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both remote and local use of refget standards. It has clients and functions for both refget sequences and refget sequence collections (seqcol).\n2. `/seqcolapi`: Sequence collections API software, a FastAPI wrapper built on top of the `refget` package. It provides a bare-bones Sequence Collections API service.\n3. `/deployment`: Server configurations for demo instances and public deployed instances. There are also github workflows (in `.github/workflows`) that deploy the demo server instance from this repository.\n4. `/test_fasta` and `/test_api`: Dummy data and a compliance test, to test external implementations of the Refget Sequence Collections API.\n5. `/frontend`: a React seqcolapi front-end.\n\n\n## Deploy to AWS ECS\n\nTo deploy the public demo instance, you can either:\n\n1. **Create a GitHub release** - This triggers the `deploy_release_software.yml` workflow, which builds and pushes the Docker image to DockerHub. After that completes, it automatically triggers `deploy_primary.yml` to deploy to AWS ECS.\n\n2. **Manual dispatch** - You can manually trigger either workflow from the GitHub Actions tab.\n\nThis builds seqcolapi, pushes to DockerHub, and deploys to ECS.\n\n## Testing\n\n### Unit tests\n\n```bash\npytest\n```\n\n### Integration tests (requires Docker)\n\nIntegration tests run against an ephemeral PostgreSQL database in Docker:\n\n```bash\n./scripts/test-integration.sh\n```\n\nThis starts the test database, runs tests, and cleans up automatically.\n\n## Development and deployment: Backend\n\n### Store-backed (no database)\n\nThe store-backed seqcolapi uses a RefgetStore (local files) instead of PostgreSQL. This is the simplest way to run the API:\n\n#### Quick start\n\n```console\nbash deployment/store_demo_up.sh\n```\n\nThis will:\n- Build a local RefgetStore from test FASTA files\n- Run the store-backed seqcolapi with uvicorn\n- Block the terminal until you press Ctrl+C, which cleans up\n\nNo Docker or database required.\n\n#### Step-by-step\n\n1. Build a store from FASTA files:\n\n```console\npython data_loaders/demo_build_store.py test_fasta /tmp/refget_demo_store\n```\n\n2. Start the store-backed API:\n\n```console\nREFGET_STORE_PATH=/tmp/refget_demo_store uvicorn seqcolapi.main:store_app --reload --port 8100\n```\n\n#### Remote store\n\nTo run against a remote (S3) store:\n\n```console\nREFGET_STORE_URL=https://example.com/store uvicorn seqcolapi.main:store_app --port 8100\n```\n\n### DB-backed (PostgreSQL)\n\nIf you need a database-backed instance (e.g., for mutable data, advanced queries), use the DB-backed workflow. In a moment I'll show you how to do these steps individually, but if you're in a hurry, the easy way to get a development API running for testing is to just use my very simple shell script like this (no data persistence, just loads demo data):\n\n```console\nbash deployment/demo_up.sh\n```\n\nThis will:\n- populate env vars\n- launch postgres container with docker\n- run the refget service with uvicorn\n- load up the demo data\n- block the terminal until you press Ctrl+C, which will shut down all services.\n\n### Step-by-step process (DB-backed)\n\nAlternatively, if you want to run each step separately to see what's really going on, start here.\n\n#### Setting up a database connection\n\nFirst configure a database connection through environment variables. Choose one of these:\n\n```\nsource deployment/local_demo/local_demo.env # local demo (see below to create the database using docker)\nsource deployment/seqcolapi.databio.org/production.env # connect to production database\n```\n\nIf you're using the `local_demo`, then use docker to launch a local postgres database service like this:\n\n```\ndocker run --rm --name refget-postgres -p 127.0.0.1:5432:5432 \\\n  -e POSTGRES_PASSWORD \\\n  -e POSTGRES_USER \\\n  -e POSTGRES_DB \\\n  -e POSTGRES_HOST \\\n  postgres:17.0\n```\n\nIf