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Genome Engineering Tool

Project description

Edge keeps structural changes between a genome and child genomes derived from it. A user creates a modified genome by applying a sequence-based operation, such as homologous recombination, to a parent genome. Users can annotate or make corrections to sequences on a genome; Edge automatically applies the changes to the appropriate regions on the derived genomes. Edge does this efficiently: making a change on a parent genome takes O(1) and is automatically propagated to the modified genomes.

Edge uses O(D) amount of storage for each modified genome, where D is the number of differences between a modified genome and its parent. The current implementation additionally keeps a cache of annotations to base pair numbers, but this cache is soft-data and is invalidated and re-built on demand.

A modified genome can be re-created by re-applying operations to a new genome (think git rebase). Currently, however, annotating a genome is not an operation. Also, applying the same operation to a genome twice results in a single child genome, not two.

Edge provides UIs to look at operations and changes and APIs for making changes. Edge can export genome sequences and annotations as GFF files. While Edge comes with a simple UI for browsing features and sequences, the UI is primitive compared to other specialized applications.

Try it using Docker

  • Use docker-compose:

The Docker environment is defined in docker-compose.yml. Use the edge service for your commands.

To start the edge server:

docker-compose up

Then check it out in your browser: http://localhost:9000/edge/#/genomes .

To import a genome, use:

docker-compose run edge python src/ import_gff 'Saccharomyces cerevisiae' example/sc_s288c.gff

To run a shell inside the Edge container:

docker-compose run --rm edge bash
  • Alternatively, you can use the Makefile:

The Makefile holds all the commands necessary for managing the server and database, both in usage and development. Run make without arguments to see a list of commmands.

Any of these make targets can be run directly from a shell inside a container:

you@localhost:edge$ docker-compose run --rm edge bash
# Now you're inside the Docker container
root@docker-image:/usr/src/edge# make test

Furthermore, any target can have -ext added to it. Commands that end in -ext are meant to be run externally to the image, i.e., from the host system.

For example, to start the edge server:

make start-ext

To run a shell:

make bash-ext

To import a genome as an example:

make add-s288c-ext

If the edge app is already running in a container, or you don’t want to rebuild the image yet, you can change -ext to -ext_fast, which will run the make target in a new container without trying to rebuild the image.

Try it without Docker

On your own machine, Construct your virtual environment and pip-install dependencies (use requirements.txt).

To start a server, first update src/server/ to use either sqlite or MySQL. For MySQL, create the appropriate databse. Then,

make migrate
(cd example; gunzip ecoli-mg1655.gff.gz; gunzip yeast.gff.gz)
python src/ import_gff 'E. coli MG1655' example/ecoli-mg1655.gff
python src/ import_gff 'Saccharomyces cerevisiae' example/yeast.gff
make run

Then set your browser to http://localhost:8000/edge/. Note the port is different than the Docker case

If you need NCBI BLAST or Primer3 support, you’ll need to make sure the packages are installed on your system. Debian and Ubuntu distributions provide binary versions of both of these packages.

Depending on where the NCBI BLAST tools and Primer3 are installed, you will probably need to tell edge where to find them, using the following environment variables:

NCBI_BIN_DIR       # Path to directory holding ncbi binaries, e.g. /usr/bin
PRIMER3_BIN        # Path to primer3 binary, e.g. /usr/bin/primer3_core
PRIMER3_CONFIG_DIR # Path to primer3 config directory, e.g. etc/primer3_config/

Then, to set up the edge BLAST db, from the src subdirectory,

python build_edge_blastdb

Editing data

You can edit genome and fragment metadata, such as name, notes, circular attributes, from the Django admin. Create a Django admin superuser, (see the superuser make target), then set your browser to the /admin/ endpoint of wherever you are running your dev server.

Deploying to production

Do not use the Dockerfile as-is for production, or the make run task. Django’s runserver command is not meant to run a production server. Instead, you’ll need to spin up a production WSGI server and run the Django projct with that, with your own settings. In this situation, it’s better to simply install the edge-genome python package on your deployed system and add it to your deployment Django server’s installed_apps setting. The package is designed so that, when built, it already contains all of the javascript assets compiled in their final state.

Development, testing, and package release

Running tests

When developing locally, you can run tests in the controlled environment of the docker container from your local machine with make test-all-ext. Make sure you’ve run the migrations at least once before doing this. If your server is already running, and you want to run tests from the host machine in a separate container, use make test-all-ext_fast. Or just keep a container up and run the tests from inside it.

Static files

Note that edge uses webassets for compilation of static assets. These assets are not automatically compiled (because the integration of that with Django is flaky). Instead, compile assets after cahnging them with make build_assets. To constantly recompile them, see make watch.

Static dependencies are managed with Bower. (Eventually to be replaced with npm/webpack). Dependencies are downloaded before the python package is built so Python package consumers already have all required JavaScript.


Edge is versioned semantically. Continuous integration builds are done automatically on all branches through Travis CI, and tagged commits to master are automatically released to PyPI. To release a new version, bump the version number with the appropriate severity of the changes (major, minor, or patch), and push the resulting tagged commits to the GitHub remote repo:

you@localhost:edge$ docker-compose run --rm edge make bump/patch-ext # Or bump/major, or bump/minor
you@localhost:edge$ git push --tags origin master

If you cannot push to master directly, do the same thing on a new branch and submit a pull request.

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