gga_load_data (WIP)
Automated integration of new organisms into GGA environments as a form of a docker stack of services.
Description:
Automatically generate functional GGA environments from a descriptive input yaml file. See example datasets (example.yml) for an example of what information can be described and the correct formatting of these input files
The "gga_load_data" tool is divided in 4 separate scripts:
- gga_init: Create directory tree for organisms and deploy stacks for the input organisms as well as Traefik and optionally Authelia stacks
- gga_get_data: Create "src_data" directory tree for organisms and copy datasets for the input organisms into the organisms directory tree
- gga_load_data: Load the datasets of the input organisms into a library in their galaxy instance
- run_workflow_phaeoexplorer: Remotely run a custom workflow in galaxy, proposed as an "example script" to take inspiration from as workflow parameters are specific to Phaeoexplorer data
Directory tree:
For every input organism, a dedicated directory is created. The script will create this directory and all subdirectories required.
If the user is adding new data to a species (for example adding another strain/sex's datasets to the same species), the directory tree will be updated
Directory tree structure:
/main_directory
|
|---/genus1_species1
| |
| |---/blast
| | |---/links.yml
| | |---/banks.yml
| |
| |---/nginx
| | |---/conf
| | |---/default.conf
| |
| |---/blast
| | |---/banks.yml
| | |---/links.yml
| |
| |---/docker_data # Data used internally by docker (do not delete!)
| |
| |---/src_data
| | |---/genome
| | | |---/genus1_species1_strain_sex
| | | |---/vX.X
| | | |---/vX.X.fasta
| | |
| | |---/annotation
| | | |---/genus1_species1_strain_sex
| | | |---/OGSX.X
| | | |---/OGSX.X.gff
| | | |---/OGSX.X_pep.fasta
| | | |---/OGSX.X_transcripts.fasta
| | |
| | |---/tracks
| | |---/genus1_species1_strain_sex
| |
| |---/apollo
| | |---/annotation_groups.tsv
| |
| |---/docker-compose.yml
| |
| |---/metada_genus1_species1.yml (WIP)
|
|---/metadata.yml
|
|---/traefik
|---/docker-compose.yml
|---/authelia
|---/users.yml
|---/configuration.yml
Usage:
The scripts all take one mandatory input file that describes your species and their associated data (see example.yml in the "examples" folder of the repository)
You must also fill in a "config" file containing sensible variables (galaxy and tripal passwords, etc..) that the script will read to create the different services and to access the galaxy container. By default, the config file inside the repository root will be used if none is precised in the command line. An example of this config file is available in the "examples" folder of the repository.
Warning: the config file is not required as an option for the "gga_init" and "gga_get_data" scripts
-
Deploy stacks part:
$ python3 /path/to/repo/gga_init.py your_input_file.yml -c/--config your_config_file [-v/--verbose] [OPTIONS]--main-directory $PATH (Path where to create/update stacks; default=current directory) --force-traefik (If specified, will overwrite traefik and authelia files; default=False) -
Copy source data file:
$ python3 /path/to/repo/gga_get_data.py your_input_file.yml [-v/--verbose] [OPTIONS]--main-directory $PATH (Path where to access stacks; default=current directory) -
Load data in galaxy library and prepare galaxy instance:
$ python3 /path/to/repo/gga_load_data.py your_input_file.yml -c/--config your_config_file [-v/--verbose]--main-directory $PATH (Path where to access stacks; default=current directory) -
Run a workflow in galaxy:
$ python3 /path/to/repo/gga_load_data.py your_input_file.yml -c/--config your_config_file --workflow /path/to/workflow.ga [-v/--verbose] [OPTIONS]--workflow $WORKFLOW (Path to the workflow to run in galaxy. A couple of preset workflows are available in the "workflows" folder of the repository) --main-directory $PATH (Path where to access stacks; default=current directory)
Warning: the "input file" and "config file" have to be the same for all scripts!
Current limitations
When deploying the stack of services, the galaxy service can take a long time to be ready. This is due to the galaxy container preparing a persistent location for the container data. This can be bypassed by setting the variable "persist_galaxy_data" to "True" in the script "config" YAML file
The stacks deployment and the data loading into galaxy should hence be run separately and only once the galaxy service is ready
To check the status of the galaxy service, you can run $ docker service logs -f genus_species_galaxy or
./serexec genus_species_galaxy supervisorctl status to verify directly from the container
(The "gga_load_data.py" script will do this automatically anyway and will exit while notifying you it is not ready)
Requirements (temporary):
Requires Python 3.6