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Fernandez ME, Martinez-Romero J, Aon MA, Bernier M, Price NL, de Cabo R. How is Big Data reshaping preclinical aging research? Lab Anim (NY) 2023; 52:289-314. [PMID: 38017182 DOI: 10.1038/s41684-023-01286-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 10/10/2023] [Indexed: 11/30/2023]
Abstract
The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.
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Affiliation(s)
- Maria Emilia Fernandez
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Jorge Martinez-Romero
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
- Laboratory of Epidemiology and Population Science, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Miguel A Aon
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
- Laboratory of Cardiovascular Science, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Michel Bernier
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Nathan L Price
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Rafael de Cabo
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
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Eppig JT. Mouse Genome Informatics (MGI) Resource: Genetic, Genomic, and Biological Knowledgebase for the Laboratory Mouse. ILAR J 2017; 58:17-41. [PMID: 28838066 PMCID: PMC5886341 DOI: 10.1093/ilar/ilx013] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Revised: 03/14/2017] [Accepted: 03/28/2017] [Indexed: 12/13/2022] Open
Abstract
The Mouse Genome Informatics (MGI) Resource supports basic, translational, and computational research by providing high-quality, integrated data on the genetics, genomics, and biology of the laboratory mouse. MGI serves a strategic role for the scientific community in facilitating biomedical, experimental, and computational studies investigating the genetics and processes of diseases and enabling the development and testing of new disease models and therapeutic interventions. This review describes the nexus of the body of growing genetic and biological data and the advances in computer technology in the late 1980s, including the World Wide Web, that together launched the beginnings of MGI. MGI develops and maintains a gold-standard resource that reflects the current state of knowledge, provides semantic and contextual data integration that fosters hypothesis testing, continually develops new and improved tools for searching and analysis, and partners with the scientific community to assure research data needs are met. Here we describe one slice of MGI relating to the development of community-wide large-scale mutagenesis and phenotyping projects and introduce ways to access and use these MGI data. References and links to additional MGI aspects are provided.
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Affiliation(s)
- Janan T. Eppig
- Janan T. Eppig, PhD, is Professor Emeritus at The Jackson Laboratory in Bar Harbor, Maine
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Eppig JT, Richardson JE, Kadin JA, Ringwald M, Blake JA, Bult CJ. Mouse Genome Informatics (MGI): reflecting on 25 years. Mamm Genome 2015; 26:272-84. [PMID: 26238262 PMCID: PMC4534491 DOI: 10.1007/s00335-015-9589-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2015] [Accepted: 07/20/2015] [Indexed: 12/02/2022]
Abstract
From its inception in 1989, the mission of the Mouse Genome Informatics (MGI) resource remains to integrate genetic, genomic, and biological data about the laboratory mouse to facilitate the study of human health and disease. This mission is ever more feasible as the revolution in genetics knowledge, the ability to sequence genomes, and the ability to specifically manipulate mammalian genomes are now at our fingertips. Through major paradigm shifts in biological research and computer technologies, MGI has adapted and evolved to become an integral part of the larger global bioinformatics infrastructure and honed its ability to provide authoritative reference datasets used and incorporated by many other established bioinformatics resources. Here, we review some of the major changes in research approaches over that last quarter century, how these changes are reflected in the MGI resource you use today, and what may be around the next corner.
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Affiliation(s)
- Janan T. Eppig
- Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME 04609 USA
| | - Joel E. Richardson
- Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME 04609 USA
| | - James A. Kadin
- Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME 04609 USA
| | - Martin Ringwald
- Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME 04609 USA
| | - Judith A. Blake
- Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME 04609 USA
| | - Carol J. Bult
- Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME 04609 USA
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Blake JA, Richardson JE, Davisson MT, Eppig JT. The Mouse Genome Database (MGD). A comprehensive public resource of genetic, phenotypic and genomic data. The Mouse Genome Informatics Group. Nucleic Acids Res 1997; 25:85-91. [PMID: 9045213 PMCID: PMC146406 DOI: 10.1093/nar/25.1.85] [Citation(s) in RCA: 45] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
The Mouse Genome Database (MGD) is a comprehensive community resource of mouse genetic and biological information populated both with data from published literature and with data electronically submitted from the research community. MGD stores genetic, physical and comparative mapping data, clones/probes/PCR information, and phenotype descriptions for genes, mutations and mouse strains. Supporting software for importation, analysis, display and distribution of mouse genetic data have been developed. User support is provided through dedicated staff providing documentation, training, and response to individual user queries. MGD is accessible over the Internet at URL http://www.informatics.jax.org.
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Affiliation(s)
- J A Blake
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA.
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Abstract
The materials of bioinformatics are biological data, and its methods are derived from a wide variety of computational techniques. Recent years have seen an explosive growth in biological data, and the development of novel computational methods. These methods have become essential to research progress in structural biology, genomics, structure-based drug design and molecular evolution. The development and maintenance of a robust infrastructure of biological data is of equal importance if biotechnology is to take maximum advantage of research advances in a wide variety of fields. While bioinformatics has already made important contributions, it faces significant challenges as it matures.
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Affiliation(s)
- D Benton
- National Center for Human Genome Research, National Institutes of Health, Bethesda, MD 20892-6050, USA. benton@extra,nchgr.nih.gov
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