Update: MOOMIN is now also available on Julia: https://github.com/htpusa/moominJL
MOOMIN (Mathematical explOration of Omics data on a MetabolIc Network) is a tool for analysing differential expression data. It takes as its input a metabolic network and the results of a DE analysis: a posterior probability of differential expression and a (logarithm of a) fold change for a list of genes. It then forms a hypothesis of a metabolic shift, determining for each reaction its status as "increased flux", "decreased flux", or "no change". These are expressed as colours: red for an increase, blue for a decrease, and grey for no change. See the paper for full details: https://doi.org/10.1093/bioinformatics/btz584
MOOMIN runs in Matlab (developed in R2016a) and relies on the COBRA Toolbox (developed with v2.0, tested with v3.0). Additionally, a MILP-solver compatible with COBRA is needed. To obtain COBRA and how to use it: https://opencobra.github.io/cobratoolbox/stable/
In order to use MOOMIN, you need DE results obtained using Bayesian methods. In other words, a posterior probability of differential expression (PPDE) is needed instead of the more common p-value. You also need a metabolic network of the organism under study. You can read an SBML-file (.xml) using the COBRA-function "readSBML" or you can download a Matlab-structure containing a COBRA model directly if one is available.
COBRA Toolbox needs to be in the Matlab path.
A MILP-solver needs to be set for COBRA.
The file "example.m" is a Matlab script that contains an example pipeline. It can be run with simply "example". Note that the script will clear your Matlab workspace.
The E.coli core network used in the example was obtained from http://systemsbiology.ucsd.edu/Downloads/EcoliCore
If you are experiencing long running times, try switching off 'stoichiometry'. This should make the MILP easier to solve.
You can export your results in different formats using the writeMoominOutput function:
-The option 'full' prints a tab-delimited file with the most information.
-The 'json' option with 'string' off can be used to visualise results using Escher.