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Integrated omics: tools, advances and future approaches

Misra, Biswapriya B, Langefeld, Carl, Olivier, Michael, Cox, Laura A·2019·Journal of Molecular Endocrinology
DOI 10.1530/jme-18-0055
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Abstract

With the rapid adoption of high-throughput omic approaches to analyze biological samples such as genomics, transcriptomics, proteomics and metabolomics, each analysis can generate tera- to peta-byte sized data files on a daily basis. These data file sizes, together with differences in nomenclature among these data types, make the integration of these multi-dimensional omics data into biologically meaningful context challenging. Variously named as integrated omics, multi-omics, poly-omics, trans-omics, pan-omics or shortened to just 'omics', the challenges include differences in data cleaning, normalization, biomolecule identification, data dimensionality reduction, biological contextualization, statistical validation, data storage and handling, sharing and data archiving. The ultimate goal is toward the holistic realization of a 'systems biology' understanding of the biological question. Commonly used approaches are currently limited by the 3 i's - integration, interpretation and insights. Post integration, these very large datasets aim to yield unprecedented views of cellular systems at exquisite resolution for transformative insights into processes, events and diseases through various computational and informatics frameworks. With the continued reduction in costs and processing time for sample analyses, and increasing types of omics datasets generated such as glycomics, lipidomics, microbiomics and phenomics, an increasing number of scientists in this interdisciplinary domain of bioinformatics face these challenges. We discuss recent approaches, existing tools and potential caveats in the integration of omics datasets for development of standardized analytical pipelines that could be adopted by the global omics research community.

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Metadata

Authors
Misra, B. B., Langefeld, C., Olivier, M., & Cox, L. A.
Affiliations
  • Wake Forest University Health Sciences (US)
  • Texas Biomedical Research Institute (US)
Corresponding author
Cox, Laura A
ORCID
Venue
Journal of Molecular Endocrinology
Publisher
Bioscientifica
Published
2019-01
Volume
62
Issue
1
Pages
R21-R45
ISSN
  • 0952-5041 (ISSN-L)
  • 1479-6813
DOI
10.1530/jme-18-0055
OpenAlex
W2884682196
Cited by
686
References
191
Citations by year
2026: 120 · 2025: 139 · 2024: 103 · 2023: 84 · 2022: 77 · 2021: 78 · 2020: 59 · 2019: 26
FWCI
3.98
Type
journal article
Language
en
Open access
closed
Locations
  • Journal of Molecular Endocrinology (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Social Sciences › Arts and Humanities › History and Philosophy of Science › Interdisciplinary Research and Collaboration
Keywords
multi-omics integration, data normalization, data cleaning, dimensionality reduction, systems biology, data analysis pipeline, data archiving, data sharing, glycomics, lipidomics, microbiomics, phenomics, high-throughput omics data

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