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Ghavi Hossein-Zadeh N. An overview of recent technological developments in bovine genomics. Vet Anim Sci 2024; 25:100382. [PMID: 39166173 PMCID: PMC11334705 DOI: 10.1016/j.vas.2024.100382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/22/2024] Open
Abstract
Cattle are regarded as highly valuable animals because of their milk, beef, dung, fur, and ability to draft. The scientific community has tried a number of strategies to improve the genetic makeup of bovine germplasm. To ensure higher returns for the dairy and beef industries, researchers face their greatest challenge in improving commercially important traits. One of the biggest developments in the last few decades in the creation of instruments for cattle genetic improvement is the discovery of the genome. Breeding livestock is being revolutionized by genomic selection made possible by the availability of medium- and high-density single nucleotide polymorphism (SNP) arrays coupled with sophisticated statistical techniques. It is becoming easier to access high-dimensional genomic data in cattle. Continuously declining genotyping costs and an increase in services that use genomic data to increase return on investment have both made a significant contribution to this. The field of genomics has come a long way thanks to groundbreaking discoveries such as radiation-hybrid mapping, in situ hybridization, synteny analysis, somatic cell genetics, cytogenetic maps, molecular markers, association studies for quantitative trait loci, high-throughput SNP genotyping, whole-genome shotgun sequencing to whole-genome mapping, and genome editing. These advancements have had a significant positive impact on the field of cattle genomics. This manuscript aimed to review recent advances in genomic technologies for cattle breeding and future prospects in this field.
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Affiliation(s)
- Navid Ghavi Hossein-Zadeh
- Department of Animal Science, Faculty of Agricultural Sciences, University of Guilan, Rasht, 41635-1314, Iran
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2
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Jiao W, Lu K, Wen M, Mao J, Ni Z, Chen ZJ, Wang X, Song Q, Yuan J. Ploidy variation induces butterfly effect on chromatin topology in wheat. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024; 119:2450-2463. [PMID: 39003593 DOI: 10.1111/tpj.16932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 06/19/2024] [Accepted: 07/03/2024] [Indexed: 07/15/2024]
Abstract
Polyploidy is a prominent driver of plant diversification, accompanied with dramatic chromosomal rearrangement and epigenetic changes that affect gene expression. How chromatin interactions within and between subgenomes adapt to ploidy transition remains poorly understood. We generate open chromatin interaction maps for natural hexaploid wheat (AABBDD), extracted tetraploid wheat (AABB), diploid wheat progenitor Aegilops tauschii (DD) and resynthesized hexaploid wheat (RHW, AABBDD). Thousands of intra- and interchromosomal loops are de novo established or disappeared in AB subgenomes after separation of D subgenome, in which 37-95% of novel loops are lost again in RHW after merger of D genome. Interestingly, more than half of novel loops are formed by cascade reactions that are triggered by disruption of chromatin interaction between AB and D subgenomes. The interaction repressed genes in RHW relative to DD are expression suppressed, resulting in more balanced expression of the three homoeologs in RHW. The interaction levels of cascade anchors are decreased step-by-step. Leading single nucleotide polymorphisms of yield- and plant architecture-related quantitative trait locus are significantly enriched in cascade anchors. The expression of 116 genes interacted with these anchors are significantly correlated with the corresponding traits. Our findings reveal trans-regulation of intrachromosomal loops by interchromosomal interactions during genome merger and separation in polyploid species.
