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Commit 08074e9b authored by Arthur Le Bars's avatar Arthur Le Bars
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reworked datasets and Chado IDs assignation

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2 merge requests!9Release 2.0 (merge dev to master),!5Workflow v2
This commit is part of merge request !5. Comments created here will be created in the context of that merge request.
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gga_get_data.py
+ 14
− 8
View file @ 08074e9b
...@@ -124,12 +124,18 @@ class GetData(speciesData.SpeciesData): ...@@ -124,12 +124,18 @@ class GetData(speciesData.SpeciesData):
# search_excluded_datasets = ["interpro_path", "orthofinder_path", "blastp_path", "blastx_path"] # search_excluded_datasets = ["interpro_path", "orthofinder_path", "blastp_path", "blastx_path"]
# # These datasets will not be searched if missing in the input file # # These datasets will not be searched if missing in the input file
dataset_shortname = ""
if self.sex is not None or self.sex != "":
dataset_shortname = self.genus[0].lower() + "_" + self.species.lower() + "_" + self.sex[0].lower()
else:
dataset_shortname = self.genus[0].lower() + "_" + self.species.lower()
# Copy datasets in the organism src_data dir tree correct folder # Copy datasets in the organism src_data dir tree correct folder
for k, v in datasets_to_get.items(): for k, v in datasets_to_get.items():
if v: # If dataset is not present in input file, skip copy if v: # If dataset is not present in input file, skip copy
if k in genome_datasets: if k in genome_datasets:
logging.info("Copying {0} ({1}) into {2}".format(k, v, organism_genome_dir)) logging.info("Copying {0} ({1}) into {2}".format(k, v, organism_genome_dir))
genome_fname = "v%s.fasta" % self.genome_version genome_fname = "{0}_v{1}.fasta".format(dataset_shortname, self.genome_version)
try: try:
shutil.copyfile(os.path.abspath(v), os.path.join(organism_genome_dir, genome_fname)) shutil.copyfile(os.path.abspath(v), os.path.join(organism_genome_dir, genome_fname))
except Exception as exc: except Exception as exc:
...@@ -137,19 +143,19 @@ class GetData(speciesData.SpeciesData): ...@@ -137,19 +143,19 @@ class GetData(speciesData.SpeciesData):
elif k in annotation_datasets: elif k in annotation_datasets:
dataset_fname = "" dataset_fname = ""
if k == "gff_path": if k == "gff_path":
dataset_fname = "OGS%s.gff" % self.ogs_version dataset_fname = "{0}_OGS{1}.gff".format(dataset_shortname, self.genome_version)
elif k == "transcripts_path": elif k == "transcripts_path":
dataset_fname = "OGS%s_transcripts.fasta" % self.ogs_version dataset_fname = "{0}_OGS{1}_transcripts.fasta".format(dataset_shortname, self.genome_version)
elif k == "proteins_path": elif k == "proteins_path":
dataset_fname = "OGS%s_proteins.fasta" % self.ogs_version dataset_fname = "{0}_OGS{1}_proteins.fasta".format(dataset_shortname, self.genome_version)
elif k == "orthofinder_path": elif k == "orthofinder_path":
dataset_fname = "OGS%s_orthofinder.tsv" % self.ogs_version dataset_fname = "{0}_OGS{1}_orthofinder.tsv".format(dataset_shortname, self.genome_version)
elif k == "interpro_path": elif k == "interpro_path":
dataset_fname = "OGS%s_interproscan.xml" % self.ogs_version dataset_fname = "{0}_OGS{1}_interproscan.xml".format(dataset_shortname, self.genome_version)
elif k == "blastp_path": elif k == "blastp_path":
dataset_fname = "OGS%s_blastp.xml" % self.ogs_version dataset_fname = "{0}_OGS{1}_blastp.xml".format(dataset_shortname, self.genome_version)
elif k == "blastx_path": elif k == "blastx_path":
dataset_fname = "OGS%s_blastx.xml" % self.ogs_version dataset_fname = "{0}_OGS{1}_blastx.xml".format(dataset_shortname, self.genome_version)
logging.info("Copying {0} ({1}) into {2}".format(k, v, organism_annotation_dir)) logging.info("Copying {0} ({1}) into {2}".format(k, v, organism_annotation_dir))
try: try:
shutil.copyfile(os.path.abspath(v), os.path.join(organism_annotation_dir, dataset_fname)) shutil.copyfile(os.path.abspath(v), os.path.join(organism_annotation_dir, dataset_fname))
......
