with direct access to the folder contents via the full path (no loop required)
- hda_ids = run_workflow_for_current_organism.import_datasets_into_history()
-
- # DEBUG
- # run_workflow_for_current_organism.get_invocation_report(workflow_name="Chado load Tripal synchronize")
-
- # Explicit workflow parameter names
- GENOME_FASTA_FILE = "0"
- GFF_FILE = "1"
- PROTEINS_FASTA_FILE = "2"
- TRANSCRIPTS_FASTA_FILE = "3"
-
- LOAD_FASTA_IN_CHADO = "4"
- LOAD_GFF_IN_CHADO = "5"
- SYNC_ORGANISM_INTO_TRIPAL = "6"
- SYNC_GENOME_ANALYSIS_INTO_TRIPAL = "7"
- SYNC_OGS_ANALYSIS_INTO_TRIPAL = "8"
- SYNC_FEATURES_INTO_TRIPAL = "9"
-
- workflow_parameters = {}
-
- workflow_parameters[GENOME_FASTA_FILE] = {}
- workflow_parameters[GFF_FILE] = {}
- workflow_parameters[PROTEINS_FASTA_FILE] = {}
- workflow_parameters[TRANSCRIPTS_FASTA_FILE] = {}
-
- workflow_parameters[LOAD_FASTA_IN_CHADO] = {"organism": run_workflow_for_current_organism.org_id,
- "analysis_id": run_workflow_for_current_organism.genome_analysis_id,
- "do_update": "true"}
- # Change "do_update": "true" to "do_update": "false" in above parameters to prevent appending/updates to the fasta file in chado
- # WARNING: It is safer to never update it and just change the genome/ogs versions in the config
- workflow_parameters[LOAD_GFF_IN_CHADO] = {"organism": run_workflow_for_current_organism.org_id,
- "analysis_id": run_workflow_for_current_organism.ogs_analysis_id}
- workflow_parameters[SYNC_ORGANISM_INTO_TRIPAL] = {"organism_id": run_workflow_for_current_organism.org_id}
- workflow_parameters[SYNC_GENOME_ANALYSIS_INTO_TRIPAL] = {"analysis_id": run_workflow_for_current_organism.ogs_analysis_id}
- workflow_parameters[SYNC_OGS_ANALYSIS_INTO_TRIPAL] = {"analysis_id": run_workflow_for_current_organism.genome_analysis_id}
- workflow_parameters[SYNC_FEATURES_INTO_TRIPAL] = {"organism_id": run_workflow_for_current_organism.org_id}
-
- # Datamap for input datasets - dataset source (type): ldda (LibraryDatasetDatasetAssociation)
- run_workflow_for_current_organism.datamap = {}
- run_workflow_for_current_organism.datamap[GENOME_FASTA_FILE] = {"src": "hda", "id": hda_ids["genome_hda_id"]}
- run_workflow_for_current_organism.datamap[GFF_FILE] = {"src": "hda", "id": hda_ids["gff_hda_id"]}
- run_workflow_for_current_organism.datamap[PROTEINS_FASTA_FILE] = {"src": "hda", "id": hda_ids["proteins_hda_id"]}
- run_workflow_for_current_organism.datamap[TRANSCRIPTS_FASTA_FILE] = {"src": "hda", "id": hda_ids["transcripts_hda_id"]}
-
- # run_workflow_for_current_organism.datamap = {}
- # run_workflow_for_current_organism.datamap[GENOME_FASTA_FILE] = {"src": "hda", "id":
- # run_workflow_for_current_organism.datasets["genome_file"]}
- # run_workflow_for_current_organism.datamap[GFF_FILE] = {"src": "hda",
- # "id": hda_ids["gff_hda_id"]}
-
- # Ensures galaxy has had time to retrieve datasets
- time.sleep(60)
- # Run the Chado load Tripal sync workflow with the parameters set above
- run_workflow_for_current_organism.run_workflow(workflow_path=workflow,
- workflow_parameters=workflow_parameters,
- datamap=run_workflow_for_current_organism.datamap,
- workflow_name="Chado load Tripal synchronize")
-