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Affiliation(s)
- Wu Jiao
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, China
| | - Kening Lu
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, China
| | - Mingxing Wen
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, China
| | - Junrong Mao
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, China
| | - Zhongfu Ni
- Frontiers Science Center for Molecular Design Breeding/Key Laboratory of Crop Heterosis and Utilization (MOE)/College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China
| | - Z Jeffrey Chen
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, 78712, Texas, USA
| | - Xiue Wang
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, China
| | - Qingxin Song
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, China
| | - Jingya Yuan
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, China
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Zhu T, Xia C, Yu R, Zhou X, Xu X, Wang L, Zong Z, Yang J, Liu Y, Ming L, You Y, Chen D, Xie W. Comprehensive mapping and modelling of the rice regulome landscape unveils the regulatory architecture underlying complex traits. Nat Commun 2024; 15:6562. [PMID: 39095348 PMCID: PMC11297339 DOI: 10.1038/s41467-024-50787-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 07/19/2024] [Indexed: 08/04/2024] Open
Abstract
Unraveling the regulatory mechanisms that govern complex traits is pivotal for advancing crop improvement. Here we present a comprehensive regulome atlas for rice (Oryza sativa), charting the chromatin accessibility across 23 distinct tissues from three representative varieties. Our study uncovers 117,176 unique open chromatin regions (OCRs), accounting for ~15% of the rice genome, a notably higher proportion compared to previous reports in plants. Integrating RNA-seq data from matched tissues, we confidently predict 59,075 OCR-to-gene links, with enhancers constituting 69.54% of these associations, including many known enhancer-to-gene links. Leveraging this resource, we re-evaluate genome-wide association study results and discover a previously unknown function of OsbZIP06 in seed germination, which we subsequently confirm through experimental validation. We optimize deep learning models to decode regulatory grammar, achieving robust modeling of tissue-specific chromatin accessibility. This approach allows to predict cross-variety regulatory dynamics from genomic sequences, shedding light on the genetic underpinnings of cis-regulatory divergence and morphological disparities between varieties. Overall, our study establishes a foundational resource for rice functional genomics and precision molecular breeding, providing valuable insights into regulatory mechanisms governing complex traits.
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Affiliation(s)
- Tao Zhu
- State Key Laboratory of Pharmaceutical Biotechnology, Department of Gastroenterology, Nanjing Drum Tower Hospital, National Resource Center for Mutant Mice, School of Life Sciences, Nanjing University, Nanjing, 210023, China
- Chemistry and Biomedicine Innovation Center, Nanjing University, Nanjing, 210023, China
| | - Chunjiao Xia
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Ranran Yu
- State Key Laboratory of Pharmaceutical Biotechnology, Department of Gastroenterology, Nanjing Drum Tower Hospital, National Resource Center for Mutant Mice, School of Life Sciences, Nanjing University, Nanjing, 210023, China
| | - Xinkai Zhou
- State Key Laboratory of Pharmaceutical Biotechnology, Department of Gastroenterology, Nanjing Drum Tower Hospital, National Resource Center for Mutant Mice, School of Life Sciences, Nanjing University, Nanjing, 210023, China
| | - Xingbing Xu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Lin Wang
- State Key Laboratory of Pharmaceutical Biotechnology, Department of Gastroenterology, Nanjing Drum Tower Hospital, National Resource Center for Mutant Mice, School of Life Sciences, Nanjing University, Nanjing, 210023, China
| | - Zhanxiang Zong
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Junjiao Yang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yinmeng Liu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Luchang Ming
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yuxin You
- State Key Laboratory of Pharmaceutical Biotechnology, Department of Gastroenterology, Nanjing Drum Tower Hospital, National Resource Center for Mutant Mice, School of Life Sciences, Nanjing University, Nanjing, 210023, China
| | - Dijun Chen
- State Key Laboratory of Pharmaceutical Biotechnology, Department of Gastroenterology, Nanjing Drum Tower Hospital, National Resource Center for Mutant Mice, School of Life Sciences, Nanjing University, Nanjing, 210023, China.
- Chemistry and Biomedicine Innovation Center, Nanjing University, Nanjing, 210023, China.
| | - Weibo Xie
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China.
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, China.
