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Liu S, Cheng H, Zhang Y, He M, Zuo D, Wang Q, Lv L, Lin Z, Song G. Fingerprint Finder: Identifying Genomic Fingerprint Sites in Cotton Cohorts for Genetic Analysis and Breeding Advancement. Genes (Basel) 2024; 15:378. [PMID: 38540437 PMCID: PMC10970022 DOI: 10.3390/genes15030378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 03/17/2024] [Accepted: 03/18/2024] [Indexed: 06/14/2024] Open
Abstract
Genomic data in Gossypium provide numerous data resources for the cotton genomics community. However, to fill the gap between genomic analysis and breeding field work, detecting the featured genomic items of a subset cohort is essential for geneticists. We developed FPFinder v1.0 software to identify a subset of the cohort's fingerprint genomic sites. The FPFinder was developed based on the term frequency-inverse document frequency algorithm. With the short-read sequencing of an elite cotton pedigree, we identified 453 pedigree fingerprint genomic sites and found that these pedigree-featured sites had a role in cotton development. In addition, we applied FPFinder to evaluate the geographical bias of fiber-length-related genomic sites from a modern cotton cohort consisting of 410 accessions. Enriching elite sites in cultivars from the Yangtze River region resulted in the longer fiber length of Yangze River-sourced accessions. Apart from characterizing functional sites, we also identified 12,536 region-specific genomic sites. Combining the transcriptome data of multiple tissues and samples under various abiotic stresses, we found that several region-specific sites contributed to environmental adaptation. In this research, FPFinder revealed the role of the cotton pedigree fingerprint and region-specific sites in cotton development and environmental adaptation, respectively. The FPFinder can be applied broadly in other crops and contribute to genetic breeding in the future.
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Affiliation(s)
- Shang Liu
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang 455000, China; (S.L.); (Y.Z.); (M.H.); (D.Z.); (Q.W.); (L.L.)
- National Key Laboratory of Crop Genetic Improvement, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China;
| | - Hailiang Cheng
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang 455000, China; (S.L.); (Y.Z.); (M.H.); (D.Z.); (Q.W.); (L.L.)
- Zhengzhou Research Base, Zhengzhou University, Zhengzhou 450001, China
| | - Youping Zhang
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang 455000, China; (S.L.); (Y.Z.); (M.H.); (D.Z.); (Q.W.); (L.L.)
| | - Man He
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang 455000, China; (S.L.); (Y.Z.); (M.H.); (D.Z.); (Q.W.); (L.L.)
| | - Dongyun Zuo
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang 455000, China; (S.L.); (Y.Z.); (M.H.); (D.Z.); (Q.W.); (L.L.)
| | - Qiaolian Wang
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang 455000, China; (S.L.); (Y.Z.); (M.H.); (D.Z.); (Q.W.); (L.L.)
| | - Limin Lv
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang 455000, China; (S.L.); (Y.Z.); (M.H.); (D.Z.); (Q.W.); (L.L.)
| | - Zhongxv Lin
- National Key Laboratory of Crop Genetic Improvement, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China;
| | - Guoli Song
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang 455000, China; (S.L.); (Y.Z.); (M.H.); (D.Z.); (Q.W.); (L.L.)