- # Jbrowse creation workflow
- elif "Jbrowse" in str(workflow):
-
- logging.info("Executing workflow 'Jbrowse'")
-
- run_workflow_for_current_organism.connect_to_instance()
- run_workflow_for_current_organism.set_get_history()
- run_workflow_for_current_organism.install_changesets_revisions_from_workflow(workflow_path=workflow)
- run_workflow_for_current_organism.get_organism_id()
- # Import datasets into history and get their hda IDs
- run_workflow_for_current_organism.import_datasets_into_history()
- hda_ids = run_workflow_for_current_organism.get_datasets_hda_ids() # Note: only call this function AFTER calling "import_datasets_into_history()"
-
- # Debugging
- # run_workflow_for_current_organism.get_invocation_report(workflow_name="Jbrowse")
-
- GENOME_FASTA_FILE = "0"
- GFF_FILE = "1"
- ADD_JBROWSE = "2"
- ADD_ORGANISM_TO_JBROWSE = "3"
-
- workflow_parameters = {}
- workflow_parameters[GENOME_FASTA_FILE] = {}
- workflow_parameters[GFF_FILE] = {}
- workflow_parameters[ADD_JBROWSE] = {}
- workflow_parameters[ADD_ORGANISM_TO_JBROWSE] = {}
-
- run_workflow_for_current_organism.datamap = {}
- run_workflow_for_current_organism.datamap[GENOME_FASTA_FILE] = {"src": "hda", "id": hda_ids["genome_hda_id"]}
- run_workflow_for_current_organism.datamap[GFF_FILE] = {"src": "hda", "id": hda_ids["gff_hda_id"]}
-
- # Run the jbrowse creation workflow
- run_workflow_for_current_organism.run_workflow(workflow_path=workflow,
- workflow_parameters=workflow_parameters,
- datamap=run_workflow_for_current_organism.datamap,
- workflow_name="Jbrowse")
-
- elif "Interpro" in str(workflow):
-
- logging.info("Executing workflow 'Interproscan")
-
- run_workflow_for_current_organism.connect_to_instance()
- run_workflow_for_current_organism.set_get_history()
- run_workflow_for_current_organism.install_changesets_revisions_from_workflow(workflow_path=workflow)
- # run_workflow_for_current_organism.get_species_history_id()
-
- # Get the attributes of the instance and project data files
- run_workflow_for_current_organism.get_instance_attributes()
- run_workflow.add_interproscan_analysis()
- run_workflow_for_current_organism.get_interpro_analysis_id()
-
- # Import datasets into history and retrieve their hda IDs
- run_workflow_for_current_organism.import_datasets_into_history()
- hda_ids = run_workflow_for_current_organism.get_datasets_hda_ids()
-
- INTERPRO_FILE = "0"
- LOAD_INTERPRO_IN_CHADO = "1"
- SYNC_INTERPRO_ANALYSIS_INTO_TRIPAL = "2"
- SYNC_FEATURES_INTO_TRIPAL = "3"
- POPULATE_MAT_VIEWS = "4"
- INDEX_TRIPAL_DATA = "5"
-
- workflow_parameters = {}
- workflow_parameters[INTERPRO_FILE] = {}
- workflow_parameters[LOAD_INTERPRO_IN_CHADO] = {"organism": run_workflow_for_current_organism.org_id,
- "analysis_id": run_workflow_for_current_organism.interpro_analysis_id}
- workflow_parameters[SYNC_INTERPRO_ANALYSIS_INTO_TRIPAL] = {"analysis_id": run_workflow_for_current_organism.interpro_analysis_id}
-
-
- run_workflow_for_current_organism.datamap = {}
- run_workflow_for_current_organism.datamap[INTERPRO_FILE] = {"src": "hda", "id": run_workflow_for_current_organism.hda_ids["interproscan_hda_id"]}
-
- # Run Interproscan workflow