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
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Feng X, Zan Y, Li T, Yao Y, Ning Z, Li J, Charati H, Xu W, Wan Q, Zeng D, Zeng Z, Liu Y, Shen X. Dual-trait genomic analysis in highly stratified Arabidopsis thaliana populations using genome-wide association summary statistics. Heredity (Edinb) 2024; 133:11-20. [PMID: 38822132 PMCID: PMC11222461 DOI: 10.1038/s41437-024-00688-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 05/07/2024] [Indexed: 06/02/2024] Open
Abstract
Genome-wide association study (GWAS) is a powerful tool to identify genomic loci underlying complex traits. However, the application in natural populations comes with challenges, especially power loss due to population stratification. Here, we introduce a bivariate analysis approach to a GWAS dataset of Arabidopsis thaliana. We demonstrate the efficiency of dual-phenotype analysis to uncover hidden genetic loci masked by population structure via a series of simulations. In real data analysis, a common allele, strongly confounded with population structure, is discovered to be associated with late flowering and slow maturation of the plant. The discovered genetic effect on flowering time is further replicated in independent datasets. Using Mendelian randomization analysis based on summary statistics from our GWAS and expression QTL scans, we predicted and replicated a candidate gene AT1G11560 that potentially causes this association. Further analysis indicates that this locus is co-selected with flowering-time-related genes. The discovered pleiotropic genotype-phenotype map provides new insights into understanding the genetic correlation of complex traits.
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Affiliation(s)
- Xiao Feng
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Guangzhou, China
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Yanjun Zan
- Key Laboratory of Tobacco Improvement and Biotechnology, Tobacco Research Institute, Chinese Academy of Agricultural Sciences, Qingdao, China
| | - Ting Li
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Guangzhou, China
| | - Yue Yao
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Guangzhou, China
| | - Zheng Ning
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Jiabei Li
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Guangzhou, China
| | - Hadi Charati
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Guangzhou, China
| | - Weilin Xu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Qianhui Wan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Mathematics, University of California, Davis, CA, USA
| | - Dongyu Zeng
- State Key Laboratory of Biocontrol, School of Ecology, Sun Yat-sen University, Shenzhen, China
| | - Ziyi Zeng
- School of Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yang Liu
- State Key Laboratory of Biocontrol, School of Ecology, Sun Yat-sen University, Shenzhen, China.
| | - Xia Shen
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Guangzhou, China.
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
- Center for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland, UK.
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5
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Zhang Y, Liu W, Lai J, Zeng H. Genetic associations in ankylosing spondylitis: circulating proteins as drug targets and biomarkers. Front Immunol 2024; 15:1394438. [PMID: 38835753 PMCID: PMC11148386 DOI: 10.3389/fimmu.2024.1394438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 04/29/2024] [Indexed: 06/06/2024] Open
Abstract
Background Ankylosing spondylitis (AS) is a complex condition with a significant genetic component. This study explored circulating proteins as potential genetic drug targets or biomarkers to prevent AS, addressing the need for innovative and safe treatments. Methods We analyzed extensive data from protein quantitative trait loci (pQTLs) with up to 1,949 instrumental variables (IVs) and selected the top single-nucleotide polymorphism (SNP) associated with AS risk. Utilizing a two-sample Mendelian randomization (MR) approach, we assessed the causal relationships between identified proteins and AS risk. Colocalization analysis, functional enrichment, and construction of protein-protein interaction networks further supported these findings. We utilized phenome-wide MR (phenMR) analysis for broader validation and repurposing of drugs targeting these proteins. The Drug-Gene Interaction database (DGIdb) was employed to corroborate drug associations with potential therapeutic targets. Additionally, molecular docking (MD) techniques were applied to evaluate the interaction between target protein and four potential AS drugs identified from the DGIdb. Results Our analysis identified 1,654 plasma proteins linked to AS, with 868 up-regulated and 786 down-regulated. 18 proteins (AGER, AIF1, ATF6B, C4A, CFB, CLIC1, COL11A2, ERAP1, HLA-DQA2, HSPA1L, IL23R, LILRB3, MAPK14, MICA, MICB, MPIG6B, TNXB, and VARS1) that show promise as therapeutic targets for AS or biomarkers, especially MAPK14, supported by evidence of colocalization. PhenMR analysis linked these proteins to AS and other diseases, while DGIdb analysis identified potential drugs related to MAPK14. MD analysis indicated strong binding affinities between MAPK14 and four potential AS drugs, suggesting effective target-drug interactions. Conclusion This study underscores the utility of MR analysis in AS research for identifying biomarkers and therapeutic drug targets. The involvement of Th17 cell differentiation-related proteins in AS pathogenesis is particularly notable. Clinical validation and further investigation are essential for future applications.
