Overview

• Mass data import from SQLite
• Search and browse data
• User and group management
• Pipelines:
  • Bin peaks and cosine scoring for search and dendrograms
  • Replicates to collapsed spectra
  • Preprocessing
  • Upload spectra files

Docker install with docker-compose

 git clone https://github.com/idbac/maldidb
 # Use project.env.template to create project.env
 cp project.env.template project.env

Edit project.env to include the following:

POSTGRES_USER=<database user>
POSTGRES_PASSWORD=<database password>
POSTGRES_DB=<database name>
DATABASE=postgres
DATABASE_HOST=postgresdb
DATABASE_PORT=5432
SECRET_KEY=<any key>

Add R01 SQLite files, if present, to the ./mdb/r01data/ folder.

Add NCBI taxonomy data files (available from https://ftp.ncbi.nlm.nih.gov/pub/taxonomy/) nodes.dmp and names.dmp, if present, to the same r01data folder.

Finally, build and run the project:

docker-compose up --build

PostgreSQL does not need to be installed on the system beforehand unless performing a manual installaion.

Running docker-compose up --build the first time may take 15-30 minutes to complete. However, successive starts should complete within 15-30 seconds.

When the build is finished, the site processes will start, including Django. When Django runs for the first time, the first time that NCBI taxonomy data is present there will be additional processing time while the taxonomy data is inserted into the database. A GinIndex is also created for indexing the taxonomic data (e.g. http://logan.tw/posts/2017/12/30/full-text-search-with-django-and-postgresql/).

Avoid rebuilding on successive starts by calling docker-compose up or docker-compose start to start the system.

For production builds, set Debug = False in ./mdb/mdb/settings.py.

Use ./manage.py graph_models -a -g -o models.png to generate graph diagrams of the application's models.