run_workflow_phaeoexplorer.py
+ 260
− 273
View file @ 08074e9b
...@@ -169,6 +169,7 @@ class RunWorkflow(speciesData.SpeciesData): ...@@ -169,6 +169,7 @@ class RunWorkflow(speciesData.SpeciesData):
name = toolshed_dict["name"] name = toolshed_dict["name"]
owner = toolshed_dict["owner"] owner = toolshed_dict["owner"]
toolshed = "https://" + toolshed_dict["tool_shed"] toolshed = "https://" + toolshed_dict["tool_shed"]
logging.warning("Installing changeset revision {0} for {1}".format(changeset_revision, name))
self.instance.toolshed.install_repository_revision(tool_shed_url=toolshed, name=name, owner=owner, self.instance.toolshed.install_repository_revision(tool_shed_url=toolshed, name=name, owner=owner,
changeset_revision=changeset_revision, changeset_revision=changeset_revision,
...@@ -183,6 +184,7 @@ class RunWorkflow(speciesData.SpeciesData): ...@@ -183,6 +184,7 @@ class RunWorkflow(speciesData.SpeciesData):
name = toolshed_dict["name"] name = toolshed_dict["name"]
owner = toolshed_dict["owner"] owner = toolshed_dict["owner"]
toolshed = "https://" + toolshed_dict["tool_shed"] toolshed = "https://" + toolshed_dict["tool_shed"]
logging.warning("Installing changeset revision {0} for {1}".format(changeset_revision, name))
self.instance.toolshed.install_repository_revision(tool_shed_url=toolshed, name=name, owner=owner, self.instance.toolshed.install_repository_revision(tool_shed_url=toolshed, name=name, owner=owner,
changeset_revision=changeset_revision, changeset_revision=changeset_revision,
...@@ -197,6 +199,7 @@ class RunWorkflow(speciesData.SpeciesData): ...@@ -197,6 +199,7 @@ class RunWorkflow(speciesData.SpeciesData):
name = toolshed_dict["name"] name = toolshed_dict["name"]
owner = toolshed_dict["owner"] owner = toolshed_dict["owner"]
toolshed = "https://" + toolshed_dict["tool_shed"] toolshed = "https://" + toolshed_dict["tool_shed"]
logging.warning("Installing changeset revision {0} for {1}".format(changeset_revision, name))
self.instance.toolshed.install_repository_revision(tool_shed_url=toolshed, name=name, owner=owner, self.instance.toolshed.install_repository_revision(tool_shed_url=toolshed, name=name, owner=owner,
changeset_revision=changeset_revision, changeset_revision=changeset_revision,
...@@ -211,7 +214,8 @@ class RunWorkflow(speciesData.SpeciesData): ...@@ -211,7 +214,8 @@ class RunWorkflow(speciesData.SpeciesData):
name = toolshed_dict["name"] name = toolshed_dict["name"]
owner = toolshed_dict["owner"] owner = toolshed_dict["owner"]
toolshed = "https://" + toolshed_dict["tool_shed"] toolshed = "https://" + toolshed_dict["tool_shed"]
logging.warning("Installing changeset revision %s for add_analysis" % changeset_revision) logging.warning("Installing changeset revision {0} for {1}".format(changeset_revision, name))
self.instance.toolshed.install_repository_revision(tool_shed_url=toolshed, name=name, owner=owner, self.instance.toolshed.install_repository_revision(tool_shed_url=toolshed, name=name, owner=owner,
changeset_revision=changeset_revision, changeset_revision=changeset_revision,
install_tool_dependencies=True, install_tool_dependencies=True,
...@@ -247,118 +251,112 @@ class RunWorkflow(speciesData.SpeciesData): ...@@ -247,118 +251,112 @@ class RunWorkflow(speciesData.SpeciesData):
self.set_get_history() self.set_get_history()
tool_version = "2.3.4+galaxy0" 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:
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})
else:
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})
# Add OGS analysis to chado
logging.info("Adding OGS 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/%s" % tool_version,
history_id=self.history_id,
tool_inputs={"name": 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})
# Add genome analysis to chado 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")
logging.info("Adding genome analysis to the instance's chado database")
self.instance.tools.run_tool( get_organisms = self.instance.tools.run_tool(
tool_id="toolshed.g2.bx.psu.edu/repos/gga/chado_analysis_add_analysis/analysis_add_analysis/%s" % tool_version, tool_id="toolshed.g2.bx.psu.edu/repos/gga/chado_organism_get_organisms/organism_get_organisms/%s" % tool_version,