- run_workflow_for_current_organism.run_workflow(workflow_path=workflow,
- workflow_parameters=workflow_parameters,
- datamap=run_workflow_for_current_organism.datamap,
- workflow_name="Interproscan")
-
- elif "Blast" in str(workflow):
-
- logging.info("Executing workflow 'Blast_Diamond")
-
- run_workflow_for_current_organism.connect_to_instance()
- run_workflow_for_current_organism.set_get_history()
- run_workflow_for_current_organism.install_changesets_revisions_from_workflow(workflow_path=workflow)
- # run_workflow_for_current_organism.get_species_history_id()
-
- # Get the attributes of the instance and project data files
- run_workflow_for_current_organism.get_instance_attributes()
- run_workflow_for_current_organism.add_blastp_diamond_analysis()
- run_workflow_for_current_organism.get_blastp_diamond_analysis_id()
-
- # Import datasets into history and retrieve their hda IDs
- run_workflow_for_current_organism.import_datasets_into_history()
- hda_ids = run_workflow_for_current_organism.get_datasets_hda_ids()
-
- BLAST_FILE = "0"
- LOAD_BLAST_IN_CHADO = "1"
- SYNC_BLAST_ANALYSIS_INTO_TRIPAL = "2"
- SYNC_FEATURES_INTO_TRIPAL = "3"
- POPULATE_MAT_VIEWS = "4"
- INDEX_TRIPAL_DATA = "5"
-
- workflow_parameters = {}
- workflow_parameters[INTERPRO_FILE] = {}
- workflow_parameters[LOAD_BLAST_IN_CHADO] = {"organism": run_workflow_for_current_organism.org_id,
- "analysis_id": run_workflow_for_current_organism.blast_diamond_analysis_id}
- workflow_parameters[SYNC_BLAST_ANALYSIS_INTO_TRIPAL] = {"analysis_id": run_workflow_for_current_organism.blast_diamond_analysis_id}
-
- run_workflow_for_current_organism.datamap = {}
- run_workflow_for_current_organism.datamap[INTERPRO_FILE] = {"src": "hda", "id": hda_ids["interproscan_hda_id"]}
-
- # Run Interproscan workflow
- run_workflow_for_current_organism.run_workflow(workflow_path=workflow,
- workflow_parameters=workflow_parameters,
- datamap=run_workflow_for_current_organism.datamap,
- workflow_name="Interproscan")
+ # workflow_parameters[GENOME_FASTA_FILE_ORG1] = {}
+ # workflow_parameters[GFF_FILE_ORG1] = {}
+ # workflow_parameters[PROTEINS_FASTA_FILE_ORG1] = {}
+ # workflow_parameters[GENOME_FASTA_FILE_ORG2] = {}
+ # workflow_parameters[GFF_FILE_ORG2] = {}
+ # workflow_parameters[PROTEINS_FASTA_FILE_ORG2] = {}
-
- else:
- logging.critical("The galaxy container for %s is not ready yet!" % run_workflow_for_current_organism.full_name)
- sys.exit()
+
+ # # ORGANISM 1
+ # workflow_parameters[LOAD_FASTA_ORG1] = {"organism": org_ids[0],
+ # "analysis_id": genome_analysis_ids[0],
+ # "do_update": "true"}
+ # # Change "do_update": "true" to "do_update": "false" in above parameters to prevent appending/updates to the fasta file in chado
+ # # WARNING: It is safer to never update it and just change the genome/ogs versions in the config
+ # workflow_parameters[JBROWSE_ORG1] = {}
+ # workflow_parameters[LOAD_GFF_ORG1] = {"organism": org_ids[0], "analysis_id": ogs_analysis_ids[0]}
+ # workflow_parameters[SYNC_FEATURES_ORG1] = {"organism_id": org_ids[0]}
+ # workflow_parameters[JBROWSE_CONTAINER] = {}
+
+
+ # # ORGANISM 2
+ # workflow_parameters[LOAD_FASTA_ORG2] = {"organism": org_ids[1],