- Zhengzhou Research Base, Zhengzhou University, Zhengzhou 450001, China
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Raza A, Chen H, Zhang C, Zhuang Y, Sharif Y, Cai T, Yang Q, Soni P, Pandey MK, Varshney RK, Zhuang W. Designing future peanut: the power of genomics-assisted breeding. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:66. [PMID: 38438591 DOI: 10.1007/s00122-024-04575-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Accepted: 02/03/2024] [Indexed: 03/06/2024]
Abstract
KEY MESSAGE Integrating GAB methods with high-throughput phenotyping, genome editing, and speed breeding hold great potential in designing future smart peanut cultivars to meet market and food supply demands. Cultivated peanut (Arachis hypogaea L.), a legume crop greatly valued for its nourishing food, cooking oil, and fodder, is extensively grown worldwide. Despite decades of classical breeding efforts, the actual on-farm yield of peanut remains below its potential productivity due to the complicated interplay of genotype, environment, and management factors, as well as their intricate interactions. Integrating modern genomics tools into crop breeding is necessary to fast-track breeding efficiency and rapid progress. When combined with speed breeding methods, this integration can substantially accelerate the breeding process, leading to faster access of improved varieties to farmers. Availability of high-quality reference genomes for wild diploid progenitors and cultivated peanuts has accelerated the process of gene/quantitative locus discovery, developing markers and genotyping assays as well as a few molecular breeding products with improved resistance and oil quality. The use of new breeding tools, e.g., genomic selection, haplotype-based breeding, speed breeding, high-throughput phenotyping, and genome editing, is probable to boost genetic gains in peanut. Moreover, renewed attention to efficient selection and exploitation of targeted genetic resources is also needed to design high-quality and high-yielding peanut cultivars with main adaptation attributes. In this context, the combination of genomics-assisted breeding (GAB), genome editing, and speed breeding hold great potential in designing future improved peanut cultivars to meet market and food supply demands.
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Affiliation(s)
- Ali Raza
- Key Laboratory of Ministry of Education for Genetics, Center of Legume Crop Genetics and Systems Biology, Oil Crops Research Institute, Fujian Agriculture and Forestry University (FAFU), Fuzhou, 350002, China
| | - Hua Chen
- Key Laboratory of Ministry of Education for Genetics, Center of Legume Crop Genetics and Systems Biology, Oil Crops Research Institute, Fujian Agriculture and Forestry University (FAFU), Fuzhou, 350002, China
| | - Chong Zhang
- Key Laboratory of Ministry of Education for Genetics, Center of Legume Crop Genetics and Systems Biology, Oil Crops Research Institute, Fujian Agriculture and Forestry University (FAFU), Fuzhou, 350002, China
| | - Yuhui Zhuang
- College of Life Science, Fujian Agriculture and Forestry University (FAFU), Fuzhou, 350002, China
| | - Yasir Sharif
- Key Laboratory of Ministry of Education for Genetics, Center of Legume Crop Genetics and Systems Biology, Oil Crops Research Institute, Fujian Agriculture and Forestry University (FAFU), Fuzhou, 350002, China
| | - Tiecheng Cai
- Key Laboratory of Ministry of Education for Genetics, Center of Legume Crop Genetics and Systems Biology, Oil Crops Research Institute, Fujian Agriculture and Forestry University (FAFU), Fuzhou, 350002, China
| | - Qiang Yang
- Key Laboratory of Ministry of Education for Genetics, Center of Legume Crop Genetics and Systems Biology, Oil Crops Research Institute, Fujian Agriculture and Forestry University (FAFU), Fuzhou, 350002, China
| | - Pooja Soni
- Center of Excellence in Genomics and Systems Biology (CEGSB), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, 502324, India
| | - Manish K Pandey
- Center of Excellence in Genomics and Systems Biology (CEGSB), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, 502324, India
| | - Rajeev K Varshney
- WA State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, WA, 6150, Australia.
| | - Weijian Zhuang
- Key Laboratory of Ministry of Education for Genetics, Center of Legume Crop Genetics and Systems Biology, Oil Crops Research Institute, Fujian Agriculture and Forestry University (FAFU), Fuzhou, 350002, China.
