A Generalized Open Source Extensile Framework for Omics Analysis

MathIOmica is based on the Wolfram Language and PyIOmica is written in Python. These *IOmica programs provide a framework for graphical, numerical and symbolic work for omics analyses. The code is cross-platform, open source  and includes full integrated documentation.

Multi-Omics Integration

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

Scientific Community Resources

Designing MathIOmica and PyIOmica 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.


*IOmica: MathIOmica and PyIOmica offer unique platforms for omics  analysis and integration.

See also

  1. MathIOmica's github page

  1. PyIOmica'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



Open Source Tools for Computation

Tools available on the gmiaslab GitHub repository

  1. MathIOmica-MSViewer
    1. MathIOmica-MSViewer GitHub Page A mass spectrometry spectra viewer written in the Wolfram Language.

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 Latest MathIOmica Release

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

You can also download previous releases from MathIOmica's github page.

Download Latest PyIOmica Release

  • PyIOmica full modules, source code and documentation
  • Gene Ontology dictionary
  • KEGG pathway dictionary
  • integrative Personal Omics Profiling example data using saliva

    Relevant Publications with MathIOmica and PyIOmica

    *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

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 and PyIOmica are released under an MIT License. If you use the packages 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.
  2. S. Domanskyi, C. Piermarocchi, G.I. Mias*, PyIOmica: longitudinal omics analysis and trend identification, Bioinformatics, , btz896, (2019), 10.1093/bioinformatics/btz896 .

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603 Wilson Rd, Biochemistry Rm 120
East Lansing, MI, 48824
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