+ # "analysis_id": genome_analysis_ids[1],
+ # "do_update": "true"}
+ # # Change "do_update": "true" to "do_update": "false" in above parameters to prevent appending/updates to the fasta file in chado
+ # # WARNING: It is safer to never update it and just change the genome/ogs versions in the config
+ # workflow_parameters[LOAD_GFF_ORG2] = {"organism": org_ids[1], "analysis_id": ogs_analysis_ids[1]}
+ # workflow_parameters[JRBOWSE_ORG2] = {}
+ # workflow_parameters[SYNC_FEATURES_ORG2] = {"organism_id": org_ids[1]}
+
+ # workflow_parameters[SYNC_GENOME_ANALYSIS_INTO_TRIPAL] = {"analysis_id": ogs_analysis_ids[0]}
+ # workflow_parameters[SYNC_OGS_ANALYSIS_INTO_TRIPAL] = {"analysis_id": genome_analysis_ids[0]}
+ # workflow_parameters[SYNC_FEATURES_INTO_TRIPAL] = {"organism_id": org_ids[0]}
+
+ # # POPULATE + INDEX DATA
+ # workflow_parameters[POPULATE_MAT_VIEWS] = {}
+ # workflow_parameters[INDEX_TRIPAL_DATA] = {}
+
+ # # Datamap for input datasets - dataset source (type): ldda (LibraryDatasetDatasetAssociation)
+ # run_workflow_for_current_organism.datamap = {}
+
+ # run_workflow_for_current_organism.datamap[GENOME_FASTA_FILE_ORG1] = {"src": "hda", "id": hda_ids_list[0]["genome_hda_id"]}
+ # run_workflow_for_current_organism.datamap[GFF_FILE_ORG1] = {"src": "hda", "id": hda_ids_list[0]["gff_hda_id"]}
+ # run_workflow_for_current_organism.datamap[PROTEINS_FASTA_FILE_ORG1] = {"src": "hda", "id": hda_ids_list[0]["proteins_hda_id"]}
+
+ # run_workflow_for_current_organism.datamap[GENOME_FASTA_FILE_ORG2] = {"src": "hda", "id": hda_ids_list[1]["genome_hda_id"]}
+ # run_workflow_for_current_organism.datamap[GFF_FILE_ORG2] = {"src": "hda", "id": hda_ids_list[1]["gff_hda_id"]}
+ # run_workflow_for_current_organism.datamap[GFF_FILE_ORG2] = {"src": "hda", "id": hda_ids_list[1]["proteins_hda_id"]}
+
+
+
+ # logging.info("OK: Workflow invoked")
+
+
+
+ # # If input workflow is Chado_load_Tripal_synchronize.ga
+ # if "Chado_load_Tripal_synchronize" in str(workflow):
+
+ # logging.info("Executing workflow 'Chado_load_Tripal_synchronize'")
+
+ # run_workflow_for_current_organism.connect_to_instance()
+ # run_workflow_for_current_organism.set_get_history()
+ # # run_workflow_for_current_organism.get_species_history_id()
+
+ # run_workflow_for_current_organism.install_changesets_revisions_for_individual_tools()
+ # run_workflow_for_current_organism.install_changesets_revisions_from_workflow(workflow_path=workflow)
+ # run_workflow_for_current_organism.add_organism_ogs_genome_analyses()
+ # run_workflow_for_current_organism.get_organism_id()
+ # run_workflow_for_current_organism.get_genome_analysis_id()
+ # run_workflow_for_current_organism.get_ogs_analysis_id()
+
+ # # run_workflow_for_current_organism.tripal_synchronize_organism_analyses()
+
+ # # Get the attributes of the instance and project data files
+ # run_workflow_for_current_organism.get_instance_attributes()
+
+ # # Import datasets into history and retrieve their hda IDs
+ # # TODO: can be simplified with direct access to the folder contents via the full path (no loop required)
+ # hda_ids = run_workflow_for_current_organism.import_datasets_into_history()
+
+ # # DEBUG