history_id=self.history_id, history_id=self.history_id,
tool_inputs={"name": self.full_name_lowercase + " genome v" + self.genome_version, tool_inputs={})
"program": "Performed by Genoscope",
"programversion": str(self.sex + "genome v" + self.genome_version),
"sourcename": "Genoscope",
"date_executed": self.date})
# # TODO: check output of get_organism --> if empty or wrong --> rerun --> else: go next time.sleep(10) # Ensure the tool has had time to complete
# # Get organism and analyses IDs (runtime inputs for workflow) org_outputs = get_organisms["outputs"] # Outputs from the get_organism tool
# time.sleep(3) org_job_out_id = org_outputs[0]["id"] # ID of the get_organism output dataset (list of dicts)
# # Get the ID for the current organism in chado org_json_output = self.instance.datasets.download_dataset(dataset_id=org_job_out_id) # Download the dataset
# org = self.instance.tools.run_tool( org_output = json.loads(org_json_output) # Turn the dataset into a list for parsing
# 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={"abbr": self.abbreviation,
# "genus": self.genus_uppercase,
# "species": self.chado_species_name,
# "common": self.common})
# time.sleep(3)
# # 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/%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 = org["outputs"][0]["id"]
# org_json_output = self.instance.datasets.download_dataset(dataset_id=org_job_out)
# try:
# org_output = json.loads(org_json_output)[0]
# self.org_id = str(org_output["organism_id"]) # id needs to be a str to be recognized by chado tools
# except IndexError:
# logging.critical("No organism matching " + self.full_name + " exists in the instance's chado database")
# sys.exit()
def get_genome_analysis_id(self):
"""
"""
# Get the ID for the genome analysis in chado org_id = None
genome_analysis = self.instance.tools.run_tool(
tool_id="toolshed.g2.bx.psu.edu/repos/gga/chado_analysis_get_analyses/analysis_get_analyses/2.3.4+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"]
genome_analysis_json_output = self.instance.datasets.download_dataset(dataset_id=genome_analysis_job_out)
try:
genome_analysis_output = json.loads(genome_analysis_json_output)[0]
self.genome_analysis_id = str(genome_analysis_output["analysis_id"])
except IndexError as exc:
logging.critical("no matching genome analysis exists in the instance's chado database")
sys.exit(exc)
return self.genome_analysis_id # 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)
def get_ogs_analysis_id(self):
"""
"""
# Get the ID for the OGS analysis in chado if org_id is None:
ogs_analysis = self.instance.tools.run_tool( if self.common == "" or self.common is None:
tool_id="toolshed.g2.bx.psu.edu/repos/gga/chado_analysis_get_analyses/analysis_get_analyses/2.3.4+galaxy0", 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(organism_output_dict["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(organism_output_dict["organism_id"]) # id needs to be a str to be recognized by chado tools
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, history_id=self.history_id,
tool_inputs={"name": self.full_name_lowercase + " OGS" + self.ogs_version}) tool_inputs={})
ogs_analysis_job_out = ogs_analysis["outputs"][0]["id"]
ogs_analysis_json_output = self.instance.datasets.download_dataset(dataset_id=ogs_analysis_job_out)
try:
ogs_analysis_output = json.loads(ogs_analysis_json_output)[0]
self.ogs_analysis_id = str(ogs_analysis_output["analysis_id"])
except IndexError as exc:
logging.critical("No matching OGS analysis exists in the instance's chado database")
sys.exit(exc)
return self.ogs_analysis_id 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)
ogs_analysis_id = None
genome_analysis_id = None
# Look up list of outputs (dictionaries)
for analysis_output_dict in analysis_output:
if analysis_output_dict["name"] == self.full_name_lowercase + " OGS" + self.ogs_version:
ogs_analysis_id = str(analysis_output_dict["analysis_id"])
if analysis_output_dict["name"] == self.full_name_lowercase + " genome v" + self.genome_version:
genome_analysis_id = str(analysis_output_dict["analysis_id"])
if ogs_analysis_id is None:
add_ogs_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": 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_ogs_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)
ogs_analysis_id = analysis_output["analysis_id"]
ogs_analysis_id = str(analysis_output_dict["analysis_id"])
if genome_analysis_id is None:
add_genome_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": self.full_name_lowercase + " genome v" + self.genome_version,
"program": "Performed by Genoscope",
"programversion": str(self.sex + "genome v" + self.genome_version),
"sourcename": "Genoscope",
"date_executed": self.date})
analysis_outputs = add_genome_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)
genome_analysis_id = str(analysis_output_dict["analysis_id"])
print({"org_id": org_id, "genome_analysis_id": genome_analysis_id, "ogs_analysis_id": ogs_analysis_id})
return({"org_id": org_id, "genome_analysis_id": genome_analysis_id, "ogs_analysis_id": ogs_analysis_id})
def add_interproscan_analysis(self): def add_interproscan_analysis(self):
...@@ -510,17 +508,7 @@ class RunWorkflow(speciesData.SpeciesData): ...@@ -510,17 +508,7 @@ class RunWorkflow(speciesData.SpeciesData):
return invocation_report return invocation_report
def import_datasets_into_history(self):
def get_datasets_ldda_ids(self):
"""
Get and return the ldda_ids (and names) for the datasets in the library
"""
return 0
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 Find datasets in a library, get their ID and import them into the current history if they are not already
...@@ -545,7 +533,6 @@ class RunWorkflow(speciesData.SpeciesData): ...@@ -545,7 +533,6 @@ class RunWorkflow(speciesData.SpeciesData):
folders_ids[i["name"]] = i["id"] folders_ids[i["name"]] = i["id"]
# Iterating over the folders to find datasets and map datasets to their IDs # Iterating over the folders to find datasets and map datasets to their IDs
logging.debug("Datasets IDs: ")
for k, v in folders_ids.items(): for k, v in folders_ids.items():
if k == "/genome/{0}/v{1}".format(self.species_folder_name, self.genome_version): if k == "/genome/{0}/v{1}".format(self.species_folder_name, self.genome_version):
sub_folder_content = self.instance.folders.show_folder(folder_id=v, contents=True) sub_folder_content = self.instance.folders.show_folder(folder_id=v, contents=True)
...@@ -555,7 +542,6 @@ class RunWorkflow(speciesData.SpeciesData): ...@@ -555,7 +542,6 @@ class RunWorkflow(speciesData.SpeciesData):
if e["name"].endswith(".fasta"): if e["name"].endswith(".fasta"):
self.datasets["genome_file"] = e["ldda_id"] self.datasets["genome_file"] = e["ldda_id"]
self.datasets_name["genome_file"] = e["name"] self.datasets_name["genome_file"] = e["name"]
logging.debug("\tGenome file:\t" + e["name"] + ": " + e["ldda_id"])
if k == "/annotation/{0}/OGS{1}".format(self.species_folder_name, self.ogs_version): if k == "/annotation/{0}/OGS{1}".format(self.species_folder_name, self.ogs_version):
sub_folder_content = self.instance.folders.show_folder(folder_id=v, contents=True) sub_folder_content = self.instance.folders.show_folder(folder_id=v, contents=True)
...@@ -563,59 +549,86 @@ class RunWorkflow(speciesData.SpeciesData): ...@@ -563,59 +549,86 @@ class RunWorkflow(speciesData.SpeciesData):
for e in v2: for e in v2:
if type(e) == dict: if type(e) == dict:
if "transcripts" in e["name"]: if "transcripts" in e["name"]:
# the attributes datasets is set in the function get_instance_attributes()
self.datasets["transcripts_file"] = e["ldda_id"] self.datasets["transcripts_file"] = e["ldda_id"]
self.datasets_name["transcripts_file"] = e["name"] self.datasets_name["transcripts_file"] = e["name"]
logging.debug("\tTranscripts file:\t" + e["name"] + ": " + e["ldda_id"])
elif "proteins" in e["name"]: elif "proteins" in e["name"]:
self.datasets["proteins_file"] = e["ldda_id"] self.datasets["proteins_file"] = e["ldda_id"]
self.datasets_name["proteins_file"] = e["name"] self.datasets_name["proteins_file"] = e["name"]