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Li Y, Yang X, Tong L, Wang L, Xue L, Luan Q, Jiang J. Phenomic selection in slash pine multi-temporally using UAV-multispectral imagery. FRONTIERS IN PLANT SCIENCE 2023; 14:1156430. [PMID: 37670863 PMCID: PMC10475579 DOI: 10.3389/fpls.2023.1156430] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 08/02/2023] [Indexed: 09/07/2023]
Abstract
Genomic selection (GS) is an option for plant domestication that offers high efficiency in improving genetics. However, GS is often not feasible for long-lived tree species with large and complex genomes. In this paper, we investigated UAV multispectral imagery in time series to evaluate genetic variation in tree growth and developed a new predictive approach that is independent of sequencing or pedigrees based on multispectral imagery plus vegetation indices (VIs) for slash pine. Results show that temporal factors have a strong influence on the h2 of tree growth traits. High genetic correlations were found in most months, and genetic gain also showed a slight influence on the time series. Using a consistent ranking of family breeding values, optimal slash pine families were selected, obtaining a promising and reliable predictive ability based on multispectral+VIs (MV) alone or on the combination of pedigree and MV. The highest predictive value, ranging from 0.52 to 0.56, was found in July. The methods described in this paper provide new approaches for phenotypic selection (PS) using high-throughput multispectral unmanned aerial vehicle (UAV) technology, which could potentially be used to reduce the generation time for conifer species and increase the genetic granularity independent of sequencing or pedigrees.
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Affiliation(s)
- Yanjie Li
- State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry, Beijing, China
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Cultivation, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of Tree Breeding of Zhejiang Province, Fuyang, Hangzhou, Zhejiang, China
| | - Xinyu Yang
- Soybean Research Institute, National Center for Soybean Improvement, Key Laboratory of Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), State Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, College of Agriculture, Nanjing Agricultural University, Nanjing, China
| | - Long Tong
- Chongqing Academy of Forestry, Chongqing, China
| | - Lingling Wang
- Forestry and Water Conservancy Bureau of Fuyang District in Hangzhou, Hangzhou, China
| | - Liang Xue
- State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry, Beijing, China
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Cultivation, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of Tree Breeding of Zhejiang Province, Fuyang, Hangzhou, Zhejiang, China
| | - Qifu Luan
- State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry, Beijing, China
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Cultivation, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of Tree Breeding of Zhejiang Province, Fuyang, Hangzhou, Zhejiang, China
| | - Jingmin Jiang
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Cultivation, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of Tree Breeding of Zhejiang Province, Fuyang, Hangzhou, Zhejiang, China
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Dwivedi SL, Heslop-Harrison P, Spillane C, McKeown PC, Edwards D, Goldman I, Ortiz R. Evolutionary dynamics and adaptive benefits of deleterious mutations in crop gene pools. TRENDS IN PLANT SCIENCE 2023; 28:685-697. [PMID: 36764870 DOI: 10.1016/j.tplants.2023.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 12/03/2022] [Accepted: 01/18/2023] [Indexed: 05/13/2023]
Abstract
Mutations with deleterious consequences in nature may be conditionally deleterious in crop plants. That is, while some genetic variants may reduce fitness under wild conditions and be subject to purifying selection, they can be under positive selection in domesticates. Such deleterious alleles can be plant breeding targets, particularly for complex traits. The difficulty of distinguishing favorable from unfavorable variants reduces the power of selection, while favorable trait variation and heterosis may be attributable to deleterious alleles. Here, we review the roles of deleterious mutations in crop breeding and discuss how they can be used as a new avenue for crop improvement with emerging genomic tools, including HapMaps and pangenome analysis, aiding the identification, removal, or exploitation of deleterious mutations.
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Affiliation(s)
| | - Pat Heslop-Harrison
- Key Laboratory of Plant Resources Conservation and Sustainable Utilization, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, 510650, China; Department of Genetics and Genome Biology, University of Leicester, Leicester, LE1 7RH, UK
| | - Charles Spillane
- Agriculture and Bioeconomy Research Centre, Ryan Institute, University of Galway, University Road, Galway, H91 REW4, Ireland
| | - Peter C McKeown
- Agriculture and Bioeconomy Research Centre, Ryan Institute, University of Galway, University Road, Galway, H91 REW4, Ireland
| | - David Edwards
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA 6009, Australia
| | - Irwin Goldman
- Department of Horticulture, College of Agricultural and Life Sciences, University of Wisconsin Madison, WI 53706, USA
| | - Rodomiro Ortiz
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, SE 23053, Sweden.