+ # # run_workflow_for_current_organism.get_invocation_report(workflow_name="Chado load Tripal synchronize")
+
+ # # Explicit workflow parameter names
+ # GENOME_FASTA_FILE = "0"
+ # GFF_FILE = "1"
+ # PROTEINS_FASTA_FILE = "2"
+ # TRANSCRIPTS_FASTA_FILE = "3"
+
+ # LOAD_FASTA_IN_CHADO = "4"
+ # LOAD_GFF_IN_CHADO = "5"
+ # SYNC_ORGANISM_INTO_TRIPAL = "6"
+ # SYNC_GENOME_ANALYSIS_INTO_TRIPAL = "7"
+ # SYNC_OGS_ANALYSIS_INTO_TRIPAL = "8"
+ # SYNC_FEATURES_INTO_TRIPAL = "9"
+
+ # workflow_parameters = {}
+
+ # workflow_parameters[GENOME_FASTA_FILE] = {}
+ # workflow_parameters[GFF_FILE] = {}
+ # workflow_parameters[PROTEINS_FASTA_FILE] = {}
+ # workflow_parameters[TRANSCRIPTS_FASTA_FILE] = {}
+
+ # workflow_parameters[LOAD_FASTA_IN_CHADO] = {"organism": run_workflow_for_current_organism.org_id,
+ # "analysis_id": run_workflow_for_current_organism.genome_analysis_id,
+ # "do_update": "true"}
+ # # Change "do_update": "true" to "do_update": "false" in above parameters to prevent appending/updates to the fasta file in chado
+ # # WARNING: It is safer to never update it and just change the genome/ogs versions in the config
+ # workflow_parameters[LOAD_GFF_IN_CHADO] = {"organism": run_workflow_for_current_organism.org_id,
+ # "analysis_id": run_workflow_for_current_organism.ogs_analysis_id}
+ # workflow_parameters[SYNC_ORGANISM_INTO_TRIPAL] = {"organism_id": run_workflow_for_current_organism.org_id}
+ # workflow_parameters[SYNC_GENOME_ANALYSIS_INTO_TRIPAL] = {"analysis_id": run_workflow_for_current_organism.ogs_analysis_id}
+ # workflow_parameters[SYNC_OGS_ANALYSIS_INTO_TRIPAL] = {"analysis_id": run_workflow_for_current_organism.genome_analysis_id}
+ # workflow_parameters[SYNC_FEATURES_INTO_TRIPAL] = {"organism_id": run_workflow_for_current_organism.org_id}
+
+ # # Datamap for input datasets - dataset source (type): ldda (LibraryDatasetDatasetAssociation)
+ # run_workflow_for_current_organism.datamap = {}
+ # run_workflow_for_current_organism.datamap[GENOME_FASTA_FILE] = {"src": "hda", "id": hda_ids["genome_hda_id"]}
+ # run_workflow_for_current_organism.datamap[GFF_FILE] = {"src": "hda", "id": hda_ids["gff_hda_id"]}
+ # run_workflow_for_current_organism.datamap[PROTEINS_FASTA_FILE] = {"src": "hda", "id": hda_ids["proteins_hda_id"]}
+ # run_workflow_for_current_organism.datamap[TRANSCRIPTS_FASTA_FILE] = {"src": "hda", "id": hda_ids["transcripts_hda_id"]}
+
+ # # run_workflow_for_current_organism.datamap = {}
+ # # run_workflow_for_current_organism.datamap[GENOME_FASTA_FILE] = {"src": "hda", "id":
+ # # run_workflow_for_current_organism.datasets["genome_file"]}
+ # # run_workflow_for_current_organism.datamap[GFF_FILE] = {"src": "hda",
+ # # "id": hda_ids["gff_hda_id"]}
+
+ # # Ensures galaxy has had time to retrieve datasets
+ # time.sleep(60)
+ # # Run the Chado load Tripal sync workflow with the parameters set above
+ # run_workflow_for_current_organism.run_workflow(workflow_path=workflow,
+ # workflow_parameters=workflow_parameters,
+ # datamap=run_workflow_for_current_organism.datamap,
+ # workflow_name="Chado load Tripal synchronize")
+
+ # # Jbrowse creation workflow
+ # elif "Jbrowse" in str(workflow):
+