logging.debug("\tProteins file:\t" + e["name"] + ": " + e["ldda_id"])
elif "gff" in e["name"]: elif "gff" in e["name"]:
self.datasets["gff_file"] = e["ldda_id"] self.datasets["gff_file"] = e["ldda_id"]
self.datasets_name["gff_file"] = e["name"] self.datasets_name["gff_file"] = e["name"]
logging.debug("\tGFF file:\t" + e["name"] + ": " + e["ldda_id"])
elif "interpro" in e["name"]: elif "interpro" in e["name"]:
self.datasets["interproscan_file"] = e["ldda_id"] self.datasets["interproscan_file"] = e["ldda_id"]
self.datasets_name["interproscan_file"] = e["name"] self.datasets_name["interproscan_file"] = e["name"]
logging.debug("\tInterproscan file:\t" + e["name"] + ": " + e["ldda_id"])
elif "blastp" in e["name"]: elif "blastp" in e["name"]:
self.datasets["blast_diamond_file"] = e["ldda_id"] self.datasets["blast_diamond_file"] = e["ldda_id"]
self.datasets_name["blast_diamond_file"] = e["name"] self.datasets_name["blast_diamond_file"] = e["name"]
logging.debug("\tBlastp diamond file:\t" + e["name"] + ": " + e["ldda_id"])
logging.debug("Uploading datasets into history %s" % self.history_id) dataset_shortname = ""
if self.sex is not None or self.sex != "":
dataset_shortname = self.genus[0].lower() + "_" + self.species.lower() + "_" + self.sex[0].lower()
else:
dataset_shortname = self.genus[0].lower() + "_" + self.species.lower()
history_datasets_li = self.instance.datasets.get_datasets()
genome_hda_id, gff_hda_id, transcripts_hda_id, proteins_hda_id, blast_diamond_hda_id, interproscan_hda_id = None, None, None, None, None, None
# Finding datasets in history (matches datasets name)
for dataset in history_datasets_li:
dataset_name = dataset["name"]
if dataset_shortname in dataset_name:
dataset_id = dataset["id"]
if dataset_name.endswith(".fasta"):
genome_hda_id = dataset_id
if dataset_name.endswith(".gff"):
gff_hda_id = dataset_id
if dataset_name.endswith("transcripts.fasta"):
transcripts_hda_id = dataset_id
if dataset_name.endswith("proteins.fasta"):
proteins_hda_id = dataset_id
if dataset_name.endswith("blasp.xml"):
blast_diamond_hda_id = dataset_id
# Import each dataset into history if it is not imported # Import each dataset into history if it is not imported
logging.debug("Uploading datasets into history %s" % self.history_id)
first_hda_ids = self.get_datasets_hda_ids(imported_datasets_ids=imported_datasets_ids) if genome_hda_id is None:
genome_dataset_upload = self.instance.histories.upload_dataset_from_library(history_id=self.history_id, lib_dataset_id=self.datasets["genome_file"])
if first_hda_ids["genome_hda_id"] is not None: genome_hda_id = genome_dataset_upload["id"]
self.instance.histories.upload_dataset_from_library(history_id=self.history_id, lib_dataset_id=self.datasets["genome_file"]) if gff_hda_id is None:
if first_hda_ids["gff_hda_id"] is not None: gff_dataset_upload = self.instance.histories.upload_dataset_from_library(history_id=self.history_id, lib_dataset_id=self.datasets["gff_file"])
self.instance.histories.upload_dataset_from_library(history_id=self.history_id, lib_dataset_id=self.datasets["gff_file"]) gff_hda_id = gff_dataset_upload["id"]
if first_hda_ids["transcripts_hda_id"] is not None: if transcripts_hda_id is None:
self.instance.histories.upload_dataset_from_library(history_id=self.history_id, lib_dataset_id=self.datasets["transcripts_file"]) transcripts_dataset_upload = 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 not None: transcripts_hda_id = transcripts_dataset_upload["id"]
self.instance.histories.upload_dataset_from_library(history_id=self.history_id, lib_dataset_id=self.datasets["proteins_file"]) if proteins_hda_id is None:
if first_hda_ids["interproscan_hda_id"] is None: proteins_dataset_upload = self.instance.histories.upload_dataset_from_library(history_id=self.history_id, lib_dataset_id=self.datasets["proteins_file"])
proteins_hda_id = proteins_dataset_upload["id"]
if interproscan_hda_id is None:
try: try:
self.instance.histories.upload_dataset_from_library(history_id=self.history_id, lib_dataset_id=self.datasets["interproscan_file"]) interproscan_dataset_upload = self.instance.histories.upload_dataset_from_library(history_id=self.history_id, lib_dataset_id=self.datasets["interproscan_file"])
