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Koltes JE, Cole JB, Clemmens R, Dilger RN, Kramer LM, Lunney JK, McCue ME, McKay SD, Mateescu RG, Murdoch BM, Reuter R, Rexroad CE, Rosa GJM, Serão NVL, White SN, Woodward-Greene MJ, Worku M, Zhang H, Reecy JM. A Vision for Development and Utilization of High-Throughput Phenotyping and Big Data Analytics in Livestock. Front Genet 2019; 10:1197. [PMID: 31921279 PMCID: PMC6934059 DOI: 10.3389/fgene.2019.01197] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Accepted: 10/29/2019] [Indexed: 01/28/2023] Open
Abstract
Automated high-throughput phenotyping with sensors, imaging, and other on-farm technologies has resulted in a flood of data that are largely under-utilized. Drastic cost reductions in sequencing and other omics technology have also facilitated the ability for deep phenotyping of livestock at the molecular level. These advances have brought the animal sciences to a cross-roads in data science where increased training is needed to manage, record, and analyze data to generate knowledge and advances in Agriscience related disciplines. This paper describes the opportunities and challenges in using high-throughput phenotyping, “big data,” analytics, and related technologies in the livestock industry based on discussions at the Livestock High-Throughput Phenotyping and Big Data Analytics meeting, held in November 2017 (see: https://www.animalgenome.org/bioinfo/community/workshops/2017/). Critical needs for investments in infrastructure for people (e.g., “big data” training), data (e.g., data transfer, management, and analytics), and technology (e.g., development of low cost sensors) were defined by this group. Though some subgroups of animal science have extensive experience in predictive modeling, cross-training in computer science, statistics, and related disciplines are needed to use big data for diverse applications in the field. Extensive opportunities exist for public and private entities to harness big data to develop valuable research knowledge and products to the benefit of society under the increased demands for food in a rapidly growing population.
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Affiliation(s)
- James E Koltes
- Department of Animal Science, College of Agriculture and Life Sciences, Iowa State University, Ames, IA, United States
| | - John B Cole
- Animal Genomics and Improvement Laboratory, USDA-ARS, Beltsville, MD, United States
| | - Roxanne Clemmens
- College of Agriculture and Life Sciences, Iowa State University, Ames, IA, United States
| | - Ryan N Dilger
- Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Luke M Kramer
- Department of Animal Science, College of Agriculture and Life Sciences, Iowa State University, Ames, IA, United States
| | - Joan K Lunney
- Animal Parasitic Diseases Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville, MD, United States
| | - Molly E McCue
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, Saint Paul, MN, United States
| | - Stephanie D McKay
- Department of Animal and Veterinary Sciences, College of Agriculture and Life Sciences, University of Vermont, Burlington, VT, United States
| | - Raluca G Mateescu
- Department of Animal Sciences, University of Florida, Gainesville, FL, United States
| | - Brenda M Murdoch
- Department of Animal and Veterinary Science, University of Idaho, Moscow, ID, United States
| | - Ryan Reuter
- Department of Animal and Food Sciences, College of Agricultural Sciences and Natural Resources, Oklahoma State University, Stillwater, OK, United States
| | - Caird E Rexroad
- Agricultural Research Service, United States Department of Agriculture, Washington D.C., DC, United States
| | - Guilherme J M Rosa
- Department of Dairy Science, University of Wisconsin-Madison, Madison, WI, United States
| | - Nick V L Serão
- Department of Animal Science, College of Agriculture and Life Sciences, Iowa State University, Ames, IA, United States
| | - Stephen N White
- Animal Disease Research Unit, Agricultural Research Service, United States Department of Agriculture, Pullman, WA, United States.,Department of Veterinary Microbiology and Pathology, College of Veterinary Medicine, Washington State University, Pullman, WA, United States.,Center for Reproductive Biology, College of Veterinary Medicine, Washington State University, Pullman, WA, United States
| | - M Jennifer Woodward-Greene
- Agricultural Research Service, United States Department of Agriculture, Washington D.C., DC, United States
| | - Millie Worku
- Department of Animal Sciences, North Carolina Agricultural and Technical State University, Greensboro, NC, United States
| | - Hongwei Zhang
- Department of Electrical and Computer Engineering, College of Engineering, Iowa State University, Ames, IA, United States
| | - James M Reecy
- Department of Animal Science, College of Agriculture and Life Sciences, Iowa State University, Ames, IA, United States
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Dodgson JB. Chicken genome sequence: a centennial gift to poultry genetics. Cytogenet Genome Res 2004; 102:291-6. [PMID: 14970719 DOI: 10.1159/000075765] [Citation(s) in RCA: 15] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2003] [Accepted: 07/28/2003] [Indexed: 11/19/2022] Open
Abstract
A draft sequence of the chicken genome will be available by early 2004. This event conveniently marks the start of the second century of poultry genetics, coming 100 years after the use of the chicken to demonstrate Mendelian inheritance in animals by William Bateson. How will the second, post-genomic century of poultry genetics differ from the first? A whole genome shotgun (WGS) approach is being used to obtain the chicken sequence, with the goal of generating approximately six-fold coverage of the genome. Bacterial artificial chromosome (BAC) and fosmid clone end sequences, along with a BAC contig map integrated with genetic linkage and radiation hybrid maps, will form the platform for assembly of the WGS data. Rapid progress in global analysis of chicken gene expression patterns is also being made. Comparative genomics will link these new discoveries to the knowledge base for all other animal species. It's hoped that the genome sequence will also provide common ground on which to unite studies of the chicken as a model species with those aimed at agriculturally-relevant applications. The current status of chicken genomics will be assessed with projections for its near and long term future.
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Affiliation(s)
- J B Dodgson
- Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan 488242-4320, USA.
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