Refine.bio harmonizes petabytes of publicly available biological data into ready-to-use datasets for cancer researchers and AI/ML scientists.
This README file is about building and running the refine.bio project source code.
Refine.bio currently has four sub-projects contained within this repo:
- common Contains code needed by both
- foreman Discovers data to download/process and manages jobs.
- workers Runs Downloader and Processor jobs.
- infrasctructure Manages infrastructure for Refine.bio.
Table of Contents
- Running Locally
- Cloud Deployment
refinebio uses a
based workflow. New features should be developed on new feature branches, and
pull requests should be sent to the
dev branch for code review. Merges into
master happen at the end of sprints, and tags in
master correspond to
To run Refine.bio locally, you will need to have the prerequisites installed onto your local machine. This will vary depending on whether you are developing on a Mac or a Linux machine. Linux instructions have been tested on Ubuntu 16.04 or later, but other Linux distributions should be able to run the necessary services. Microsoft Windows is currently unsupported by this project.
Note: The install_all.sh script will configure a git pre-commit hook to auto-format your python code. This will format your code in the same way as the rest of the project, allowing it to pass our linting check.
The easiest way to run Refine.bio locally is to run
to install all of the necessary dependencies. As long as you are using a recent
version of Ubuntu or macOS it should work. If you are using another version of
Linux it should still install most of the dependencies as long as you give the
INSTALL_CMD environment variable, but some dependencies may be
named differently in your package manager than in Ubuntu's.
The following services will need to be installed:
- Python3 and Pip:
sudo apt-get -y install python3-pip
- Docker: Be sure to follow the post installation steps so Docker does not need sudo permissions.
- Nomad can be installed on Linux clients with
- pip3 can be installed on Linux clients with
sudo apt-get install python3-pip
- black can be installed on Linux clients with
pip3 install black
Instructions for installing Docker, Terraform, and Nomad can be found by
following the link for each service. jq, and iproute2 can be installed via
sudo apt-get install jq iproute2 shellcheck.
The following services will need to be installed:
Once Homebrew is installed, the other required applications can be installed by running:
brew install iproute2mac nomad terraform jq black shellcheck.
Many of the computational processes running are very memory intensive. You will need to raise the amount of virtual memory available to Docker from the default of 2GB to 12GB or 24GB, if possible.
./scripts/create_virtualenv.sh to set up the virtualenv. It will activate the
for you the first time. This virtualenv is valid for the entire
repo. Sub-projects each have their own environments managed by their
containers. When returning to this project you should run
source dr_env/bin/activate to reactivate the virtualenv.
refinebio also depends on Postgres and Nomad. Postgres can be
run in a local Docker container, but Nomad must be run on your
To start a local Postgres server in a Docker container, use:
Then, to initialize the database, run:
If you need to access a
psql shell for inspecting the database, you can use:
or if you have
psql installed this command will give you a better shell experience:
source scripts/common.sh && PGPASSWORD=mysecretpassword psql -h $(get_docker_db_ip_address) -U postgres -d data_refinery
Similarly, you will need to run a local Nomad service in development mode.
However if you run Linux and you have followed the installation instructions, you can run Nomad with:
sudo -E ./scripts/run_nomad.sh
(Note: This step may take some time because it downloads lots of files.)
Nomad is an orchestration tool which Refine.bio uses to run
Processor jobs. Jobs are queued by sending a message to
the Nomad agent, which will then launch a Docker container which runs
the job. If address conflicts emerge, old Docker containers can be purged
docker container prune -f.
The common sub-project contains common code which is
depended upon by the other sub-projects. So before anything else you
should prepare the distribution directory
common/dist with this
(Note: This step requires the postgres container to be running and initialized.)
Note: there is a small chance this might fail with a
can't stat, error. If this happens, you have
to manually change permissions on the volumes directory with
sudo chmod -R 740 volumes_postgres
then re-run the migrations.
One of the API endpoints is powered by ElasticSearch. ElasticSearch must be running for this functionality to work. A local ElasticSearch instance in a Docker container can be executed with:
And then the ES Indexes (akin to Postgres 'databases') can be created with:
The end to end tests require a separate Nomad client to be running so that the tests can be run without interfering with local development. The second Nomad client can be started with:
sudo -E ./scripts/run_nomad.sh -e test
To run the entire test suite:
(Note: Running all the tests can take some time, especially the first time because it downloads a lot of files.)
You can use the following to get the current status of nomad when running in the test environment.
$ source scripts/common.sh $ set_nomad_test_address $ nomad status
Running the end to end tests is tricky because Nomad's needs to pull images from docker with our code.