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Affiliation(s)
- Ye Zhang
- Traditional Chinese Medicine Department of Immunology, Women & Children Health Institute Futian Shenzhen, Shenzhen, China
| | - Wei Liu
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
| | - Junda Lai
- Department of Human Life Sciences, Beijing Sport University, Beijing, China
| | - Huiqiong Zeng
- Traditional Chinese Medicine Department of Immunology, Women & Children Health Institute Futian Shenzhen, Shenzhen, China
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Tao Y, Zhao R, Yang B, Han J, Li Y. Dissecting the shared genetic landscape of anxiety, depression, and schizophrenia. J Transl Med 2024; 22:373. [PMID: 38637810 PMCID: PMC11025255 DOI: 10.1186/s12967-024-05153-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 04/01/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND Numerous studies highlight the genetic underpinnings of mental disorders comorbidity, particularly in anxiety, depression, and schizophrenia. However, their shared genetic loci are not well understood. Our study employs Mendelian randomization (MR) and colocalization analyses, alongside multi-omics data, to uncover potential genetic targets for these conditions, thereby informing therapeutic and drug development strategies. METHODS We utilized the Consortium for Linkage Disequilibrium Score Regression (LDSC) and Mendelian Randomization (MR) analysis to investigate genetic correlations among anxiety, depression, and schizophrenia. Utilizing GTEx V8 eQTL and deCODE Genetics pQTL data, we performed a three-step summary-data-based Mendelian randomization (SMR) and protein-protein interaction analysis. This helped assess causal and comorbid loci for these disorders and determine if identified loci share coincidental variations with psychiatric diseases. Additionally, phenome-wide association studies, drug prediction, and molecular docking validated potential drug targets. RESULTS We found genetic correlations between anxiety, depression, and schizophrenia, and under a meta-analysis of MR from multiple databases, the causal relationships among these disorders are supported. Based on this, three-step SMR and colocalization analyses identified ITIH3 and CCS as being related to the risk of developing depression, while CTSS and DNPH1 are related to the onset of schizophrenia. BTN3A1, PSMB4, and TIMP4 were identified as comorbidity loci for both disorders. Molecules that could not be determined through colocalization analysis were also presented. Drug prediction and molecular docking showed that some drugs and proteins have good binding affinity and available structural data. CONCLUSIONS Our study indicates genetic correlations and shared risk loci between anxiety, depression, and schizophrenia. These findings offer insights into the underlying mechanisms of their comorbidities and aid in drug development.
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Affiliation(s)
- Yiming Tao
- Department of Intensive Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hankou, Wuhan, 430030, China
- Department of Critical Care Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250101, Shandong, China
| | - Rui Zhao
- Department of Laboratory Medicine, The First Afliated Hospital of Chongqing Medical University, Chongqing, 400042, China
| | - Bin Yang
- Department of Intensive Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hankou, Wuhan, 430030, China
| | - Jie Han
- Department of Emergency, School of Medicine, Qingdao Municipal Hospital, Qingdao University, Qingdao, 266071, China.
| | - Yongsheng Li
- Department of Intensive Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hankou, Wuhan, 430030, China.
