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REVIEW article
Front. Immunol.
Sec. Microbial Immunology
Volume 15 - 2024 |
doi: 10.3389/fimmu.2024.1439434
This article is part of the Research Topic Immune Mechanisms of Protection Against Mycobacterium tuberculosis View all 11 articles
A Practical Guide to FAIR Data Management in the Age of Multi-OMICS and AI
Provisionally accepted- 1 Department of Immunology and Infectious Diseases, School of Public Health, Harvard University, Boston, Massachusetts, United States
- 2 BioMicro Center, Massachusetts Institute of Technology, Cambridge, MA, USA, Cambridge, United States
- 3 Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
- 4 Ragon Institute, Cambridge, Massachusetts, United States
- 5 Department of Biological Engineering, School of Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
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Multi-cellular biological systems, including the immune system, are highly complex, dynamic, and adaptable. Systems biologists aim to understand such complexity at a quantitative level. However, these ambitious efforts are often limited by access to a variety of high-density intra-, extra- and multi-cellular measurements resolved in time and space and across a variety of perturbations. The advent of automation, OMICs and single-cell technologies now allows high dimensional multi-modal data acquisition from the same biological samples multiplexed at scale (multi-OMICs). As a result, systems biologists -theoretically- have access to more data than ever. However, the mathematical frameworks and computational tools needed to analyze and interpret such data are often still nascent, limiting the biological insights that can be obtained without years of computational method development and validation. More pressingly, much of the data sits in silos in formats that are incomprehensible to other scientists or machines limiting its value to the vaster scientific community, especially the computational biologists tasked with analyzing these vast amounts of data in more nuanced ways. With the rapid development and increasing interest in using artificial intelligence (AI) for the life sciences, improving how biologic data is organized and shared is more pressing than ever for scientific progress. Here, we outline a practical approach to multi-modal data management and FAIR sharing, which are in line with the latest US and EU funders’ data sharing policies. This framework can help extend the longevity and utility of data by allowing facile use and reuse, accelerating scientific discovery in the biomedical sciences.
Keywords: FAIR Data, Systems Biology, immunology, omics, multi-modal data, artificial intelligence, modeling, Science administration
Received: 27 May 2024; Accepted: 17 Dec 2024.
Copyright: © 2024 Mugahid, Lyon, Demurjian, Eolin, Whittaker, Godek, Lauffenburger, Fortune and Levine. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Douaa Mugahid, Department of Immunology and Infectious Diseases, School of Public Health, Harvard University, Boston, Massachusetts, United States
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.