Mathiomica: an integrative platform for dynamic omics
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Abstract
Multiple omics data are rapidly becoming available, necessitating the use of new methods to integrate different technologies and interpret the results arising from multimodal assaying. The MathIOmica package for Mathematica provides one of the first extensive introductions to the use of the Wolfram Language to tackle such problems in bioinformatics. The package particularly addresses the necessity to integrate multiple omics information arising from dynamic profiling in a personalized medicine approach. It provides multiple tools to facilitate bioinformatics analysis, including importing data, annotating datasets, tracking missing values, normalizing data, clustering and visualizing the classification of data, carrying out annotation and enumeration of ontology memberships and pathway analysis. We anticipate MathIOmica to not only help in the creation of new bioinformatics tools, but also in promoting interdisciplinary investigations, particularly from researchers in mathematical, physical science and engineering fields transitioning into genomics, bioinformatics and omics data integration.
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PyIOmica includes algorithms adapted from MathIOmica, now made available as Python open source code, and expands graphical utilities for visualization of categorized temporal data and network representation of time series, extending previous developments to an open-source, community-accessible platform for data science.
“PyIOmica includes optimized new algorithms adapted from MathIOmica (Mias et al., 2016; which runs on the proprietary Mathematica platform), now made available as Python open source code for all users, and additionally expands extensively graphical utilities for visualization of categorized temporal data, and network representation of time series.”
From PyIOmica: longitudinal omics analysis and trend identification (2020) · cites this paper as Mias et al., 2016
Temporal trends are automatically discovered using periodogram and autocorrelation calculations based on a Lomb-Scargle transformation algorithm, which properly accounts for missing points and/or unevenly sampled data.
“Temporal trends are automatically discovered using periodogram and autocorrelation calculations based on a Lomb-Scargle transformation algorithm (Mias et al., 2016), which properly accounts for missing points and/or unevenly sampled data.”
From PyIOmica: longitudinal omics analysis and trend identification (2020) · cites this paper as Mias et al., 2016
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Self-citation
- Authors
- Mias, G. I., Yusufaly, T., Roushangar, R., Brooks, L. R. K., Singh, V. V., & Christou, C.
- Affiliations
- Michigan State University (US)
- University of Southern California (US)
- Sisters of Mercy Health System (US)
- Corresponding author
- Mias, George I.
- Venue
- Scientific Reports
- Published
- 2016-11-24
- Volume
- 6
- Issue
- 1
- Pages
- 37237-37237
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC5121649
- OpenAlex
- W2951891881
- Cited by
- 27
- References
- 60
- Citations by year
- 2026: 1 · 2025: 1 · 2023: 1 · 2022: 3 · 2021: 2 · 2020: 4 · 2019: 8 · 2018: 6 · 2017: 1
- FWCI
- 2.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Michigan State University
- Australian Abalone Growers Association
- National Institutes of Health
- College of Engineering, Michigan State University
- National Human Genome Research Institute (K99HG007065, R00HG007065)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genetics, Bioinformatics, and Biomedical Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- Mathematica, Wolfram Language, multi-omics integration, multimodal data, dynamic profiling, personalized medicine, data annotation, data normalization, clustering, pathway analysis, ontology annotation, bioinformatics software
- MeSH terms
- Humans, Software, Computational Biology, Proteomics, Molecular Sequence Annotation, Gene Ontology
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