Installation

Installing Python

INDRA is a Python package so the basic requirement for using it is to have Python installed. Python is shipped with most Linux distributions and with OSX. INDRA works with both Python 2 and 3 (tested with 2.7 and 3.5).

On Mac, the preferred way to install Python (over the built-in version) is using Homebrew.

brew install python

On Windows, we recommend using Anaconda which contains compiled distributions of the scientific packages that INDRA depends on (numpy, scipy, pandas, etc).

Installing INDRA

Installing via Github

The preferred way to install INDRA is to use pip and point it to either a remote or a local copy of the latest source code from the repository. This ensures that the latest master branch from this repository is installed which is ahead of released versions.

To install directly from Github, do:

pip install git+https://github.com/sorgerlab/indra.git

Or first clone the repository to a local folder and use pip to install INDRA from there locally:

git clone https://github.com/sorgerlab/indra.git
cd indra
pip install .

Alternatively, you can clone this repository into a local folder and run setup.py from the terminal as

git clone https://github.com/sorgerlab/indra.git
cd indra
python setup.py install

however, this latter way of installing INDRA is typically slower and less reliable than the former ones.

Cloning the source code from Github

You may want to simply clone the source code without installing INDRA as a system-wide package.

git clone https://github.com/sorgerlab/indra.git

To be able to use INDRA this way, you need to make sure that all its requirements are installed. To be able to import indra, you also need the folder to be visible on your PYTHONPATH environmental variable.

Installing releases with pip

Releases of INDRA are also available via PyPI. You can install the latest released version of INDRA as

pip install indra

INDRA dependencies

INDRA depends on a few standard Python packages (e.g. rdflib, requests, objectpath). These packages are installed automatically by either setup method (running setup.py install or using pip).

Below we provide a detailed description of some extra dependencies that may require special steps to install.

PySB and BioNetGen

INDRA builds on the PySB framework to assemble rule-based models of biochemical systems. The pysb python package is installed by the standard install procedure. However, to be able to generate mathematical model equations and to export to formats such as SBML, the BioNetGen framework also needs to be installed in a way that is visible to PySB. Detailed instructions are given in the PySB documentation.

Pyjnius

To be able to use INDRA’s BioPAX API and optional offline reading via the REACH and Eidos APIs, an additional package called pyjnius is needed to allow using Java/Scala classes from Python. This is only strictly required in these input sources and the rest of INDRA will work without pyjnius.

1. Install JRE and JDK 8 from Oracle. Pyjnius is currently incompatible with Java 9, so make sure to get Java 8.

2. On Mac, install Legacy Java for OSX. If you have trouble installing it, you can try the following as an alternative. Edit

/Library/Java/JavaVirtualMachines/jdk1.8.0_74.jdk/Contents/Info.plist

(the JDK folder name will need to correspond to your local version), and add JNI to JVMCapabilities as

...
<dict>
    <key>JVMCapabilities</key>
    <array>
        <string>CommandLine</string>
        <string>JNI</string>
    </array>
...
  1. Set JAVA_HOME to your JDK home directory, for instance
export JAVA_HOME=/Library/Java/JavaVirtualMachines/jdk1.8.0_74.jdk/Contents/Home
  1. Then first install Cython (tested with version 0.28.1) followed by pyjnius (note that the released version of pyjnius does _not_ work with recent Cython versions, hence installation from Github is required). These need to be broken up into two sequential calls to pip install.
pip install cython
pip install git+https://github.com/kivy/pyjnius.git@1cbfef

Graphviz

Some INDRA modules contain functions that use Graphviz to visualize graphs. On most systems, doing

pip install pygraphviz

works. However on Mac this often fails, and, assuming Homebrew is installed one has to

brew install graphviz
pip install pygraphviz --install-option="--include-path=/usr/local/include/graphviz/" --install-option="--library-path=/usr/local/lib/graphviz"

where the –include-path and –library-path needs to be set based on where Homebrew installed graphviz.

Matplotlib

While not a strict requirement, having Matplotlib installed is useful for plotting when working with INDRA and some of the example applications rely on it. It can be installed as

pip install matplotlib

Optional additional dependencies

Some dependencies of INDRA are only needed by certain submodules or are only used in specialized use cases. These are not installed by default but are listed as “extra” requirements, and can be installed separately using pip. An extra dependency list (e.g. one called extra_list) can be installed as

pip install indra[extra_list]

You can also install all extra dependencies by doing

pip install indra --install-option="complete"

or

pip install indra[all]

In all of the above, you may replace indra with . (if you’re in a local copy of the indra folder or with the Github URL of the INDRA repo, depending on your installation method. See also the corresponding pip documentation for more information.

The table below provides the name and the description of each “extra” list of dependencies.

Extra list name Purpose
biopax BioPAX input processing and Pathway Commons queries
bel BEL input processing and output assembly
trips_offline Offline reading with local instance of TRIPS system
reach_offline Offline reading with local instance of REACH system
eidos_offline Offline reading with local instance of Eidos system
geneways Genewayas reader input processing
sofia SOFIA reader input processing
bbn BBN reader input processing
sbml SBML model export through the PySB Assembler
machine Running a local instance of a “RAS machine”
explanation Finding explanatory paths in rule-based models
aws Accessing AWS compute and storage resources
graph Assembling into a visualizing Graphviz graphs
plot Create and display plots

Configuring INDRA

Various aspects of INDRA, including API keys, dependency locations, and Java memory limits, are parameterized by a configuration file that lives in ~/.config/indra/config.ini. The default configuration file is provided in indra/resources/default_config.ini, and is copied to ~/.config/indra/config.ini when INDRA starts if no configuration already exists. Every value in the configuration can also be set as an environment variable: for a given configuration key, INDRA will first check for an environment variable with that name and if not present, will use the value in the configuration file. In other words, an environment variable, when set, takes precedence over the value set in the config file.

Configuration values include:

  • REACHPATH: The location of the JAR file containing a local instance of the REACH reading system
  • EIDOSPATH: The location of the JAR file containing a local instance of the Eidos reading system
  • SPARSERPATH: The location of a local instance of the Sparser reading system (path to a folder)
  • DRUMPATH: The location of a local installation of the DRUM reading system (path to a folder)
  • NDEX_USERNAME, NDEX_PASSWORD: Credentials for accessing the NDEx web service
  • ELSEVIER_API_KEY, ELSEVIER_INST_KEY: Elsevier web service API keys
  • BIOGRID_API_KEY: API key for BioGRID web service (see http://wiki.thebiogrid.org/doku.php/biogridrest)
  • INDRA_DEFAULT_JAVA_MEM_LIMIT: Maximum memory limit for Java virtual machines launched by INDRA
  • SITEMAPPER_CACHE_PATH: Path to an optional cache (a pickle file) for the SiteMapper’s automatically obtained mappings.