This is the Python implementation of the cMonkey algorithm based on the original R implementation by David J. Reiss, Institute for Systems Biology.

A complete set of documentation for installation and running of cMonkey is on the wiki. There are also developer and user discussion groups.

Please report all bugs or other issues using the issue tracker. Please direct any and all questions to either the developer or user discussion groups.

cMonkey₂ has been tested and runs on all tested recent versions of Linux (including debian-based [Ubuntu, Mint, Debian] and RPM-based [CentOS, Fedora]) and recent versions of Mac OS X. Additional dependencies include:

• Developed and tested with Python 2.7.x
• scipy >= 0.9.0
• numpy >= 1.6.0
• biopython >= 1.63
• MySQLdb >= 1.2.3
• BeautifulSoup >= 3.2.0
• R >= 2.14.1
• rpy2 >= 2.2.1
• MEME 4.3.0 or >= 4.8.1
• csh (for running MEME) for the human setup, Weeder 1.4.2 is needed

for running the unit tests (optional):

• python-xmlrunner

for running the interactive monitoring and visualization web application (optional):

• CherryPy 3
• Jinja2
• python-routes

./run_tests.sh

In general, you should be able to run cmonkey2 on microbial gene expression ratios with

./cmonkey.py --organism <organism-code> --ratios <tab separated file of gene expressions>

The file can be either in your file system or a web URL.

After the program was started, a log file will be written in cmonkey.log. You can see all available options with

./cmonkey.py --help

There is a startup script for cMonkey to run the current integrated system

./cmonkey.py --organism hal --ratios example_data/hal/halo_ratios5.tsv

python cmviewer/main.py

To run cMonkey on human data, run the following code with your own <ratios.tsv> file

./cmonkey.py --organism hsa --ratios <ratios.tsv> --string <stringFile> --rsat_organism Homo_sapiens_GRCh37 --rsat_URL http://rsat.sb-roscoff.fr/ --rsat_features protein_coding --nooperons

Running cMonkey on Human data is somewhat difficult because neither the string database nor the RSAT database has human data cleanly entered. Here are the steps for a sucessful python cMonkey run on human

1. Make a gene interaction file. The example data file mentioned above was generated from Biogrid around 10/6/14.
2. Find an RSAT mirror that has .raw chromose files and feature files. In the above example, we use Homo_sapiens_ensembl_74_GRCh37 from the main RSAT database. To annotate these we use 'protein_coding.tab' and 'protein_coding_names.tab'. In principal, other annotation files such as 'processed_transcript' would work just as well.
3. Adjust the upstream region searched, and perhaps modify the code to search for know TF and miRNA motifs rather than de-novo motifs. NOTE: Modiyfing the motif search step is non-trivial.