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Yang H, Zhang Z, Zhang N, Li T, Wang J, Zhang Q, Xue J, Zhu W, Xu S. QTL mapping for plant height and ear height using bi-parental immortalized heterozygous populations in maize. FRONTIERS IN PLANT SCIENCE 2024; 15:1371394. [PMID: 38590752 PMCID: PMC10999566 DOI: 10.3389/fpls.2024.1371394] [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: 01/16/2024] [Accepted: 03/06/2024] [Indexed: 04/10/2024]
Abstract
Introduction Plant height (PH) and ear height (EH) are key plant architectural traits in maize, which will affect the photosynthetic efficiency, high plant density tolerance, suitability for mechanical harvesting. Methods QTL mapping were conducted for PH and EH using a recombinant inbred line (RIL) population and two corresponding immortalized backcross (IB) populations obtained from crosses between the RIL population and the two parental lines. Results A total of 17 and 15 QTL were detected in the RIL and IB populations, respectively. Two QTL, qPH1-1 (qEH1-1) and qPH1-2 (qEH1-4) in the RIL, were simultaneously identified for PH and EH. Combing reported genome-wide association and cloned PH-related genes, co-expression network analyses were constructed, then five candidate genes with high confidence in major QTL were identified including Zm00001d011117 and Zm00001d011108, whose homologs have been confirmed to play a role in determining PH in maize and soybean. Discussion QTL mapping used a immortalized backcross population is a new strategy. These identified genes in this study can provide new insights for improving the plant architecture in maize.
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Affiliation(s)
| | | | | | | | | | | | | | - Wanchao Zhu
- College of Agronomy, Key Laboratory of Maize Biology and Genetic Breeding in Arid Area of Northwest Region, Yangling, Shaanxi, China
| | - Shutu Xu
- College of Agronomy, Key Laboratory of Maize Biology and Genetic Breeding in Arid Area of Northwest Region, Yangling, Shaanxi, China
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Tian D, Xu T, Kang H, Luo H, Wang Y, Chen M, Li R, Ma L, Wang Z, Hao L, Tang B, Zou D, Xiao J, Zhao W, Bao Y, Zhang Z, Song S. Plant genomic resources at National Genomics Data Center: assisting in data-driven breeding applications. ABIOTECH 2024; 5:94-106. [PMID: 38576435 PMCID: PMC10987443 DOI: 10.1007/s42994-023-00134-4] [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: 11/10/2023] [Accepted: 12/18/2023] [Indexed: 04/06/2024]
Abstract
Genomic data serve as an invaluable resource for unraveling the intricacies of the higher plant systems, including the constituent elements within and among species. Through various efforts in genomic data archiving, integrative analysis and value-added curation, the National Genomics Data Center (NGDC), which is a part of the China National Center for Bioinformation (CNCB), has successfully established and currently maintains a vast amount of database resources. This dedicated initiative of the NGDC facilitates a data-rich ecosystem that greatly strengthens and supports genomic research efforts. Here, we present a comprehensive overview of central repositories dedicated to archiving, presenting, and sharing plant omics data, introduce knowledgebases focused on variants or gene-based functional insights, highlight species-specific multiple omics database resources, and briefly review the online application tools. We intend that this review can be used as a guide map for plant researchers wishing to select effective data resources from the NGDC for their specific areas of study. Supplementary Information The online version contains supplementary material available at 10.1007/s42994-023-00134-4.
