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Listed are the download links to each dependency, however most OSes have a package manager or binaries that can be easily installed. Most of the below links describe alternate download and install methods.

On Mac OS X, Homebrew is the recommended way to install most of these of these libraries.

• Python 2.6+
• Ruby 1.8.7+
• RubyGems 1.3+
• NodeJS 0.8+
• Redis 2.6+
• PostgreSQL 9.2+
• Memcached
• Ruby Sass gem

Install the Sass gem:

gem install sass

Install the Bourbon gem:

gem install bourbon

Note, the INSTALL file contains instructions for setting up a server running RedHat Enterprise Linux Server 6.3.

• nginx
• supervisord

SolveBio provides easy integration with external datasets such as ClinVar, OMIM, dbSNP, and PubMed. It is currently integrated into the variant resource, and populates a portion of the variant details view in the Varify web client.

SolveBio is currently in Private Beta, but Varify users can get access by signing up at solvebio.com.

To enable SolveBio within Varify, first install the Python package:

pip install solvebio

Then, make sure that the SOLVEBIO_API_KEY Django setting is set either via an environment variable (see global_settings.py) or explicitly in your local_settings.py. You can find your API key from your account page on the SolveBio website.

Distribute, Pip and virtualenv are required. To check if you have them:

which pip easy_install virtualenv

If nothing prints out, install the libraries corresponding to the commands below:

Watch out for sudo! The root user $PATH most likely does not include /usr/local/bin. If you did not install Python through your distro's package manager, use the absolute path to the new Python binary to prevent installing the above libraries with the wrong version (like Python 2.4 on CentOS 5), e.g. /usr/local/bin/python2.7.

curl http://python-distribute.org/distribute_setup.py | python
curl https://raw.github.com/pypa/pip/master/contrib/get-pip.py | python
pip install virtualenv

Create your virtualenv:

virtualenv varify-env
cd varify-env
. bin/activate

Clone the repo:

git clone https://github.com/cbmi/varify.git
cd varify

Install the requirements:

pip install -r requirements.txt

Start the postgres server. This may look something like:

initdb /usr/local/var/postgres -E utf8

pg_ctl -D /usr/local/var/postgres -l /usr/local/var/postgres/server.log start

Create the varify database, you might first want to make sure you are a user

createuser --user postgres -s -r yourusername
createdb varify

Start memcached

memcached -d

Start redis

redis-server /usr/local/etc/redis.conf

If you are on a Mac, you will need to start postfix to allow SMTP:

sudo postfix start

Initialize the Django and Varify schemas

./bin/manage.py syncdb
./bin/manage.py migrate

Then either start the built-in Django server:

./bin/manage.py runserver

or run a uwsgi process:

uwsgi --ini server/uwsgi/local.ini --protocol http --socket 127.0.0.1:8000 --check-static _site

• build - builds and initializes all submodules, compiles SCSS and optimizes JavaScript
• watch - watches the SCSS files in the background for changes and automatically recompiles the files
• unwatch - stops watching the SCSS files
• sass - one-time explicit recompilation of SCSS files

• deploy:[<branch>@]<commit> - deploy a specific Git commit or tag

local_settings.py is intentionally not versioned (via .gitignore). It should contain any environment-specific settings and/or sensitive settings such as passwords, the SECRET_KEY and other information that should not be in version control. Defining local_settings.py is not mandatory but will warn if it does not exist.

Sass is awesome. SCSS is a superset of CSS so you can use as much or as little SCSS syntax as you want. It is recommended to write all of your CSS rules as SCSS, since at the very least the Sass minifier can be taken advantage of.

Execute the following commands to begin watching the static files and collect the files (using Django's collectstatic command):

make sass collect watch

Note, the sass target is called first to ensure the compiled files exist before attempting to collect them.

The following describes the steps to execute the loading pipeline, the performance of the pipeline, and the process behind it.

We have provided a set of test data to use to test the load pipeline or use as sample data when first standing up your Varify instance. To use the test data, run the commands below.

wget https://github.com/cbmi/varify-demo-data/archive/0.1.tar.gz -O varify-demo-data-0.1.tar.gz
tar -zxf varify-demo-data-0.1.tar.gz
gunzip varify-demo-data-0.1/CEU.trio.2010_03.genotypes.annotated.vcf.gz

At this point, the VCF and MANIFEST in the varify-demo-data-0.1 directory are ready for loading in the pipeline. You can use the varify-demo-data-0.1 directory as the argument to the samples queue command in the Queue Samples step below if you want to just load this test data.

Since the pipeline can take a while to load large collections(see Performance section below), you may want to consider following the Tmux steps to attach/detach to/from the load process.

Tmux is like screen, just newer. It is useful for detaching/reattaching sessions with long running processes.

New Session

tmux

Existing Session

tmux attach -t 0 # first session

source bin/activate

For this example, we will assume you have redis-server running on localhost:6379 against the database with index 0. If you have redis running elsewhere simply update the settings below with the address info and DB you wish to use. Open your local_settings.py file and add the following setting:

RQ_QUEUES = {
    'default': {
        'HOST': 'localhost',
        'PORT': 6379,
        'DB': 0,
    },
    'samples': {
        'HOST': 'localhost',
        'PORT': 6379,
        'DB': 0,
    },
    'variants': {
        'HOST': 'localhost',
        'PORT': 6379,
        'DB': 0,
    },
}

Optionally specify a directory, otherwise it will recursively scan all directories defined in the VARIFY_SAMPLE_DIRS setting in the Varify project.

./bin/manage.py samples queue [directory]

You can technically start as many of each type for loading data in parallel, but this may cause undesired database contention which could actually slow down the loading process. A single worker for variants is generally preferred and two or three are suitable for the default type.

./bin/manage.py rqworker variants &
./bin/manage.py rqworker default &

Note, these workers will run forever, if there is only a single sample being loaded, the --burst argument can be used to terminate the worker when there are no more items left in the queue.

You can monitor the workers and the queues using the rq-dashboard or rqinfo. Information on setting up and using those services can be found here.

After the batch of samples have been loaded, a two more commands need to be executed to update the annotations and cohort frequencies. These are performed post-load for performance reasons.

./bin/manage.py variants load --evs --1000g --sift --polyphen2 > variants.load.txt 2>&1 &
./bin/manage.py samples allele-freqs > samples.allele-freqs.txt 2>&1 &

• File size: 610 MB
• Variant count: 1,794,055

Iteration over flat file (no parsing) with batch counting (every 1000)

• Time: 80 seconds
• Memory: 0

Iteration over VCF parsed file using PyVCF

• Time: 41 minutes (extrapolated)
• Memory: 246 KB

1. Fill Queue
2. Spawn Worker(s)
3. Consume Job(s)
   • Validate Input
   • (work)
   • Validate Output
   • Commit

The COPY command is a single statement which means the data being loaded is all or nothing. If multiple samples are being loaded in parallel, it is likely they will have overlapping variants.

To prevent integrity errors, workers will need to consult one or more centralized caches to check if the current variant has been addressed already. If this is the case, the variant will be skipped by the worker.

This incurs a second issue in that downstream jobs that depend on the existence of some data that does not yet exist because another worker has not yet committed it's data. In this case, non-matches will be queued up in the deferred queue that can be run at a later time, after the default queue is empty or in parallel with the default queue.