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A reference implementation of the ga4gh API

Project description

GA4GH Reference Implementation

This is a prototype for the GA4GH reference client and server applications. It is under heavy development, and many aspects of the layout and APIs will change as requirements are better understood. If you would like to help, please check out our list of issues!

Our aims for this implementation are:


The main goal of this implementation is to provide an easy to understand and maintain implementation of the GA4GH API. Design choices are driven by the goal of making the code as easy to understand as possible, with performance being of secondary importance. With that being said, it should be possible to provide a functional implementation that is useful in many cases where the extremes of scale are not important.


The code is written in Python for maximum portability, and it should be possible to run on any modern computer/operating system (Windows compatibility should be possible, although this has not been tested). Our coding guidelines specify using a subset of Python 3 which is backwards compatible with Python 2 following the current best practices. The project currently does not yet support Python 3, as support for it is lacking in several packages that we depend on. However, our eventual goal is to support both Python 2 and 3.

Ease of use

The code follows the Python Packaging User Guide. Specifically, pip is used to handle python package dependencies (see below for details). This allows for easy installation of the ga4gh reference code across a range of operating systems.

Configuration file and data hierarchy

The GA4GH reference server is a Flask application and uses the standard Flask configuration file mechanisms. An example configuration file might look like:

DATA_SOURCE = "/path/to/data/root"
# TODO other example config

Data is input to the GA4GH server as a directory hierarchy, in which the structure of data to be served is represented by the filesystem. For now, we support only one dataset, but this will be generalised to multiple datasets in later releases. An example data layout might be:

            # More VCFs
            # More VCFs
            # TODO fill in details for read data.


There are three different types of installation that we deal with here: Deployment, Client tools and Development installations. A deployment installation is a production server, usually using Apache or another web server on a dedicated machine. A client tools installation creates a sandbox in which a user can easily try out the GA4GH client utilities, and run queries against arbitrary servers (specifically, any server running the correct version of the GA4GH API; not necessarily this implementation). Finally, a development installation is a local installation used for either development of GA4GH reference server itself, or client applications depending on the reference Python client libraries.


To deploy on Apache on Debian/Ubuntu platforms, do the following.

  • Install some basic pre-requisite packages:

    $ sudo apt-get install python-dev zlib1g-dev libdb-dev
  • Install Apache and mod_wsgi, and enable mod_wsgi:

    $ sudo apt-get install apache2 libapache2-mod-wsgi
    $ sudo a2enmod wsgi
  • Create the python egg cache directory, and make it writable by www-data:

    $ sudo mkdir /var/cache/apache2/python-egg-cache
    $ sudo chown www-data:www-data /var/cache/apache2/python-egg-cache/
  • Create a directory to hold the GA4GH server code, configuration and data. For convenience, we make this owned by the current user (but make sure all the files are world-readable).:

    $ sudo mkdir /srv/ga4gh
    $ sudo chown $USER /srv/ga4gh
    $ cd /srv/ga4gh
  • Make a virtualenv, and install the ga4gh package:

    $ virtualenv ga4gh-server-env
    $ source ga4gh-server-env/bin/activate
    $ pip install --pre ga4gh  # We need the --pre because ga4gh is pre-release
    $ deactivate
  • Download and unpack the example data:

    $ wget
    $ tar -zxf ga4gh-example-data.tar.gz
  • Create the WSGI file at /srv/ga4gh/application.wsgi and write the following contents:

    from ga4gh.frontend import app as application
    import ga4gh.frontend as frontend
  • Create the configuration file at /srv/ga4gh/, and write the following contents:

    DATA_SOURCE = "/srv/ga4gh/ga4gh-example-data"
  • Configure Apache. Edit the file /etc/apache2/sites-enabled/000-default.conf and insert the following contents towards the end of the file (within the <VirtualHost:80>...</VirtualHost> block):

    WSGIDaemonProcess ga4gh python-path=/srv/ga4gh/ga4gh-server-env/lib/python2.7/site-packages python-eggs=/var/cache/apache2/python-egg-cache
    WSGIScriptAlias /ga4gh /srv/ga4gh/application.wsgi
    <Directory /srv/ga4gh>
        WSGIProcessGroup ga4gh
        WSGIApplicationGroup %{GLOBAL}
        Require all granted
  • Restart Apache:

    $ sudo service apache restart
  • Test the installation by pointing a web-browser at the root URL; for example, to test on the installation server use:

    $ links http://localhost/ga4gh

    To test the API, make a development installation and try running some Example client queries against the server

These instructions are just one way in which we can achieve the same thing. There are any number of different ways in which we can set up a WSGI application under Apache, which may be preferable in different installations. (In particular, the Apache configuration here may be specific to Ubuntu 14.04, where this was tested.) See the mod_wsgi documentation <> for more details. These instructions are also specific to Debian/Ubuntu and different commands and directory structures will be required on different platforms.