We have a docker image registry that runs locally, but you'll need to update it with different images in order to make the code run.
./scripts/prepare_image.sh can be used to prepare the images before pushing them.
$ ./scripts/prepare_image.sh -i downloaders -d localhost:5000 $ docker push localhost:5000/dr_downloaders:latest $ ./scripts/prepare_image.sh -i no_op -d localhost:5000 $ docker push localhost:5000/dr_no_op:latest
That's for the images
no_op, the same need to be executed for the other images:
If you want to debug the status of a specific nomad job you can use:
$ nomad status NO_OP_0_2048/dispatch-1567796915-3d7c7c87 $ nomad status f9c1345b $ nomad logs f9c1345b
f9c1345b is the allocation id that it's returned in
These tests will also be run continuously for each commit via CircleCI.
For more granular testing, you can just run the tests for specific parts of the system.
To just run the API tests:
To just run the common tests:
To just run the foreman tests:
To just run the workers tests:
If you only want to run tests with a specific tag, you can do that too. For example, to run just the salmon tests:
./workers/run_tests.sh -t salmon
All of our worker tests are tagged, generally based on the Docker image required to run them. Possible values for worker test tags are:
- qn (short for quantile normalization)
In addition to following pep8, python files must also conform to the formatting style enforced by black.
black is a highly opinionated auto-formatter.
black's highly opinionated style is a strict sub-set of pep8.)
The easiest way to conform to this style is to run
black . --line-length=100.
This will auto-format your code.
./scripts/install_all.sh script will install a pre-commit git hook that will run this formatter on every commit you make locally. Under the hood this uses pre-commit, which you can also install directly by running
pip3 install pre-commit & pre-commit install. Then, if you want to run
pre-commit without making a git commit, you can use
pre-commit run --all-files.
black see the installation instructions.
Any Pull Requests that do not conform to the style enforced by
black will be flagged by our continous integration and will not be accepted until that check passes.
All user-facing scripts have been linted with
shellcheck for common
warnings and POSIX-correctness. If a script is user-facing, it should ideally
be POSIX-compliant and have the extension
.sh, but if bashisms are necessary
it should have the extension
.bash. To install
shellcheck, you can run
apt-get install shellcheck or
brew install shellcheck. Then, you can lint
During development, you make encounter some occasional strangeness. Here's some things to watch out for:
- Since we use multiple Docker instances, don't forget to
- If builds are failing, increase the size of Docker's memory allocation. (Mac only.)
- If Docker images are failing mysteriously during creation, it may
be the result of Docker's
Docker.rawfile filling. You can prune old images with
docker system prune -a.
- If it's killed abruptly, the containerized Postgres images can be left in an unrecoverable state. Annoying.
We have created some utilities to help us keep R stable, reliable, and from periodically causing build errors related to version incompatibilites.
The primary goal of these is to pin the version for every R package that we have.
The R package
devtools is useful for this, but in order to be able to install a specific version of it, we've created the R script
There is another gotcha to be aware of should you ever need to modify versions of R or its packages.
In Dockerfiles for images that need the R language, we install apt packages that look like
It's unclear why the version for these is so weird, but it was determined by visiting the package list here: https://cran.revolutionanalytics.com/bin/linux/ubuntu/xenial/
If it needs to be updated then a version should be selected from that list.
Additionally there are two apt packages, r-base and r-base-core, which seem to be very similar except that r-base-core is slimmed down some by not including some additional packages. For a while we were using r-base, but we switched to r-base-core when we pinned the version of the R language because the r-base package caused an apt error.
Once you've built the
common/dist directory and have
the Nomad and Postgres services running, you're ready to run jobs.
To run the API you also need the elasticsearch service running.
There are three kinds of jobs within Refine.bio.
The API can be run with:
Surveyor Jobs discover samples to download/process along with
recording metadata about the samples. A Surveyor Job should queue
Downloader Jobs to download the data it discovers.
The Surveyor can be run with the
script. The first argument to this script is the type of Surveyor Job
to run, which will always be
Details on these expected arguments can be viewed by running:
./foreman/run_surveyor.sh survey_all -h
The Surveyor can accept a single accession code from any of the source data repositories (e.g., Sequencing Read Archive, ArrayExpress, Gene Expression Omnibus):
./foreman/run_surveyor.sh survey_all --accession <ACCESSION_CODE>
Example for a GEO experiment:
./foreman/run_surveyor.sh survey_all --accession GSE85217
Example for an ArrayExpress experiment:
./foreman/run_surveyor.sh survey_all --accession E-MTAB-3050 # AFFY ./foreman/run_surveyor.sh survey_all --accession E-GEOD-3303 # NO_OP
Transcriptome indices are a bit special. For species within the "main" Ensembl division, the species name can be provided like so:
./foreman/run_surveyor.sh survey_all --accession "Homo sapiens"
However for species that are in other divisions, the division must follow the species name after a comma like so:
./foreman/run_surveyor.sh survey_all --accession "Caenorhabditis elegans, EnsemblMetazoa"
The possible divisions that can be specified are:
- Ensembl (this is the "main" division and is the default)
If you are unsure what division a species falls into, unfortunately the only way to tell is go to check ensembl.com. (Although googling the species name + "ensembl" may work pretty well.)
