A Docker container to wrap IGV.js
Visualizations in Refinery are provided by Docker containers. The link between Refinery and the Docker containers is django_docker_engine. A container may provide back-end services to a visualization, or after start up it may just serve static html and js, as is the case here. In either case the input files are provided in a directory which is mounted on a predetermined path.
The IGV instance will look for genome reference files in a particular S3 bucket. To add reference files for a new assembly,
check out get-reference-genomes and run genome-to-s3.sh
.
Clone the repository, make sure Docker is installed, and then:
pip install -r requirements.txt
python test.py
After the tests run successfully the containers used for testing are killed.
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The docker_igv_js container can be run by pointing to valid
input.json
file by url:docker run -p 8080:80 -e INPUT_JSON_URL=https://github.com/raw/refinery-platform/docker_igv_js/master/input_fixtures/good/input.json gehlenborglab/docker_igv_js
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Or an
input.json
file can be constructed and passed to the container directly through the envvar:INPUT_JSON
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docker run -p 8080:80 -e INPUT_JSON=' { "node_info": { "id-1": { "file_url": "https://github.com/raw/igvteam/igv/master/test/data/bed/intervalTest.bed", "node_solr_info": {"name": "track-name"} } }, "parameters": [ {"name": "Genome Build", "value": "hg19"} ] }' gehlenborglab/docker_igv_js
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Visit http://localhost:8080
Note: The current intentions of this project are to satisfy the needs of the refinery-platform, which currently has a strict way of passing input data to the underlying application (as seen above). Decoupling this tool, and providing a more general purpose solution has been talked of here.
Successful Github tags and PRs will prompt Travis to push the built image to Dockerhub. For a new version number:
git tag v0.0.x && git push origin --tags