Genecentric is a package that performs two main tasks:
  1. Implement the BPM generation algorithm with pruning described in
     Leiserson et al, 2010. Parallelization is automatically exploited
     there is more than one CPU.
  2. Perform GO enrichment on BPMs using the JSON-RPC API of Funcassociate 2.0.
     Documentation of the API can be found here: 
     http://llama.mshri.on.ca/funcassociate/documentation

DEPENDENCIES
============
As of right now, Python 2.6 or greater is required. The main cause of this is 
use of Python's json library, which was added in 2.6. If there's demand, we can 
see about working around this.

Also, if you're not running Python 2.7, you'll need to install 'argparse' from 
PyPI. (Should be as easy as `easy_install-2.6 argparse`.)

There are no other dependencies other than the Internet for GO enrichment.

INSTALLATION
============
For *nix based systems:
  Download the distribution at 
  http://bcb.cs.tufts.edu/genecentric/download_linux.php

  Extract it, change into the directory, and install:

    tar zxf genecentric*.tar.gz
    cd genecentric*
    python2 setup.py install

  Instead of using 'python2 setup.py install', you could also install it with
  easy_install like so:

    easy_install-2.7 ./

  This method is particularly useful if you don't have root access on the 
  machine your installing it to.

For Windows based systems:
  Download the distribution at 
  http://bcb.cs.tufts.edu/genecentric/download_win.php
  
  Run the executable. ???

EXECUTION
=========
You should now be able to run an example using some data that come with the 
package. (It should be installed to Python's installation prefix, typically 
'/usr' on *nix based machines. If on Windows, it may be in your Python 
distribution folder.)

If you can't find the data on your system, you can download it here:
http://bcb.cs.tufts.edu/genecentric/files/data/
Remember, this is Yeast EMAP data from the Krogan Lab. You may want to find
your own data if this data isn't applicable. To see where we got this example
data from, see:
http://bcb.cs.tufts.edu/genecentric/files/data/README

Back to the example:

  export DATA="/usr/share/genecentric/data/"
  genecentric-bpms -m 20 -e $DATA/essentials $DATA/yeast_emap.gi output.bpm

And to run GO enrichment (requires an Internet connection):

  genecentric-go -e $DATA/essentials $DATA/yeast_emap.gi output.bpm enrich.gobpm

Which should give a list of generated BPMs in 'output.bpm' and enrichment
results in 'enrich.gobpm' in the current directory.

OTHER NOTES
===========

Some terminology on file formats used by Genecentric:

It is convention to use the file format names on the left as extensions to
corresponding files. i.e., 'yeast_emap.gi'. This is not required, however.

  gi      A 'genetic interaction' file is formatted as tab-delimited with three 
          columns. The first column contains the first gene name, the second 
          column contains the second gene name, and the third column contains 
          the interaction score.

  bpm     A 'bpm' file is a tab-delimited file where each row represents a 
          module in a between-pathway module. The first column is the BPM and 
          module identifier, i.e. 'BPM5/Module1', and the rest of the columns 
          are the gene names belonging to that particular module.

  gobpm   A 'gobpm' file contains the GO enrichment results of every module in 
          a 'bpm' file. Each module begins with a line starting with '>' and 
          followed by the BPM/module identifier, i.e. '> BPM5/Module1'.
          The line immediately following that is a tab-delimited list of genes 
          in that module.
          The lines following that, up to the next '> ...' line, are the GO 
          enrichment results. Namely, each row represents a GO term that has 
          been enriched for some subset of this module's genes.
          Each line representing a GO term contains the following information 
          in order and in tab-delimited form: the p-value, the ratio of the 
          number of genes in the query associated with this GO term to the 
          total number of genes in the query, the GO name, and finally, the 
          subset of genes in the module that have been enriched for this GO 
          term. (The subset of genes is delimited by single space characters.)