interproscan_hda_id = interproscan_dataset_upload["id"]
except Exception as exc: except Exception as exc:
logging.debug("Interproscan file not found in library (history: {0})".format(self.history_id)) logging.debug("Interproscan file not found in library (history: {0})".format(self.history_id))
if first_hda_ids["blast_diamond_hda_id"] is None: if blast_diamond_hda_id is None:
try: try:
self.instance.histories.upload_dataset_from_library(history_id=self.history_id, lib_dataset_id=self.datasets["blast_diamond_file"]) blast_diamond_dataset_upload = self.instance.histories.upload_dataset_from_library(history_id=self.history_id, lib_dataset_id=self.datasets["blast_diamond_file"])
blast_diamond_hda_id = blast_diamond_upload["id"]
except Exception as exc: except Exception as exc:
logging.debug("Blastp file not found in library (history: {0})".format(self.history_id)) logging.debug("Blastp file not found in library (history: {0})".format(self.history_id))
# _datasets = self.instance.datasets.get_datasets()
# with open(os.path.join(self.main_dir, "datasets_ids.json"), "w") as datasets_ids_outfile:
# datasets_ids_outfile.write(str(_datasets))
# Return a dict made of the hda ids # Return a dict made of the hda ids
return self.get_datasets_hda_ids(imported_datasets_ids=first_hda_ids["imported_datasets_ids"]) return {"genome_hda_id": genome_hda_id,
"gff_hda_id": gff_hda_id,
"transcripts_hda_id": transcripts_hda_id,
"proteins_hda_id": proteins_hda_id,
"blast_diamond_hda_id": blast_diamond_hda_id,
"interproscan_hda_id": interproscan_hda_id}
def get_datasets_hda_ids(self, imported_datasets_ids): def get_datasets_hda_ids(self):
""" """
Get the hda IDs of the datasets imported into an history Get the hda IDs of the datasets imported into an history
...@@ -677,81 +690,6 @@ class RunWorkflow(speciesData.SpeciesData): ...@@ -677,81 +690,6 @@ class RunWorkflow(speciesData.SpeciesData):
"imported_datasets_ids": imported_datasets_ids} "imported_datasets_ids": imported_datasets_ids}
def get_organism_id(self):
"""
Retrieve current organism ID
Will try to add it to Chado if the organism ID can't be found
:return:
"""
tool_version = "2.3.4+galaxy0"
time.sleep(3)
# # Get the ID for the current organism in chado
# 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",
# history_id=self.history_id,
# tool_inputs={"abbr": self.abbreviation,
# "genus": self.genus_uppercase,
# "species": self.chado_species_name,
# "common": self.common})
# time.sleep(3)
# 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/2.3.4+galaxy0",
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 = org["outputs"][0]["id"]
org_json_output = self.instance.datasets.download_dataset(dataset_id=org_job_out)
try:
org_output = json.loads(org_json_output)[0]
self.org_id = str(org_output["organism_id"]) # id needs to be a str to be recognized by chado tools
except IndexError:
logging.warning("No organism matching " + self.full_name + " exists in the instance's chado database, adding it")
if self.common == "" or self.common is None:
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})
else:
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})
# 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/2.3.4+galaxy0",
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 = org["outputs"][0]["id"]
org_json_output = self.instance.datasets.download_dataset(dataset_id=org_job_out)
try:
org_output = json.loads(org_json_output)[0]
self.org_id = str(org_output["organism_id"]) # id needs to be a str to be recognized by chado tools
except IndexError:
logging.critical("Cannot add {0} as an organism in Chado, please check the galaxy instance {1}".format(self.full_name, self.instance_url))
sys.exit()
return self.org_id
def run_workflow(workflow_path, workflow_parameters, datamap, config, input_species_number): def run_workflow(workflow_path, workflow_parameters, datamap, config, input_species_number):
""" """
Run a workflow in galaxy Run a workflow in galaxy