You can also supply a newline-deliminated file to
survey_all which will
dispatch survey jobs based on accession codes like so:
./foreman/run_surveyor.sh survey_all --file MY_BIG_LIST_OF_CODES.txt
The main foreman job loop can be started with:
This must actually be running for jobs to move forward through the pipeline.
Sequence Read Archive
When surveying SRA, you can supply either run accession codes (e.g.,
codes beginning in
ERR) or study accession codes
Run example (single read):
./foreman/run_surveyor.sh survey_all --accession DRR002116
Run example (paired read):
./foreman/run_surveyor.sh survey_all --accession SRR6718414
./foreman/run_surveyor.sh survey_all --accession ERP006872
Ensembl Transcriptome Indices
Building transcriptome indices used for quantifying RNA-seq data requires us to retrieve genome information from Ensembl. The Surveyor expects a species' scientific name in the main Ensembl division as the accession:
./foreman/run_surveyor.sh survey_all --accession "Homo Sapiens"
TODO: Update once this supports organisms from multiple Ensembl divisions
Downloader Jobs will be queued automatically when
discover new samples. However, if you just want to queue a
yourself rather than having the Surveyor do it for you, you can use the
./workers/tester.sh run_downloader_job --job-name=<EXTERNAL_SOURCE> --job-id=<JOB_ID>
./workers/tester.sh run_downloader_job --job-name=SRA --job-id=12345
./workers/tester.sh run_downloader_job --job-name=ARRAY_EXPRESS --job-id=1
Or for more information run:
Processor Jobs will be queued automatically by successful
However, if you just want to run a
Processor Job without yourself without having
Downloader Job do it for you, the following command will do so:
./workers/tester.sh -i <IMAGE_NAME> run_processor_job --job-name=<JOB_NAME> --job-id=<JOB_ID>
./workers/tester.sh -i affymetrix run_processor_job --job-name=AFFY_TO_PCL --job-id=54321
./workers/tester.sh -i no_op run_processor_job --job-name=NO_OP --job-id=1
./workers/tester.sh -i salmon run_processor_job --job-name=SALMON --job-id=1
./workers/tester.sh -i transcriptome run_processor_job --job-name=TRANSCRIPTOME_INDEX_LONG --job-id=1
Or for more information run:
Creating Quantile Normalization Reference Targets
If you want to quantile normalize combined outputs, you'll first need to create a reference target for a given organism. This can be done in a production environment with the following:
nomad job dispatch -meta ORGANISM=DANIO_RERIO CREATE_QN_TARGET
To create QN targets for all organisms, do so with the dispatcher:
nomad job dispatch QN_DISPATCHER
This will at some point move to the foreman and then it will take a list of organisms to create QN targets for.
Creating species-wide compendia for a given species can be done in a production environment by running the following on the Foreman instance:
./run_management_command.sh create_compendia --organisms=DANIO_RERIO --svd-algorithm=ARPACK
or for a list of organisms:
./run_management_command.sh create_compendia --organisms=DANIO_RERIO,HOMO_SAPIENS --svd-algorithm=ARPACK
or for all organisms with sufficient data:
./run_management_command.sh create_compendia --svd-algorithm=ARPACK
Alternatively a compendium can be created which only includes quant.sf files by using the create_quantpentida command:
./run_management_command.sh create_quantpendia --organisms=DANIO_RERIO
Compendia jobs run on the smasher instance.
However they require a very large amount of RAM to be able to complete.
Our smasher instance does not generally have enough RAM to be able to run them, so if you need to run a smasher job you should temporarily increase the size of the smasher instance.
This can be done by changing the terraform variable
smasher_instance_type which can be found in
Select an AWS instance type that has enough RAM to run the compendia jobs.
At the time of writing, compendia jobs require 180GB of RAM and m5.12xlarge has 192GM of RAM so it is sufficiently large to run the jobs.
