A Generalized Open Source Extensile Framework for Omics Analysis

MathIOmica is based on the Wolfram Language.  This provides a framework for graphical, numerical and symbolic work for omics analyses. The code cross-platform, open source  and includes full integrated documentation.

Multi-Omics Integration

Omics analyses and integration requires extensive processing. MathIOmica provides a user-friendly framework for handling downstream analysis and visualization that is generalizable to multi-modal omics.

Scientific Community Resources

Designing MathIOmica required the creation of robust multi-omics datasets for time series analyses and network inference. Such sets are being made availabe to the scientific community on completion.


MathIOmica: a unique platform for omics  analysis and integration.  MathIOmica's current version: 1.1.3.

See also MathIOmica's github page.

"Personalized medicine is expected to benefit from combining genomic information with regular monitoring of physiological states by multiple high- throughput methods. Here, we present an integrative personal omics profile (iPOP), an analysis that combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14 month period. Our iPOP analysis revealed various medical risks, including type II diabetes. It also uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions. Extremely high-coverage genomic and transcriptomic data, which provide the basis of our iPOP, discovered extensive heteroallelic changes during healthy and diseased states and an unexpected RNA editing mechanism. This study demonstrates that longitudinal iPOP can be used to interpret healthy and disease states by connecting genomic information with additional dynamic omics activity."

From Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes

Cell, Volume 148, Issue 6, 1293-1307, 16 March 2012

Curated mapped subsets of the pilot iPOP data are utilized in MathIOmica for examples and documentation. The original raw data for the pilot iPOP study have been made publically available as follows:


snyderome contains local repository of iPOP data



Coming soon!

Stand-alone helper tools to aid in visualization

MathIOmica Manuals

MathIOmica utilizes in-built Mathematica Documentation. A printout of various documentation is provided below in pdf form.

  1. MathIOmica Guide
  2. MathIOmica Multi-Omics Example Tutorial
  3. MathIOmica Dynamic Transcriptome Example
  4. MathIOmica Function Manual

Notes and Primers

  1. George I. Mias, M. Snyder, Personal Genomes, Quantitative Dynamic Omics and Personalized Medicine, Quantitative Biology 1(1) (2013),
    doi:10.1007/s40484-013-0005-3   Offers examples of computational tools as an introduction to integrative dynamic omics.

Mathematica Resources

  1. Mathematica
  2. The Wolfram Language
  3. An Elementary Introduction to the Wolfram Language
  4. Stack Overflow has answers to a lot of programming questions.

Omics Resources

  1. ms-utils.org variety of Mass Spectrometry Utilities
  2. The Tuxedo Suite offers great tools for sequence analysis, such as Bowtie, TopHat and Cufflinks.
  3. NCBI tools


Download MathIOmica Full Package

  • MathIOmica full modules, source code and documentation
  • Gene Ontology dictionary
  • KEGG pathway dictionary
  • integrative Personal Omics Profiling example data
Alternative Download

You can also download the package from MathIOmica's github page.

    Relevant Publications with MathIOmica

    *corresponding author(s)
      MathIOmica Publications:
    1. G.I. Mias*, T. Yusufaly, R. Roushangar, L.R.K. Brooks, V.V. Singh, C. Christou, MathIOmica: An Integrative Platform for Dynamic Omics , Scientific Reports 6, 37237 (2016), doi: 10.1038/srep37237.
    2. R. Roushangar, G.I. Mias, MathIOmica-MSViewer: A Dynamic Viewer for Mass Spectrometry Files for Mathematica, Journal of Mass Spectrometry, 52: 315–318, (2017), doi: 10.1002/jms.3928.
      Publications using MathIOmica functionality:
    1. A. Marcobal, T. Yusufaly, S. Higginbottom, M. Snyder, J.L. Sonnenburg*, G. I. Mias*, Metabolome progression during early gut microbial colonization of gnotobiotic mice Scientific Reports 5, 11589; (2015)
      doi: 10.1038/srep11589

A Python version is now in development

Mapped RNA-Sequencing Data


Mapped Proteomics Mass Spectrometry Data


Aligned Metabolomics Mass Spectrometry Data


Data from multi-omics analysis (Raw and mapped) will be made available (Mias, Im et al. in preparation).

Mass Spectrometry Spectral Viewer, MathIOmica-MSViewer


How To Cite

MathIOmica is released under an MIT License. If you use the package please cite the relevant publications:

  1. G.I. Mias*, T. Yusufaly, R. Roushangar, L.R.K. Brooks, V.V. Singh, C. Christou, MathIOmica: An Integrative Platform for Dynamic Omics Scientific Reports 6, 37237 (2016), doi:10.1038/srep37237.

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Contact Us

G. Mias Lab
603 Wilson Rd, Biochemistry Rm 120
East Lansing, MI, 48824
MathIOmica Support