Release v2.1.0

General

• Bump cryptography requirement version to 3.2
• Create tools to handle multiple docker stacks in docker_tools repository (toggl galaxy state, update service)

Input config.yml file

• Fix typo in parameter galaxy_default_admin_user
• Add parameter galaxy_default_admin_key (required to create the admin user galaxy_default_admin_user in Galaxy)
• Check that all required parameters are not empty

Input organisms.yml file

• Add the parameter picture_path in the description section to add this picture in the Tripal homepage
• Add parameter contig_prefix in the data section to list the prefixes used for contig names. Required when they are multiple strains from the same species to map the contig name prefix to the right JBrowse dataset ID in Tripal (see Step "init")
• Add the go summary functionality in Tripal. This functionality can be activated with parameter go set to 1 in the services section
• Remove unused performed_by parameter
• Fix code for empty strain and/or sex

Step "init"

• Add a picture of the species in the Tripal homepage (the picture of the main strain if exists, the picture of another strain if not)
• Fix Tripal site name
• Add env variable JBROWSE_LINKS in the Tripal service, to map the contig name prefix to the right JBrowse dataset ID in Tripal (for Tripal hyperlinks to JBrowse)

Step "get data"

• Change data file names: use prefix strain_sex

Step "run workflow Phaeoexplorer"

• Create a parent module run_workflow_phaeo.py, with classes and methods used by the workflow scripts
• Create file constants_phaeo.py to store constants used in step "run workflow Phaeoexplorer" (workflows and galaxy tools details)
• Script gga_run_workflow_phaeo_jbrowse.py: runs a Galaxy workflow to load the genome, the annotations and the proteins to Chado, Tripal and JBrowse, for one or two strains of the same species
• Work in progress: workflows to load blast and interpro results

run_workflow_phaeoexplorer.py

@@ -987,12 +987,13 @@ if __name__ == "__main__":
 
     args = parser.parse_args()
 
+    bioblend_logger = logging.getLogger("bioblend")
     if args.verbose:
         logging.basicConfig(level=logging.DEBUG)
+        bioblend_logger.setLevel(logging.DEBUG)
     else:
         logging.basicConfig(level=logging.INFO)
-    logging.getLogger("urllib3").setLevel(logging.INFO)
-    logging.getLogger("bioblend").setLevel(logging.INFO)
+        bioblend_logger.setLevel(logging.INFO)
 
     # Parsing the config file if provided, using the default config otherwise
     if not args.config:
@@ -1000,6 +1001,12 @@ if __name__ == "__main__":
     else:
         args.config = os.path.abspath(args.config)
 
+    if args.config:
+        config_file = os.path.abspath(args.config)
+    else:
+        config_file = os.path.join(os.path.dirname(os.path.realpath(sys.argv[0])), constants.DEFAULT_CONFIG)
+    config = utilities.parse_config(config_file)
+
     if not args.main_directory:
         args.main_directory = os.getcwd()
     else:
@@ -1190,7 +1197,7 @@ 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 +1433,7 @@ 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 +1548,7 @@ 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 +1702,7 @@ 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))