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Affiliation(s)
- Dongmei Tian
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
| | - Tianyi Xu
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
| | - Hailong Kang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Hong Luo
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
| | - Yanqing Wang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
| | - Meili Chen
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
| | - Rujiao Li
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
| | - Lina Ma
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
| | - Zhonghuang Wang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Lili Hao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
| | - Bixia Tang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
| | - Dong Zou
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
| | - Jingfa Xiao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Wenming Zhao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Yiming Bao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Zhang Zhang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Shuhui Song
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
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Roces V, Guerrero S, Álvarez A, Pascual J, Meijón M. PlantFUNCO: Integrative Functional Genomics Database Reveals Clues into Duplicates Divergence Evolution. Mol Biol Evol 2024; 41:msae042. [PMID: 38411627 PMCID: PMC10917205 DOI: 10.1093/molbev/msae042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 02/08/2024] [Accepted: 02/16/2024] [Indexed: 02/28/2024] Open
Abstract
Evolutionary epigenomics and, more generally, evolutionary functional genomics, are emerging fields that study how non-DNA-encoded alterations in gene expression regulation are an important form of plasticity and adaptation. Previous evidence analyzing plants' comparative functional genomics has mostly focused on comparing same assay-matched experiments, missing the power of heterogeneous datasets for conservation inference. To fill this gap, we developed PlantFUN(ctional)CO(nservation) database, which is constituted by several tools and two main resources: interspecies chromatin states and functional genomics conservation scores, presented and analyzed in this work for three well-established plant models (Arabidopsis thaliana, Oryza sativa, and Zea mays). Overall, PlantFUNCO elucidated evolutionary information in terms of cross-species functional agreement. Therefore, providing a new complementary comparative-genomics source for assessing evolutionary studies. To illustrate the potential applications of this database, we replicated two previously published models predicting genetic redundancy in A. thaliana and found that chromatin states are a determinant of paralogs degree of functional divergence. These predictions were validated based on the phenotypes of mitochondrial alternative oxidase knockout mutants under two different stressors. Taking all the above into account, PlantFUNCO aim to leverage data diversity and extrapolate molecular mechanisms findings from different model organisms to determine the extent of functional conservation, thus, deepening our understanding of how plants epigenome and functional noncoding genome have evolved. PlantFUNCO is available at https://rocesv.github.io/PlantFUNCO.
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Affiliation(s)
- Víctor Roces
- Plant Physiology, Department of Organisms and Systems Biology, Faculty of Biology and Biotechnology Institute of Asturias, University of Oviedo, Asturias, Spain
| | - Sara Guerrero
- Plant Physiology, Department of Organisms and Systems Biology, Faculty of Biology and Biotechnology Institute of Asturias, University of Oviedo, Asturias, Spain
| | - Ana Álvarez
- Plant Physiology, Department of Organisms and Systems Biology, Faculty of Biology and Biotechnology Institute of Asturias, University of Oviedo, Asturias, Spain
| | - Jesús Pascual
- Plant Physiology, Department of Organisms and Systems Biology, Faculty of Biology and Biotechnology Institute of Asturias, University of Oviedo, Asturias, Spain
| | - Mónica Meijón
- Plant Physiology, Department of Organisms and Systems Biology, Faculty of Biology and Biotechnology Institute of Asturias, University of Oviedo, Asturias, Spain
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10
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Gao Y, Zhou Q, Luo J, Xia C, Zhang Y, Yue Z. Crop-GPA: an integrated platform of crop gene-phenotype associations. NPJ Syst Biol Appl 2024; 10:15. [PMID: 38346982 PMCID: PMC10861494 DOI: 10.1038/s41540-024-00343-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 01/22/2024] [Indexed: 02/15/2024] Open
Abstract
With the increasing availability of large-scale biology data in crop plants, there is an urgent demand for a versatile platform that fully mines and utilizes the data for modern molecular breeding. We present Crop-GPA ( https://crop-gpa.aielab.net ), a comprehensive and functional open-source platform for crop gene-phenotype association data. The current Crop-GPA provides well-curated information on genes, phenotypes, and their associations (GPAs) to researchers through an intuitive interface, dynamic graphical visualizations, and efficient online tools. Two computational tools, GPA-BERT and GPA-GCN, are specifically developed and integrated into Crop-GPA, facilitating the automatic extraction of gene-phenotype associations from bio-crop literature and predicting unknown relations based on known associations. Through usage examples, we demonstrate how our platform enables the exploration of complex correlations between genes and phenotypes in crop plants. In summary, Crop-GPA serves as a valuable multi-functional resource, empowering the crop research community to gain deeper insights into the biological mechanisms of interest.