Running Tximport Early
Normally we wait until ever sample in an experiment has had Salmon run on it before we run Tximport. However Salmon won't work on every sample, so some experiments are doomed to never make it to 100% completion. Tximport can be run on such an experiment with:
nomad job dispatch -meta EXPERIMENT_ACCESSION=SRP009841 TXIMPORT
Note that if the experiment does not have at least 25 samples with at least 80% of them processed, this will do nothing.
Checking on Local Jobs
Note: The following instructions assume you have set the environment variable NOMAD_ADDR to include the IP address of your development machine. This can be done with:
source ./scripts/common.sh && export NOMAD_ADDR=http://$(get_ip_address):4646
To check on the status of a job, run:
It should output something like:
ID Type Priority Status Submit Date DOWNLOADER batch/parameterized 50 running 01/31/18 18:34:05 EST DOWNLOADER/dispatch-1517441663-4b02e7a3 batch 50 dead 01/31/18 18:34:23 EST PROCESSOR batch/parameterized 50 running 01/31/18 18:34:05 EST
The rows whose
PROCESSOR are the parameterized
jobs which are waiting to dispatch Refine.bio jobs. If you don't understand
what that means, don't worry about it. All you really need to do is select
one of the jobs whose ID contains
dispatch and whose
matches the time when the job you want to check on was run, copy that full ID
(in this case
DOWNLOADER/dispatch-1517437920-ae8b77a4), and paste it
after the previous command, like so:
nomad status DOWNLOADER/dispatch-1517441663-4b02e7a3
This will output a lot of information about that
Nomad Dispatch Job,
of which we're mostly interested in the section titled Allocations.
Here is an example:
Allocations ID Node ID Task Group Version Desired Status Created At b30e4edd fda75a5a jobs 0 run complete 01/31/18 18:34:23 EST
If you paste that after the original
nomad status command, like so:
nomad status b30e4edd
you'll see a lot of information about allocation, which probably isn't what you're interested in. Instead, you should run:
nomad logs -verbose b30e4edd
This command will output both the stderr and stdout logs from the container which ran that allocation. The allocation is really a Refine.bio job.
It can be useful to have an interactive Python interpreter running within the
context of the Docker container. The
scripts/run_shell.sh script has been provided
for this purpose. It is in the top level directory so that if you wish to
reference it in any integrations its location will be constant. However, it
is configured by default for the Foreman project. The interpreter will
have all the environment variables, dependencies, and Django configurations
for the Foreman project. There are instructions within the script describing
how to change this to another project.
Refine.bio requires an active, credentialed AWS account with appropriate permissions to create network infrastructure, users, compute instances and databases.
Deploys are automated to run via CirlceCI whenever a signed tag starting with a
v is pushed to either the
master branches (v as in version, i.e. v1.0.0).
Tags intended to trigger a staging deploy MUST end with
CircleCI runs a deploy on a dedicated AWS instance so that the Docker cache can be preserved between runs.
Instructions for setting up that instance can be found in the infrastructure/deploy_box_instance_data.sh script.
To trigger a new deploy, first see what tags already exist with
git tag --list | sort --version-sort
We have two different version counters, one for
dev and one for
master so a list including things like:
However you may see that the
dev counter is way ahead, because we often need more than one staging deploy to be ready for a production deploy.
This is okay, just find the latest version of the type you want to deploy and increment that to get your version.
For example, if you wanted to deploy to staging and the above versions were the largest that
git tag --list output, you would increment
v1.1.3-dev to get
Once you know which version you want to deploy, say
v1.1.4-dev, you can trigger the deploy with these commands:
git checkout dev git pull origin dev git tag -s v1.1.4-dev git push origin v1.1.4-dev
git tag -s v1.1.4-dev will prompt you to write a tag message; please try to make it descriptive.
We use semantic versioning for this project so the last number should correspond to bug fixes and patches, the second middle number should correspond to minor changes that don't break backwards compatibility, and the first number should correspond to major changes that break backwards compatibility.
Please try to keep the
master versions in sync for major and minor versions so only the patch version gets out of sync between the two.
Refine.bio uses a number of different Docker images to run different pieces of the system.
By default, refine.bio will pull images from the Dockerhub repo
If you would like to use images you have built and pushed to Dockerhub yourself you can pass the
-d option to the
To make building and pushing your own images easier, the
scripts/update_my_docker_images.sh has been provided.
-d option will allow you to specify which repo you'd like to push to.
If the Dockerhub repo requires you to be logged in, you should do so before running the script using
The -v option allows you to specify the version, which will both end up on the Docker images you're building as the SYSTEM_VERSION environment variable and also will be the docker tag for the image.
scripts/update_my_docker_images.sh will not build the dr_affymetrix image, because this image requires a lot of resources and time to build.