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5
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Karabulut E, Erkoç K, Acı M, Aydın M, Barriball S, Braley J, Cassetta E, Craine EB, Diaz-Garcia L, Hershberger J, Meyering B, Miller AJ, Rubin MJ, Tesdell O, Schlautman B, Şakiroğlu M. Sainfoin ( Onobrychis spp.) crop ontology: supporting germplasm characterization and international research collaborations. FRONTIERS IN PLANT SCIENCE 2023; 14:1177406. [PMID: 37255566 PMCID: PMC10225502 DOI: 10.3389/fpls.2023.1177406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 04/18/2023] [Indexed: 06/01/2023]
Abstract
Sainfoin (Onobrychis spp.) is a perennial forage legume that is also attracting attention as a perennial pulse with potential for human consumption. The dual use of sainfoin underpins diverse research and breeding programs focused on improving sainfoin lines for forage and pulses, which is driving the generation of complex datasets describing high dimensional phenotypes in the post-omics era. To ensure that multiple user groups, for example, breeders selecting for forage and those selecting for edible seed, can utilize these rich datasets, it is necessary to develop common ontologies and accessible ontology platforms. One such platform, Crop Ontology, was created in 2008 by the Consortium of International Agricultural Research Centers (CGIAR) to host crop-specific trait ontologies that support standardized plant breeding databases. In the present study, we describe the sainfoin crop ontology (CO). An in-depth literature review was performed to develop a comprehensive list of traits measured and reported in sainfoin. Because the same traits can be measured in different ways, ultimately, a set of 98 variables (variable = plant trait + method of measurement + scale of measurement) used to describe variation in sainfoin were identified. Variables were formatted and standardized based on guidelines provided here for inclusion in the sainfoin CO. The 98 variables contained a total of 82 traits from four trait classes of which 24 were agronomic, 31 were morphological, 19 were seed and forage quality related, and 8 were phenological. In addition to the developed variables, we have provided a roadmap for developing and submission of new traits to the sainfoin CO.
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Affiliation(s)
- Ebrar Karabulut
- Bioengineering Department, Adana Alparslan Türkeş Science and Technology University, Adana, Türkiye
| | - Kübra Erkoç
- Bioengineering Department, Adana Alparslan Türkeş Science and Technology University, Adana, Türkiye
| | - Murat Acı
- Bioengineering Department, Adana Alparslan Türkeş Science and Technology University, Adana, Türkiye
- The Land Institute, Salina, KS, United States
| | - Mahmut Aydın
- Department of Computer Engineering, Kafkas University, Kars, Türkiye
| | | | - Jackson Braley
- Donald Danforth Plant Science Center, St. Louis, MO, United States
| | | | | | - Luis Diaz-Garcia
- Department of Viticulture and Enology, University of California Davis, Davis, CA, United States
| | - Jenna Hershberger
- Plant and Environmental Sciences Department, Clemson University, Clemson, SC, United States
| | - Bo Meyering
- The Land Institute, Salina, KS, United States
| | - Allison J. Miller
- Donald Danforth Plant Science Center, St. Louis, MO, United States
- Department. of Biology, Saint Louis University, St. Louis, MO, United States
| | - Matthew J. Rubin
- Donald Danforth Plant Science Center, St. Louis, MO, United States
| | - Omar Tesdell
- Department of Geography, Birzeit University, Birzeit, West Bank, Palestine
| | | | - Muhammet Şakiroğlu
- Bioengineering Department, Adana Alparslan Türkeş Science and Technology University, Adana, Türkiye
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Xie X, Xia F, Wu Y, Liu S, Yan K, Xu H, Ji Z. A Novel Feature Selection Strategy Based on Salp Swarm Algorithm for Plant Disease Detection. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0039. [PMID: 37228513 PMCID: PMC10204742 DOI: 10.34133/plantphenomics.0039] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 02/28/2023] [Indexed: 05/27/2023]
Abstract
Deep learning has been widely used for plant disease recognition in smart agriculture and has proven to be a powerful tool for image classification and pattern recognition. However, it has limited interpretability for deep features. With the transfer of expert knowledge, handcrafted features provide a new way for personalized diagnosis of plant diseases. However, irrelevant and redundant features lead to high dimensionality. In this study, we proposed a swarm intelligence algorithm for feature selection [salp swarm algorithm for feature selection (SSAFS)] in image-based plant disease detection. SSAFS is employed to determine the ideal combination of handcrafted features to maximize classification success while minimizing the number of features. To verify the effectiveness of the developed SSAFS algorithm, we conducted experimental studies using SSAFS and 5 metaheuristic algorithms. Several evaluation metrics were used to evaluate and analyze the performance of these methods on 4 datasets from the UCI machine learning repository and 6 plant phenomics datasets from PlantVillage. Experimental results and statistical analyses validated the outstanding performance of SSAFS compared to existing state-of-the-art algorithms, confirming the superiority of SSAFS in exploring the feature space and identifying the most valuable features for diseased plant image classification. This computational tool will allow us to explore an optimal combination of handcrafted features to improve plant disease recognition accuracy and processing time.
