Information Flow Analysis in biological networks

This project's goal is to predict systemic effect of multiple gene perturbation, whether triggered by a drug or by a disease (such as cancer or disease with complex genetic background). It's main intended uses are prediction of drug toxicity of de-novo drugs due to a distributed off-target effect and linkage between a phenotype and a complex genotype.

It's main advantage is integration of quantitative computational predictions with prior biological knowledge and ability to integrate such diverse source of knowledge as databases, simulation, publication data (currently in dev) and expert knowledge.

The application is currently under development and in alpha stage. However, if you desire to use it, you are welcome to do so and fill in the tickets if you encounter any issues

The license is BSD, but in case of academic usage, please cite the url of this repository (I am currently writing a publication). The full API documentation is available at readthedocs.org.

If you are on Ubuntu 14.04: :

> sh ubuntu_14_04_setup.sh
> pip install git+https://github.com/chiffa/BioFlow.git

For more information, refer to the installation guide

Fire up the databases :

> /home/ank/neo4j-yeast/bin/neo4j start
> nohup /home/ank/mongodb/bin/mongod &

Setup environment :

> bioflow initialize --/home/ank/data_store
> bioflow downloaddbs
> bioflow setorg yeast
> bioflow loadneo4j

For more information about data and config files, refer to the data and database guide

Set the set of perturbed proteins on which we would want to base our analysis :

> bioflow setsource /home/ank/source_data/perturbed_proteins_ids.csv

Build network interfaces :

> bioflow extractmatrix --interactome
> bioflow extractmatrix --annotome

Perform the analysis:

> bioflow analyze --matrix interactome --depth 24 --processors 4
> bioflow analyze --matrix annotome --depth 24 --processors 4

The results of analysis will be available in the output folder, and printed out to the standard output.

For more details or usage as a library, refer to the usage guide.