State of the field in multi-omics research: from computational needs to data mining and sharing
Open accessAbstract
Multi-omics, variously called integrated omics, pan-omics, and trans-omics, aims to combine two or more omics data sets to aid in data analysis, visualization and interpretation to determine the mechanism of a biological process. Multi-omics efforts have taken center stage in biomedical research leading to the development of new insights into biological events and processes. However, the mushrooming of a myriad of tools, datasets, and approaches tends to inundate the literature and overwhelm researchers new to the field. The aims of this review are to provide an overview of the current state of the field, inform on available reliable resources, discuss the application of statistics and machine/deep learning in multi-omics analyses, discuss findable, accessible, interoperable, reusable (FAIR) research, and point to best practices in benchmarking. Thus, we provide guidance to interested users of the domain by addressing challenges of the underlying biology, giving an overview of the available toolset, addressing common pitfalls, and acknowledging current methods' limitations. We conclude with practical advice and recommendations on software engineering and reproducibility practices to share a comprehensive awareness with new researchers in multi-omics for end-to-end workflow.
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Other transcriptomics platforms require at least 35 samples per group to detect the same magnitude fold change at the same power and false discovery rate levels.
“This is as opposed to other (larger) transcriptomics platforms that would require at least 35 samples per group to detect the same magnitude fold change at the same power and false discovery rate levels (Krassowski et al., 2020).”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Krassowski et al., 2020
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References 91 · Cited by 351
Research works printed but not linked: 1
- Neyton L. Zheng X. Skouras C. Wilson A. B. Gutmann M. U. Yau C. (2019). Multiomic definition of generalizable endotypes in human acute pancreatitis. bioRxiv [Preprint]. 10.1101/539569 · DOI
- Authors
- Krassowski, M., Das, V., Sahu, S. K., & Misra, B. B.
- Affiliations
- Novo Nordisk (United States) (US)
- Corresponding author
- Misra, Biswapriya B.
- Venue
- Frontiers in Genetics
- Publisher
- Frontiers Media SA
- Published
- 2020-12-10
- Volume
- 11
- Pages
- 610798-610798
- ISSN
- 1664-8021 (ISSN-L)
- PMC
- PMC7758509
- OpenAlex
- W3112212292
- Cited by
- 351
- References
- 91
- Citations by year
- 2026: 46 · 2025: 61 · 2024: 80 · 2023: 82 · 2022: 68 · 2021: 14
- FWCI
- 3.92
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Genetics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Social Sciences › Decision Sciences › Information Systems and Management › Interdisciplinary Research and Collaboration
- Keywords
- multi-omics, multi-omics integration, trans-omics, deep learning, FAIR principles, data sharing, benchmarking, reproducibility, software engineering, data mining
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