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Characterizing Multi-omic Data in Systems Biology

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Systems Analysis of Human Multigene Disorders

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 799))

Abstract

In today’s biology, studies have shifted to analyzing systems over discrete biochemical reactions and pathways. These studies depend on combining the results from scores of experimental methods that analyze DNA; mRNA; noncoding RNAs, DNA, RNA, and protein interactions; and the nucleotide modifications that form the epigenome into global datasets that represent a diverse array of “omics” data (transcriptional, epigenetic, proteomic, metabolomic). The methods used to collect these data consist of high-throughput data generation platforms that include high-content screening, imaging, flow cytometry, mass spectrometry, and nucleic acid sequencing. Of these, the next-generation DNA sequencing platforms predominate because they provide an inexpensive and scalable way to quickly interrogate the molecular changes at the genetic, epigenetic, and transcriptional level. Furthermore, existing and developing single-molecule sequencing platforms will likely make direct RNA and protein measurements possible, thus increasing the specificity of current assays and making it possible to better characterize “epi-alterations” that occur in the epigenome and epitranscriptome. These diverse data types present us with the largest challenge: how do we develop software systems and algorithms that can integrate these datasets and begin to support a more democratic model where individuals can capture and track their own medical information through biometric devices and personal genome sequencing? Such systems will need to provide the necessary user interactions to work with the trillions of data points needed to make scientific discoveries. Here, we describe novel approaches in the genesis and processing of such data, models to integrate these data, and the increasing ubiquity of self-reporting and self-measured genomics and health data.

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Acknowledgments

This work was supported by the National Institutes of Health grants 1R01HG006798, 2R44HG005297, and 1R01NS076465. GeneSifter® is a registered trademark of PerkinElmer Inc.

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Authors and Affiliations

  1. Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY, USA

    Christopher E. Mason

  2. The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY, USA

    Christopher E. Mason

  3. Digital World Biology, 2442 NW Market St., PMB 160, Seattle, WA, 98107, USA

    Sandra G. Porter & Todd M. Smith

  4. PerkinElmer, 100 W. Harrison St. NT 330, Seattle, WA, 98119, USA

    Todd M. Smith

Authors
  1. Christopher E. Mason
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  2. Sandra G. Porter
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  3. Todd M. Smith
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Corresponding authors

Correspondence to Christopher E. Mason or Todd M. Smith .

Editor information

Editors and Affiliations

  1. University of Chicago, Chicago, Illinois, USA

    Natalia Maltsev

  2. University of Chicago, Chicago, Illinois, USA

    Andrey Rzhetsky

  3. University of Chicago, Chicago, Illinois, USA

    T. Conrad Gilliam

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© 2014 Springer Science+Business Media New York

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Mason, C.E., Porter, S.G., Smith, T.M. (2014). Characterizing Multi-omic Data in Systems Biology. In: Maltsev, N., Rzhetsky, A., Gilliam, T. (eds) Systems Analysis of Human Multigene Disorders. Advances in Experimental Medicine and Biology, vol 799. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8778-4_2

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