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From Omics to Multi-Omics: A Review of Advantages and Tradeoffs

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Hayes, C. Nelson, Nakahara, Hikaru, Ono, Atsushi, Tsuge, Masataka, Oka, Shiro·2024·Genes
DOI 10.3390/genes15121551PMC PMC11675490
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Abstract

Bioinformatics is a rapidly evolving field charged with cataloging, disseminating, and analyzing biological data. Bioinformatics started with genomics, but while genomics focuses more narrowly on the genes comprising a genome, bioinformatics now encompasses a much broader range of omics technologies. Overcoming barriers of scale and effort that plagued earlier sequencing methods, bioinformatics adopted an ambitious strategy involving high-throughput and highly automated assays. However, as the list of omics technologies continues to grow, the field of bioinformatics has changed in two fundamental ways. Despite enormous success in expanding our understanding of the biological world, the failure of bulk methods to account for biologically important variability among cells of the same or different type has led to a major shift toward single-cell and spatially resolved omics methods, which attempt to disentangle the conflicting signals contained in heterogeneous samples by examining individual cells or cell clusters. The second major shift has been the attempt to integrate two or more different classes of omics data in a single multimodal analysis to identify patterns that bridge biological layers. For example, unraveling the cause of disease may reveal a metabolite deficiency caused by the failure of an enzyme to be phosphorylated because a gene is not expressed due to aberrant methylation as a result of a rare germline variant. Conclusions: There is a fine line between superficial understanding and analysis paralysis, but like a detective novel, multi-omics increasingly provides the clues we need, if only we are able to see them.

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  • Small changes in normalization, imputation, or similarity metrics can substantially alter network topology and derived biological insights.

    “Small changes in normalization, imputation, or similarity metrics can substantially alter network topology and derived biological insights316,317.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [316]

    Section 3.9, “Statistical Issues in Multi-Omics” (page unavailable), says normalization and missing-value handling matter and warns that apparent relationships may be statistical artefacts. It does not say that small changes in those choices—or in similarity metrics—substantially alter network topology or derived biological insights. (LLM, conf=0.76)

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Cited by

Sorted by citations

Authors
Hayes, C. N., Nakahara, H., Ono, A., Tsuge, M., & Oka, S.
Affiliations
Hiroshima University (JP)
Corresponding author
Hayes, C. Nelson
ORCID
Venue
Genes
Publisher
MDPI AG
Published
2024-11-29
Volume
15
Issue
12
Pages
1551
ISSN
2073-4425 (ISSN-L)
DOI
10.3390/genes15121551
PMC
PMC11675490
OpenAlex
W4404860874
Cited by
119
References
17
Citations by year
2026: 77 · 2025: 42
FWCI
4.87
Type
journal article
Language
en
Open access
Licence
Locations
  • Genes (publishedVersion, cc-by)
  • Preprints.org (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • 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 › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords
multi-omics, omics technologies, single-cell omics, single-cell RNA sequencing, multimodal data integration, cellular heterogeneity, next-generation sequencing, bioinformatics, epigenetic methylation, germline mutations, disease mechanisms
MeSH terms
Multiomics, Animals, Humans, Computational Biology, Genomics, Proteomics, Metabolomics, Single-Cell Analysis

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