{"id":19829201,"url":"https://github.com/ssi-dk/bifrost-archive","last_synced_at":"2026-05-14T04:35:34.699Z","repository":{"id":95968299,"uuid":"150706894","full_name":"ssi-dk/bifrost-archive","owner":"ssi-dk","description":"Bacterial Surveillance platform, focused on QC in 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bifrost\n\n### User interface\n![](docs/_media/usage.gif)\n\n### Filter\n![](docs/_media/screenshot-filter.png)\n\n### Run plot\n![](docs/_media/screenshot-plot.png)\n\n### Run status checker\n![](docs/_media/run_checker.png)\n\n## Installing bifrost\n\n### Requirements\n\nPython \u003e=3.7\n\nanaconda/miniconda \u003e=4.3\n\nMongoDB \u003e=4.0.0\n\npip \u003e= 9.0\n\n### Setting up MongoDB\n\nGet MongoDB (community edition) and set it up following their documentation. \nYou'll need a database and read/write account for it.\n\nhttps://www.mongodb.com/download-center/community\n\nThe system relies on a species collection containing species-specific information used for \nQC testing and species identification. \n\nHere is an example of an entry:\n\n```\n{\n    \"_id\" : ___,\n    \"organism\" : \"E. coli\",\n    \"group\" : \"Escherichia\",\n    \"mlst_species\" : \"/path_to_db/ariba/mlst/Escherichia_coli_1/ref_db/\",\n    \"ncbi_species\" : \"Escherichia coli\",\n    \"min_length\" : 4500000, #min and max genome size to pass QC\n    \"max_length\" : 5800000\n}\n```\n\nAfter this step you should have a mongodb instance running, a user and a species collection with the species you expect to find in the samples.\n\n### Setting up the conda environment\n\nInstall the conda environment using envs/bifrost_for_install.yaml\n\n### Installing bifrostlib\n\nInstall bifrostlib withing the conda environment:\n\n```\nsource activate bifrost\npip install lib/bifrostlib/\n```\n\n\n### Adjusting config\n\nAdjust the main config.yaml and the individual components config.yaml.\n\n### Setting up the database key file location environment variable\n\nCreate a file named `keys.txt` with the mongodb URI including the database to use.\n\nCreate an environment variable with the mongodb key location.\n```\nexport BIFROST_DB_KEY=/path/to/keys.txt\n```\n\nYou can also set the key on the conda environment so it gets set when the environment is active.\n\nhttps://conda.io/docs/user-guide/tasks/manage-environments.html#saving-environment-variables\n\n## Running bifrost:\n\nActivate conda environment `bifrost`:\n```\nsource activate bifrost\n```\n\nCreate output directory, and move into it \n```\nmkdir run_name # Run name will be taken from the directory name or it can be set through config\ncd run_name\n```\n\nLink the demultiplexed (fastq.gz) samples folder as samples (by default):\n```\nln -s path/to/run samples\n```\n\nCopy/clone this directory in src directory:\n\n```\ngit clone git@github.com:ssi-dk/bifrost.git\nmv bifrost src\n```\n\nRun snakemake to generate the run command and the folder structures.\n\nSet the components you want to run (component names are the filenames in /components dir), default is `qcquickie,assemblatron,analyzer,ssi_stamper`.\n\nUse `use_mongodb=False` in config if you don't want to store the run, samples and results in the database.\nFor now however, you need to use the database to access the species table to run mlst.\n\nThen start the program running:\n\n```\nsnakemake -s src/bifrost.smk\n```\n\nYou can change any option in config.yaml using the `--config` argument. Here is an example with some useful ones:\n\n```\n--config components=qcquickie,assemblatron,analyzer partition=project run_name=myrun assembly_with=spades\n```\n\nFor more info on each parameter, check the sample config file.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fssi-dk%2Fbifrost-archive","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fssi-dk%2Fbifrost-archive","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fssi-dk%2Fbifrost-archive/lists"}