you need to load test data into your server, then you have to install [gtars](https://docs.bedbase.org/gtars/) (with `pip install gtars`), a Python package for computing GA4GH digests. You can then load test data like this:\n\n```\nPYTHONPATH=. python data_loaders/load_demo_seqcols.py\n```\n\nor:\n\n```\nrefget add-fasta -p test_fasta/test_fasta_metadata.csv -r test_fasta\n```\n\n#### Running the seqcolapi API backend\n\nRun the demo `seqcolapi` service like this:\n\n```\nuvicorn seqcolapi.main:app --reload --port 8100\n```\n\n#### Running with docker\n\nTo build the docker file, first build the image from the root of this repository:\n\n```\ndocker build -f deployment/dockerhub/Dockerfile -t databio/seqcolapi seqcolapi\n```\n\nTo run in container:\n\n```\nsource deployment/seqcolapi.databio.org/production.env\ndocker run --rm -p 8000:80 --name seqcolapi \\\n  --env \"POSTGRES_USER\" \\\n  --env \"POSTGRES_DB\" \\\n  --env \"POSTGRES_PASSWORD\" \\\n  --env \"POSTGRES_HOST\" \\\n  databio/seqcolapi\n```\n\n#### Deploying container to dockerhub\n\nUse the github action in this repo which deploys on release, or through manual dispatch.\n\n## Running the frontend\n\nOnce you have a backend running, you can run a frontend to interact with it\n\n### Local client with local server\n\n```\ncd frontend\nnpm i\nVITE_API_BASE=\"http://localhost:8100\" npm run dev\n```\n\n### Local client with production server\n\n```\ncd frontend\nnpm i\nVITE_API_BASE=\"https://seqcolapi.databio.org\" npm run dev\n```\n\n### Development with local WASM\n\nThe `/digest` feature uses [@databio/gtars](https://www.npmjs.com/package/@databio/gtars) for WASM-based FASTA processing. To use a local gtars-wasm build instead of the npm package:\n\n```\nLOCAL_GTARS=../../gtars/gtars-wasm/pkg npm run dev\n```\n\nThe `LOCAL_GTARS` env var should point to the `pkg/` directory of a built gtars-wasm package (run `wasm-pack build --target web` in gtars-wasm to build it).\n\n### gtars WASM API Reference\n\nThe streaming API handles files of any size:\n\n```javascript\nimport * as gtars from '@databio/gtars';\nawait gtars.default();  // Initialize WASM\n\n// Streaming API (for large files)\nconst handle = gtars.fastaHasherNew();\ngtars.fastaHasherUpdate(handle, chunk);  // Feed Uint8Array chunks\nconst result = gtars.fastaHasherFinish(handle);  // Get SeqColResult\n\n// Batch API (for small files)\nconst result = gtars.digestSeqcol(fastaBytes);\n```\n\nResult object:\n```typescript\ninterface SeqColResult {\n  digest: string;           // Collection digest (SHA512t24u)\n  names_digest: string;\n  sequences_digest: string;\n  lengths_digest: string;\n  n_sequences: number;\n  sequences: Array\u003c{\n    name: string;\n    length: number;\n    alphabet: string;       // dna2bit, dna3bit, etc.\n    sha512t24u: string;\n    md5: string;\n    description?: string;\n  }\u003e;\n}\n```\n\n### Deploying\n\n1. Ensure the [refget](https://github.com/refgenie/refget/) package master branch is as you want it.\n2. Deploy the updated [secqolapi](https://github.com/refgenie/seqcolapi/) app to dockerhub (using manual dispatch, or deploy on github release).\n3. Finally, deploy the instance with manual dispatch using the included GitHub action.\n\n## Developer notes\n\n### Models\n\nThe objects and attributes are represented as SQLModel objects in `refget/models.py`. To add a new attribute:\n\n1. create a new model. This will create a table for that model, etc.\n2. change the function that creates the objects, to populate the new attribute.\n\n## Example of loading reference fasta datasets:\n\n```\nrefget add-fasta -p ref_fasta.csv -r $BRICKYARD/datasets_downloaded/pangenome_fasta/reference_fasta\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frefgenie%2Frefget","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frefgenie%2Frefget","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frefgenie%2Frefget/lists"}