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Affiliation(s)
- Xiaojun Xie
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
- Center for Data Science and Intelligent Computing, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Fei Xia
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Yufeng Wu
- State Key Laboratory for Crop Genetics and Germplasm Enhancement, Bioinformatics Center, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Shouyang Liu
- Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Ke Yan
- Department of the Built Environment, College of Design and Engineering, National University of Singapore, 4 Architecture Drive, Singapore 117566, Singapore
| | - Huanliang Xu
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Zhiwei Ji
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
- Center for Data Science and Intelligent Computing, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
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Coleman GRY, Salter WT. More eyes on the prize: open-source data, software and hardware for advancing plant science through collaboration. AOB PLANTS 2023; 15:plad010. [PMID: 37025102 PMCID: PMC10071051 DOI: 10.1093/aobpla/plad010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 03/08/2023] [Indexed: 06/19/2023]
Abstract
Automating the analysis of plants using image processing would help remove barriers to phenotyping and large-scale precision agricultural technologies, such as site-specific weed control. The combination of accessible hardware and high-performance deep learning (DL) tools for plant analysis is becoming widely recognised as a path forward for both plant science and applied precision agricultural purposes. Yet, a lack of collaboration in image analysis for plant science, despite the open-source origins of much of the technology, is hindering development. Here, we show how tools developed for specific attributes of phenotyping or weed recognition for precision weed control have substantial overlapping data structure, software/hardware requirements and outputs. An open-source approach to these tools facilitates interdisciplinary collaboration, avoiding unnecessary repetition and allowing research groups in both basic and applied sciences to capitalise on advancements and resolve respective bottlenecks. The approach mimics that of machine learning in its nascence. Three areas of collaboration are identified as critical for improving efficiency, (1) standardized, open-source, annotated dataset development with consistent metadata reporting; (2) establishment of accessible and reliable training and testing platforms for DL algorithms; and (3) sharing of all source code used in the research process. The complexity of imaging plants and cost of annotating image datasets means that collaboration from typically distinct fields will be necessary to capitalize on the benefits of DL for both applied and basic science purposes.