...@@ -810,9 +748,7 @@ def run_workflow(workflow_path, workflow_parameters, datamap, config, input_spec ...@@ -810,9 +748,7 @@ def run_workflow(workflow_path, workflow_parameters, datamap, config, input_spec
def create_sp_workflow_dict(sp_dict, main_dir, config):
def create_sp_workflow_dict(sp_dict, main_dir, config, imported_datasets_ids):
""" """
""" """
...@@ -847,13 +783,16 @@ def create_sp_workflow_dict(sp_dict, main_dir, config, imported_datasets_ids): ...@@ -847,13 +783,16 @@ def create_sp_workflow_dict(sp_dict, main_dir, config, imported_datasets_ids):
history_id = run_workflow_for_current_organism.set_get_history() 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.install_changesets_revisions_for_individual_tools()
run_workflow_for_current_organism.add_organism_ogs_genome_analyses() ids = run_workflow_for_current_organism.add_organism_ogs_genome_analyses()
org_id = run_workflow_for_current_organism.get_organism_id() org_id = None
genome_analysis_id = run_workflow_for_current_organism.get_genome_analysis_id() genome_analysis_id = None
ogs_analysis_id = run_workflow_for_current_organism.get_ogs_analysis_id() ogs_analysis_id = None
org_id = ids["org_id"]
genome_analysis_id = ids["genome_analysis_id"]
ogs_analysis_id = ids["ogs_analysis_id"]
instance_attributes = run_workflow_for_current_organism.get_instance_attributes() 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) 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) 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 genus_species = run_workflow_for_current_organism.genus_species
...@@ -914,13 +853,12 @@ def install_changesets_revisions_from_workflow(instance, workflow_path): ...@@ -914,13 +853,12 @@ def install_changesets_revisions_from_workflow(instance, workflow_path):
# (If it's not installed, the show_tool version returned will be a default version with the suffix "XXXX+0") # (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 show_tool["version"] != v["tool_version"]:
# If it doesn't match, proceed to install of the correct changeset revision # 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"] toolshed = "https://" + v["tool_shed_repository"]["tool_shed"]
name = v["tool_shed_repository"]["name"] name = v["tool_shed_repository"]["name"]
owner = v["tool_shed_repository"]["owner"] owner = v["tool_shed_repository"]["owner"]
changeset_revision = v["tool_shed_repository"]["changeset_revision"] changeset_revision = v["tool_shed_repository"]["changeset_revision"]
logging.debug("Installing changeset {0} for tool {1}".format(changeset_revision, name)) logging.warning("Installed tool versions for tool {0} do not match the version required by the specified workflow, installing changeset {1}".format(name, changeset_revision))
# Install changeset # Install changeset
instance.toolshed.install_repository_revision(tool_shed_url=toolshed, name=name, owner=owner, instance.toolshed.install_repository_revision(tool_shed_url=toolshed, name=name, owner=owner,
...@@ -928,6 +866,12 @@ def install_changesets_revisions_from_workflow(instance, workflow_path): ...@@ -928,6 +866,12 @@ def install_changesets_revisions_from_workflow(instance, workflow_path):
install_tool_dependencies=True, install_tool_dependencies=True,
install_repository_dependencies=False, install_repository_dependencies=False,
install_resolver_dependencies=True) install_resolver_dependencies=True)
else:
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("Installed tool versions for tool {0} match the version in the specified workflow (changeset {1})".format(name, changeset_revision))
logging.info("Tools versions and changesets from workflow validated") logging.info("Tools versions and changesets from workflow validated")
...@@ -992,17 +936,10 @@ if __name__ == "__main__": ...@@ -992,17 +936,10 @@ if __name__ == "__main__":
config = utilities.parse_config(args.config) config = utilities.parse_config(args.config)
all_sp_workflow_dict = {} all_sp_workflow_dict = {}
# 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: 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 # 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) current_sp_workflow_dict = create_sp_workflow_dict(sp_dict, main_dir=args.main_directory, config=config)
current_sp_key = list(current_sp_workflow_dict.keys())[0] current_sp_key = list(current_sp_workflow_dict.keys())[0]
