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The MathIOmica Toolbox: General Analysis Utilities for Dynamic Omics Datasets.

George I Mias1,2, Minzhang Zheng2

  • 1Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan.

Current Protocols in Bioinformatics
|December 19, 2019
PubMed
Summary

MathIOmica, a bioinformatics package, analyzes longitudinal omics data using time-series classification. It identifies temporal trends in transcriptomics data, aiding individualized health monitoring and adverse event detection.

Keywords:
gene expressionlongitudinalomicspersonalized medicinetime series

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Omics experiments generate complex longitudinal data.
  • Analyzing temporal trends in omics data is crucial for understanding biological processes.
  • Existing tools may struggle with missing data and uneven sampling in time-series omics datasets.

Purpose of the Study:

  • Introduce MathIOmica, a Wolfram Language package for analyzing longitudinal omics data.
  • Demonstrate MathIOmica's capabilities in time-series classification and biological significance assessment.
  • Facilitate the analysis of individualized health monitoring data and detection of health-related temporal trends.

Main Methods:

  • Utilizes Mathematica's notebook interface for data import, quality control, and normalization.
  • Employs spectral methods (periodograms, autocorrelations) for temporal behavior classification.
  • Performs Gene Ontology and pathway enrichment analyses for biological significance.

Main Results:

  • Successfully classified temporal trends in a transcriptomics (RNA-sequencing) dataset.
  • Identified significant temporal trends using autocorrelation and validated with null distributions.
  • Visualized trends via heatmaps and assessed biological relevance through enrichment analyses.

Conclusions:

  • MathIOmica provides a robust framework for analyzing longitudinal omics data.
  • The package effectively handles challenges like missing data and uneven sampling.
  • Enables the discovery of biologically significant temporal patterns relevant to health monitoring.