+ # logging.info("Executing workflow 'Jbrowse'")
+
+ # run_workflow_for_current_organism.connect_to_instance()
+ # run_workflow_for_current_organism.set_get_history()
+ # run_workflow_for_current_organism.install_changesets_revisions_from_workflow(workflow_path=workflow)
+ # run_workflow_for_current_organism.get_organism_id()
+ # # Import datasets into history and get their hda IDs
+ # run_workflow_for_current_organism.import_datasets_into_history()
+ # hda_ids = run_workflow_for_current_organism.get_datasets_hda_ids() # Note: only call this function AFTER calling "import_datasets_into_history()"
+
+ # # Debugging
+ # # run_workflow_for_current_organism.get_invocation_report(workflow_name="Jbrowse")
+
+ # GENOME_FASTA_FILE = "0"
+ # GFF_FILE = "1"
+ # ADD_JBROWSE = "2"
+ # ADD_ORGANISM_TO_JBROWSE = "3"
+
+ # workflow_parameters = {}
+ # workflow_parameters[GENOME_FASTA_FILE] = {}
+ # workflow_parameters[GFF_FILE] = {}
+ # workflow_parameters[ADD_JBROWSE] = {}
+ # workflow_parameters[ADD_ORGANISM_TO_JBROWSE] = {}
+
+ # run_workflow_for_current_organism.datamap = {}
+ # run_workflow_for_current_organism.datamap[GENOME_FASTA_FILE] = {"src": "hda", "id": hda_ids["genome_hda_id"]}
+ # run_workflow_for_current_organism.datamap[GFF_FILE] = {"src": "hda", "id": hda_ids["gff_hda_id"]}
+
+ # # Run the jbrowse creation workflow
+ # run_workflow_for_current_organism.run_workflow(workflow_path=workflow,
+ # workflow_parameters=workflow_parameters,
+ # datamap=run_workflow_for_current_organism.datamap,
+ # workflow_name="Jbrowse")
+
+ # elif "Interpro" in str(workflow):
+
+ # logging.info("Executing workflow 'Interproscan")
+
+ # run_workflow_for_current_organism.connect_to_instance()
+ # run_workflow_for_current_organism.set_get_history()
+ # run_workflow_for_current_organism.install_changesets_revisions_from_workflow(workflow_path=workflow)
+ # # run_workflow_for_current_organism.get_species_history_id()
+
+ # # Get the attributes of the instance and project data files
+ # run_workflow_for_current_organism.get_instance_attributes()
+ # run_workflow.add_interproscan_analysis()
+ # run_workflow_for_current_organism.get_interpro_analysis_id()
+
+ # # Import datasets into history and retrieve their hda IDs
+ # run_workflow_for_current_organism.import_datasets_into_history()
+ # hda_ids = run_workflow_for_current_organism.get_datasets_hda_ids()
+
+ # INTERPRO_FILE = "0"
+ # LOAD_INTERPRO_IN_CHADO = "1"
+ # SYNC_INTERPRO_ANALYSIS_INTO_TRIPAL = "2"
+ # SYNC_FEATURES_INTO_TRIPAL = "3"
+ # POPULATE_MAT_VIEWS = "4"
+ # INDEX_TRIPAL_DATA = "5"
+
+ # workflow_parameters = {}
+ # workflow_parameters[INTERPRO_FILE] = {}
+ # workflow_parameters[LOAD_INTERPRO_IN_CHADO] = {"organism": run_workflow_for_current_organism.org_id,
+ # "analysis_id": run_workflow_for_current_organism.interpro_analysis_id}
+ # workflow_parameters[SYNC_INTERPRO_ANALYSIS_INTO_TRIPAL] = {"analysis_id": run_workflow_for_current_organism.interpro_analysis_id}
+
+
+ # run_workflow_for_current_organism.datamap = {}
+ # run_workflow_for_current_organism.datamap[INTERPRO_FILE] = {"src": "hda", "id": run_workflow_for_current_organism.hda_ids["interproscan_hda_id"]}
+
+ # # Run Interproscan workflow