current_sp_value = list(current_sp_workflow_dict.values())[0] current_sp_value = list(current_sp_workflow_dict.values())[0]
...@@ -1087,6 +1024,29 @@ if __name__ == "__main__": ...@@ -1087,6 +1024,29 @@ if __name__ == "__main__":
org1_transcripts_hda_id = org1_dict["hda_ids"]["transcripts_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_proteins_hda_id = org1_dict["hda_ids"]["proteins_hda_id"]
org1_gff_hda_id = org1_dict["hda_ids"]["gff_hda_id"] org1_gff_hda_id = org1_dict["hda_ids"]["gff_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_genome_analysis_id": org1_genome_analysis_id,
"org1_ogs_analysis_id": org1_ogs_analysis_id,
"org1_transcripts_hda_id": org1_transcripts_hda_id,
"org1_proteins_hda_id": org1_proteins_hda_id,
"org1_gff_hda_id": org1_gff_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 found for organism {0} (parameter: {1}, parameter value: {2})".format(org1_full_name, param_name, param_value))
sys.exit()
# Organism 2 attributes # Organism 2 attributes
org2_genus = org2_dict["genus"] org2_genus = org2_dict["genus"]
...@@ -1103,7 +1063,30 @@ if __name__ == "__main__": ...@@ -1103,7 +1063,30 @@ if __name__ == "__main__":
org2_transcripts_hda_id = org2_dict["hda_ids"]["transcripts_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_proteins_hda_id = org2_dict["hda_ids"]["proteins_hda_id"]
org2_gff_hda_id = org2_dict["hda_ids"]["gff_hda_id"] org2_gff_hda_id = org2_dict["hda_ids"]["gff_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": org2_species_folder_name,
"org2_full_name": org2_full_name,
"org2_strain": org2_strain,
"org2_sex": org2_sex,
"org2_org_id": org2_org_id,
"org2_genome_analysis_id": org2_genome_analysis_id,
"org2_ogs_analysis_id": org2_ogs_analysis_id,
"org2_transcripts_hda_id": org2_transcripts_hda_id,
"org2_proteins_hda_id": org2_proteins_hda_id,
"org2_gff_hda_id": org2_gff_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():
logging.info("Parameters for organism 1 (%s)" % org2_full_name)
if param_value is None or param_value == "":
logging.critical("Empty parameter 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) # Source files association (ordered by their IDs in the workflow)
GENOME_FASTA_FILE_ORG1 = "0" GENOME_FASTA_FILE_ORG1 = "0"
...@@ -1177,50 +1160,54 @@ if __name__ == "__main__": ...@@ -1177,50 +1160,54 @@ if __name__ == "__main__":
datamap[GFF_FILE_ORG2] = {"src": "hda", "id": org2_gff_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} datamap[PROTEINS_FASTA_FILE_ORG2] = {"src": "hda", "id": org2_proteins_hda_id}
with open(workflow_path, 'r') as ga_in_file: # with open(workflow_path, 'r') as ga_in_file:
# Store the decoded json dictionary # # Store the decoded json dictionary
workflow_dict = json.load(ga_in_file) # workflow_dict = json.load(ga_in_file)
workflow_name = workflow_dict["name"] # workflow_name = workflow_dict["name"]
# For the Jbrowse tool, we unfortunately have to manually edit the parameters instead of setting them # # 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) # # 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) # # Scratchgmod test: need "http" (or "https"), the hostname (+ port)
if "menu_url" not in config.keys(): # 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_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\}") # 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: # else:
jbrowse_menu_url_org1 = config["menu_url"] # jbrowse_menu_url_org1 = config["menu_url"]
jbrowse_menu_url_org2 = jbrowse_menu_url_org1 # 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)
# # 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))
# 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)
print(workflow_dict)
# 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))
# Get the instance attribute from the object for future connections # Get the instance attribute from the object for future connections
......
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