It can instead be built with
./scripts/prepare_image.sh -i affymetrix -d <YOUR_DOCKERHUB_REPO>.
WARNING: The affymetrix image installs a lot of data-as-R-packages and needs a lot of disk space to build the image.
It's not recommended to build the image with less than 60GB of free space on the disk that Docker runs on.
Once you have Terraform installed and your AWS account credentials installed, you're almost ready to deploy a dev stack.
The only thing remaining is to copy the RefinebioSSHKey from LastPass and save it to the file:
If you do not have access to this key in LastPass, ask another developer.
The correct way to deploy to the cloud is by running the
deploy.sh script. This script will perform additional
configuration steps, such as setting environment variables, setting up Nomad job specifications, and performing database migrations. It can be used from the
infrastructure directory like so:
./deploy.sh -u myusername -e dev -r us-east-1 -v v1.0.0 -d my-dockerhub-repo
This will spin up the whole system. It will usually take about 15 minutes, most of which is spent waiting for the Postgres instance to start.
The command above would spin up a development stack in the
us-east-1 region where all the resources' names would end with
All of the images used in that stack would come from
my-dockerhub-repo and would be tagged with
-e specifies the environment you would like to spin up. You may specify,
dev is meant for individuals to test infrastructure changes or to run large tests.
staging is to test the overall system before re-deploying to
To see what's been created at any time, you can:
terraform state list
If you want to change a single entity in the state, you can use
terraform taint <your-entity-from-state-list>
And then rerun
deploy.sh with the same parameters you originally ran it with.
Jobs can be submitted via Nomad, either from a server/client or a local machine if you supply a server address and have an open network ingress.
To start a job with a file located on the foreman docker image:
nomad job dispatch -meta FILE=NEUROBLASTOMA.txt SURVEYOR_DISPATCHER
or to start a job with a file located in S3:
nomad job dispatch -meta FILE=s3://data-refinery-test-assets/NEUROBLASTOMA.txt SURVEYOR_DISPATCHER
All of the different Refine.bio subservices log to the same AWS CloudWatch Log
Group. If you want to consume these logs, you can use the
awslogs tool, which
can be installed from
pip like so:
pip install awslogs
or, for OSX El Capitan:
pip install awslogs --ignore-installed six
awslogs is installed, you can find your log group with:
Then, to see all of the logs in that group for the past day, watching as they come in:
awslogs get <your-log-group> ALL --start='1 days' --watch
You can also apply a filter on these logs like so:
awslogs get <your-log-group> ALL --start='1 days' --watch --filter-pattern="DEBUG"
Or, look at a named log stream (with or without a wildcard.) For instance: (Unfortunately this feature seems to be broken at the moment: https://github.com/jorgebastida/awslogs/issues/158)
awslogs get data-refinery-log-group-myusername-dev log-stream-api-nginx-access-* --watch
will show all of the API access logs made by Nginx.
Dumping and Restoring Database Backups
Automatic snapshots are created automatically by RDS. Manual database dumps can be created by priveledged users with these instructions. Postgres versions on the host (I suggest the PGBouncer instance) must match the RDS instance version:
sudo add-apt-repository "deb http://apt.postgresql.org/pub/repos/apt/ $(lsb_release -sc)-pgdg main" wget --quiet -O - https://www.postgresql.org/media/keys/ACCC4CF8.asc | sudo apt-key add - sudo apt-get update sudo apt-get install postgresql-9.6
Archival dumps can also be provided upon request.
Dumps can be restored locally by copying the
backup.sql file to the
volumes_postgres directory, then executing:
docker exec -it drdb /bin/bash psql --user postgres -d data_refinery -f /var/lib/postgresql/data/backup.sql
This can take a long time (>30 minutes)!
A stack that has been spun up via
deploy.sh -u myusername -e dev can be taken down with
destroy_terraform.sh -u myusername -e dev -r us-east-1.
The same username and environment must be passed into
destroy_terraform.sh as were used to run
deploy.sh either via the -e and -u options or by specifying
TF_VAR_user so that the script knows which to take down.
Note that this will prompt you for confirmation before actually destroying all of your cloud resources.
Refine.bio is supported by Alex's Lemonade Stand Foundation, with some initial development supported by the Gordon and Betty Moore Foundation via GBMF 4552 to Casey Greene.
The table of contents for this README is generated using
doctoc can be installed with:
sudo npm install -g doctoc
doctoc is installed the table of contents can be re-generated with:
BSD 3-Clause License.