+ # run_workflow_for_current_organism.run_workflow(workflow_path=workflow,
+ # workflow_parameters=workflow_parameters,
+ # datamap=run_workflow_for_current_organism.datamap,
+ # workflow_name="Interproscan")
+
+ # elif "Blast" in str(workflow):
+
+ # logging.info("Executing workflow 'Blast_Diamond")
+
+ # run_workflow_for_current_organism.connect_to_instance()
+ # run_workflow_for_current_organism.set_get_history()
+ # run_workflow_for_current_organism.install_changesets_revisions_from_workflow(workflow_path=workflow)
+ # # run_workflow_for_current_organism.get_species_history_id()
+
+ # # Get the attributes of the instance and project data files
+ # run_workflow_for_current_organism.get_instance_attributes()
+ # run_workflow_for_current_organism.add_blastp_diamond_analysis()
+ # run_workflow_for_current_organism.get_blastp_diamond_analysis_id()
+
+ # # Import datasets into history and retrieve their hda IDs
+ # run_workflow_for_current_organism.import_datasets_into_history()
+ # hda_ids = run_workflow_for_current_organism.get_datasets_hda_ids()
+
+ # BLAST_FILE = "0"
+ # LOAD_BLAST_IN_CHADO = "1"
+ # SYNC_BLAST_ANALYSIS_INTO_TRIPAL = "2"
+ # SYNC_FEATURES_INTO_TRIPAL = "3"
+ # POPULATE_MAT_VIEWS = "4"
+ # INDEX_TRIPAL_DATA = "5"
+
+ # workflow_parameters = {}
+ # workflow_parameters[INTERPRO_FILE] = {}
+ # workflow_parameters[LOAD_BLAST_IN_CHADO] = {"organism": run_workflow_for_current_organism.org_id,
+ # "analysis_id": run_workflow_for_current_organism.blast_diamond_analysis_id}
+ # workflow_parameters[SYNC_BLAST_ANALYSIS_INTO_TRIPAL] = {"analysis_id": run_workflow_for_current_organism.blast_diamond_analysis_id}
+
+ # run_workflow_for_current_organism.datamap = {}
+ # run_workflow_for_current_organism.datamap[INTERPRO_FILE] = {"src": "hda", "id": hda_ids["interproscan_hda_id"]}
+
+ # # Run Interproscan workflow
+ # run_workflow_for_current_organism.run_workflow(workflow_path=workflow,
+ # workflow_parameters=workflow_parameters,
+ # datamap=run_workflow_for_current_organism.datamap,
+ # workflow_name="Interproscan")
diff --git a/speciesData.py b/speciesData.py
index caccfda..2dbd952 100755
--- a/speciesData.py
+++ b/speciesData.py
@@ -59,17 +59,24 @@ class SpeciesData:
self.full_name_lowercase = self.full_name.lower()
self.abbreviation = "_".join(utilities.filter_empty_not_empty_items([self.genus_lowercase[0], self.species, self.strain, self.sex])["not_empty"])
+ # Bioblend/Chado IDs for an organism analyses/organisms/datasets/history/library
+ self.org_id = None
+ self.genome_analysis_id = None
+ self.ogs_analysis_id = None
+ self.interproscan_analysis_id = None
+ self.blastp_diamond_analysis_id = None
+ self.blastx_diamond_analysis_id = None
+ self.hda_ids = {}
self.instance_url = None
self.instance = None
self.history_id = None
self.library = None
self.library_id = None
+
self.script_dir = os.path.dirname(os.path.realpath(sys.argv[0]))
self.main_dir = None
self.species_dir = None
- self.org_id = None
- self.genome_analysis_id = None
- self.ogs_analysis_id = None
+
self.tool_panel = None
self.datasets = dict()
self.datasets_name = dict()
--
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