Mummichog version 1.0.10

Mummichog version 1.0.10 is now in standard Python package index, available at Pypi. For older standalone versions, see "version 1.0.9".

To get the install tool pip, follow instruction at https://pip.pypa.io/en/stable/installing/. Please note that for Mac OS and Linux, Python is shipped by default. Things can be more complicated on MS Windows, for which we highly recommend to install Anaconda 2 (note: mummchog is in Python 2, not tested for Python 3), a scientific Python distribution. Anaconda is good to have anyway.

Once you have pip, get mummichog1 by pip install mummichog1

In a command line window, type mummichog1 and mummichog will return the help message.
To analyze your data in file myData.txt, type mummichog1 -f myData.txt -o myResult

Input format

A tab-delimited text file is used as input. One feature per line. Any rows starting with '#' will be skipped. Each line has to contain the following tab-delimited numeric fields: m/z, retention time, p-value, statistic score

Example of input file (a test data file, right click to save): 

            mz      rtime   p-value t-score
            186.0185697     463     0.000149751400132       3.82
            279.1773473     90      0.000399613326314       3.56
            344.1330624     124     0.000998323061251       -3.31
            215.9641894     132     0.00105418285794        -3.29
            177.0323244     77     0.00121065359218        3.256
            296.0973768     135     0.00171645907855        -3.15
            527.3784209     593     0.00176815004959        -3.14

Output from version 1

A default run will generate a directory structure like this 

          1370958677.93.rhino9t/
            result.html
            mummichog.log
            tsv/
                InspectedNodes_ActivityNetwork.csv
                mcg_pathwayanalysis_rhino9t.csv
                mcg_pathwayanalysis_rhino9t.xlsx
                mcg_modularanalysis_rhino9t.csv
                mcg_modularanalysis_rhino9t.xlsx
                _tentative_featurematch_rhino9t.csv
                _tentative_featurematch_rhino9t.xlsx
            sif/
                ActivityNetwork.txt
                module_1.txt
                ...
            web/
                ...

A summary report is given in result.html, which can be viewed in modern web browsers (excluding IE 8 or older). Internet connection is required to link a visualization library.

Full tables, results from annotation, pathway analysis and network module analysis, are given under the tsv/ directory. Results are provided in two formats: tab-delimited (.tsv) and Excel (.xlsx).

Visualization

Web browser based visual The "result.html" visualizes the activity network and up to top 5 modules, through javascript based technologies, while software development continues.

Cytoscape and .txt Network/pathway can be described in .txt files. One can use Cytoscape (cytoscape.org) to visualize the .txt files. Cytoscape is a powerful tool to work on network graphs in a friendly graphic interface. Please refer to Cytoscape's guides for details.

Mummichog version 2 (testing)

The major change in version 2 is the enforcement of retention time in grouping isotopes/adducts. Other improvements include modular software design for better web service and inegration, tracking of user data, and adduct calculation more concious of chemical formula. Mummichog 2 test version is available at Pypi.

Once you have pip (see instruction under version 1), get mummichog by pip install mummichog

To analyze your data in file myData.txt, type mummichog -f myData.txt -o myResult. Input format is the same as for version 1 above.

Output from version 2

A default run will generate a directory structure like this 

          1370958677.93.rhino9t/
            result.html
            tables/
                ListOfEmpiricalCompounds.csv
                mcg_pathwayanalysis_rhino9t.csv
                mcg_pathwayanalysis_rhino9t.xlsx
                mcg_modularanalysis_rhino9t.csv
                mcg_modularanalysis_rhino9t.xlsx
                userInputData.txt
            figures/
                network_modules/
                    ...
                mcg_pathwayanalysis_rhino9t.pdf
                plot_mcg_pathwayanalysis_rhino9t.pdf
                ...
                ...

The curent and future of Mummichog

Current features include

  • computing significantly enriched metabolic pathways;
  • identifying significant modules in the metabolic network;
  • visualization of top networks in web browser
  • visualization that also plugs into Cytoscape
  • tentative annotations
  • metabolic models for different species through plugins

Future directions include

  • Standalone web service (under development)
  • Adding more metabolic models and for more species (ongoing)
  • Tools of integrating multiple -omics data types
  • Adding supports for more MS technologies, e.g. MS^n, GC-MS, IM.

Please let us know if you would like to share ideas or request features.