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Affiliation(s)
- Guy R Y Coleman
- School of Life and Environmental Sciences, Sydney Institute of Agriculture, The University of Sydney, Brownlow Hill, New South Wales 2570, Australia
| | - William T Salter
- School of Life and Environmental Sciences, Sydney Institute of Agriculture, The University of Sydney, Narrabri, New South Wales 2390, Australia
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Evolutionary divergence of duplicated genomes in newly described allotetraploid cottons. Proc Natl Acad Sci U S A 2022; 119:e2208496119. [PMID: 36122204 PMCID: PMC9522333 DOI: 10.1073/pnas.2208496119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Wild relatives of domesticated plants provide a rich resource for crop improvement and a valuable comparative perspective for understanding genomic, physiological, and agricultural traits. Here, we provide high-quality reference genomes of one early domesticated form of the economically most important cotton species, Gossypium hirsutum, and two other wild species, to clarify evolutionary relationships and understand the genomic changes that characterize these species and their close relatives. We document abundant gene resources involved in adaptation to environmental challenges, highlighting the potential for introgression of favorable genes into domesticated cotton and for increasing resilience to climate variability. Our study complements other recent genomic analyses in the cotton genus and provides a valuable foundation for breeding improved cotton varieties. Allotetraploid cotton (Gossypium) species represents a model system for the study of plant polyploidy, molecular evolution, and domestication. Here, chromosome-scale genome sequences were obtained and assembled for two recently described wild species of tetraploid cotton, Gossypium ekmanianum [(AD)6, Ge] and Gossypium stephensii [(AD)7, Gs], and one early form of domesticated Gossypium hirsutum, race punctatum [(AD)1, Ghp]. Based on phylogenomic analysis, we provide a dated whole-genome level perspective for the evolution of the tetraploid Gossypium clade and resolved the evolutionary relationships of Gs, Ge, and domesticated G. hirsutum. We describe genomic structural variation that arose during Gossypium evolution and describe its correlates—including phenotypic differentiation, genetic isolation, and genetic convergence—that contributed to cotton biodiversity and cotton domestication. Presence/absence variation is prominent in causing cotton genomic structural variations. A presence/absence variation-derived gene encoding a phosphopeptide-binding protein is implicated in increasing fiber length during cotton domestication. The relatively unimproved Ghp offers the potential for gene discovery related to adaptation to environmental challenges. Expanded gene families enoyl-CoA δ isomerase 3 and RAP2-7 may have contributed to abiotic stress tolerance, possibly by targeting plant hormone-associated biochemical pathways. Our results generate a genomic context for a better understanding of cotton evolution and for agriculture.
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Ambika, Aski MS, Gayacharan, Hamwieh A, Talukdar A, Kumar Gupta S, Sharma BB, Joshi R, Upadhyaya HD, Singh K, Kumar R. Unraveling Origin, History, Genetics, and Strategies for Accelerated Domestication and Diversification of Food Legumes. Front Genet 2022; 13:932430. [PMID: 35979429 PMCID: PMC9376740 DOI: 10.3389/fgene.2022.932430] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 06/15/2022] [Indexed: 11/24/2022] Open
Abstract
Domestication is a dynamic and ongoing process of transforming wild species into cultivated species by selecting desirable agricultural plant features to meet human needs such as taste, yield, storage, and cultivation practices. Human plant domestication began in the Fertile Crescent around 12,000 years ago and spread throughout the world, including China, Mesoamerica, the Andes and Near Oceania, Sub-Saharan Africa, and eastern North America. Indus valley civilizations have played a great role in the domestication of grain legumes. Crops, such as pigeon pea, black gram, green gram, lablab bean, moth bean, and horse gram, originated in the Indian subcontinent, and Neolithic archaeological records indicate that these crops were first domesticated by early civilizations in the region. The domestication and evolution of wild ancestors into today’s elite cultivars are important contributors to global food supply and agricultural crop improvement. In addition, food legumes contribute to food security by protecting human health and minimize climate change impacts. During the domestication process, legume crop species have undergone a severe genetic diversity loss, and only a very narrow range of variability is retained in the cultivars. Further reduction in genetic diversity occurred during seed dispersal and movement across the continents. In general, only a few traits, such as shattering resistance, seed dormancy loss, stem growth behavior, flowering–maturity period, and yield traits, have prominence in the domestication process across the species. Thus, identification and knowledge of domestication responsive loci were often useful in accelerating new species’ domestication. The genes and metabolic pathways responsible for the significant alterations that occurred as an outcome of domestication might aid in the quick domestication of novel crops. Further, recent advances in “omics” sciences, gene-editing technologies, and functional analysis will accelerate the domestication and crop improvement of new crop species without losing much genetic diversity. In this review, we have discussed about the origin, center of diversity, and seed movement of major food legumes, which will be useful in the exploration and utilization of genetic diversity in crop improvement. Further, we have discussed about the major genes/QTLs associated with the domestication syndrome in pulse crops and the future strategies to improve the food legume crops.
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