The server can be deployed on any WSGI compliant web server. See the instructions in the Flask documentation <> for more details on how to deploy on various other servers.


If you are encountering difficulties getting the above to work, it is helpful to turn on debugging output. Do this by adding the following line to your config file:

DEBUG = True

When an error occurs, the details of this will then be printed to the web server’s error log (in Apache on Debian/Ubuntu, for example, this is /var/log/apache2/error.log).

Client tools


  • Python 2.7,

  • Berkeley DB together with include and lib files (version 4.8 or higher),

  • Virtualenv (or another python sandboxing tool) is highly recommended.

General installation procedure:

  • Install Berkeley DB (version 4.8 or higher) using your system’s preferred package manager, see the wormtable help page for platform-specific details.

  • (On MacOS X, make sure the LDFLAGS and CFLAGS environment variables are set to include the lib and include directories for the Berkeley DB install of your choice. The wormtable help page cited above provides more detailed instructions, or see the System specific install examples section for an example install on that platform.)

  • Create a python sandbox directory using virtualenv, preferably not inside the ga4gh server directory. For an good introduction to using virtualenv, see the Python Guide page. On some systems, you may need to specify the –no-site-packages option to ensure a clean dependency install. For example, to create a sandbox named testenv:

    $ virtualenv --no-site-packages testenv
  • Make the virtualenv sandbox created above active:

    $ source testenv/bin/activate
  • cd to the ga4gh server directory, and load the dependencies via pip:

    $ cd [your ga4gh server directory]
    $ pip install -r requirements.txt
  • Finally, run the install script, and run nosetests to confirm the install:

    $ python install
    $ nosetests

A successfull install should result in a clean run of all the tests, resulting in a line of dots followed by OK. If this still isn’t working, you may want to check the System specific install examples section.

To run the server on this example dataset, follow the steps on installing the server, then download and unpack the example data

$ wget
$ tar -zxvf ga4gh-example-data.tar.gz

An easier way to download and upack the data is to run the following script, which will do these steps for you:

$ python scripts/

You can now run the server, which will by default serve variants from the sets in the downloaded datafile:

$ ga4gh_server

To change the data that is served, a configuration file can be specified using the -f <config_file> command line argument. Run:

$ ga4gh_server --help

for details on the options for this program.

Example client queries

To run queries against this server, we can use the ga4gh_client program; for example, here we run the variants/search method over the 1000g_2013.wt variant set, where the reference name is 1 and we only want calls returned for call set ID HG03279:

$ ga4gh_client variants-search http://localhost:8000/v0.5.1 -V 1000g_2013.wt -r 1 -c HG03279 | less -S

We can also query against the variant name; here we return the variant that has variant name rs75454623:

$ ga4gh_client variants-search http://localhost:8000/v0.5.1 -V 1000g_2013.wt -r 1 -n rs75454623  | less -S
System specific install examples

MacOS X (with MacPorts):

$ sudo port install db48
$ export CFLAGS=-I/opt/local/include/db48/  LDFLAGS=-L/opt/local/lib/db48/
$ cd [some working directory outside the ga4gh server directory tree]
$ virtualenv --no-site-packages testenv
$ source testenv/bin/activate
$ cd [your ga4gh server directory]
$ pip install -r requirements.txt
$ python install
$ nosetests

TODO Append examples of installs (using package managers if possible, no dependency installs from source) on the target platform of your choice.


A development installation of the GA4GH reference implementation is a local copy of the server repo, along with all of the tools required for development. Please ensure that all the system requirements (as listed above) are installed, and clone a local copy of the repo. Install all of the required Python libraries into your Python user installation:

$ pip install -r requirements.txt --user

All of the command line interface utilities have local scripts that simplify development: for example, we can run the local version of the ga2sam program by using:

$ python

To run the server locally in development mode, we can use the script, e.g.:

$ python

will run a server using the default configuration. This default configuration expects a data hierarchy to exist in the ga4gh-example-data directory. This default configuration can be changed by providing a (fully qualified) path to a configuration file (see the Configuration file and data hierarchy section for details).


The code for the project is held in the ga4gh package, which corresponds to the ga4gh directory in the project root. Within this package, the functionality is split between the client, server, protocol and cli modules. The cli module contains the definitions for the ga4gh_client and ga4gh_server programs.

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