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Affiliation(s)
- Yujia Gao
- School of Information and Artificial Intelligence, Anhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei, Anhui, 230036, China
| | - Qian Zhou
- School of Information and Artificial Intelligence, Anhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei, Anhui, 230036, China
| | - Jiaxin Luo
- School of Information and Artificial Intelligence, Anhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei, Anhui, 230036, China
| | - Chuan Xia
- School of Information and Artificial Intelligence, Anhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei, Anhui, 230036, China
| | - Youhua Zhang
- School of Information and Artificial Intelligence, Anhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei, Anhui, 230036, China.
| | - Zhenyu Yue
- School of Information and Artificial Intelligence, Anhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei, Anhui, 230036, China.
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Lang X, Yu C, Shen M, Gu L, Qian Q, Zhou D, Tan J, Li Y, Peng X, Diao S, Deng Z, Ruan Z, Xu Z, Xing J, Li C, Wang R, Ding C, Cao Y, Liu Q. PRMD: an integrated database for plant RNA modifications. Nucleic Acids Res 2024; 52:D1597-D1613. [PMID: 37831097 PMCID: PMC10768107 DOI: 10.1093/nar/gkad851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 08/23/2023] [Accepted: 09/23/2023] [Indexed: 10/14/2023] Open
Abstract
The scope and function of RNA modifications in model plant systems have been extensively studied, resulting in the identification of an increasing number of novel RNA modifications in recent years. Researchers have gradually revealed that RNA modifications, especially N6-methyladenosine (m6A), which is one of the most abundant and commonly studied RNA modifications in plants, have important roles in physiological and pathological processes. These modifications alter the structure of RNA, which affects its molecular complementarity and binding to specific proteins, thereby resulting in various of physiological effects. The increasing interest in plant RNA modifications has necessitated research into RNA modifications and associated datasets. However, there is a lack of a convenient and integrated database with comprehensive annotations and intuitive visualization of plant RNA modifications. Here, we developed the Plant RNA Modification Database (PRMD; http://bioinformatics.sc.cn/PRMD and http://rnainformatics.org.cn/PRMD) to facilitate RNA modification research. This database contains information regarding 20 plant species and provides an intuitive interface for displaying information. Moreover, PRMD offers multiple tools, including RMlevelDiff, RMplantVar, RNAmodNet and Blast (for functional analyses), and mRNAbrowse, RNAlollipop, JBrowse and Integrative Genomics Viewer (for displaying data). Furthermore, PRMD is freely available, making it useful for the rapid development and promotion of research on plant RNA modifications.
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Affiliation(s)
- Xiaoqiang Lang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
- Microbiology and Metabolic Engineering Key Laboratory of Sichuan Province, College of Life Science, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Chunyan Yu
- Frontiers Science Center for Disease-related Molecular Network, Laboratory of Omics Technology and Bioinformatics, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Mengyuan Shen
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Guangdong Key Laboratory of New Technology in Rice Breeding, Guangdong Rice Engineering Laboratory, Guangzhou, 510640, China
| | - Lei Gu
- Epigenetics Laboratory, Max Planck Institute for Heart and Lung Research & Cardiopulmonary Institute (CPI). Parkstr.1 61231 Bad Nauheim Germany
| | - Qian Qian
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Guangdong Key Laboratory of New Technology in Rice Breeding, Guangdong Rice Engineering Laboratory, Guangzhou, 510640, China
| | - Degui Zhou
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Guangdong Key Laboratory of New Technology in Rice Breeding, Guangdong Rice Engineering Laboratory, Guangzhou, 510640, China
| | - Jiantao Tan
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Guangdong Key Laboratory of New Technology in Rice Breeding, Guangdong Rice Engineering Laboratory, Guangzhou, 510640, China
| | - Yiliang Li
- Guangdong Provincial Key Laboratory of Silviculture, Protection and Utilization/Guangdong Academy of Forestry, Guangzhou, Guangdong 510520, China
| | - Xin Peng
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Guangdong Key Laboratory of New Technology in Rice Breeding, Guangdong Rice Engineering Laboratory, Guangzhou, 510640, China
| | - Shu Diao
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou, China
| | - Zhujun Deng
- Precision Medicine Center, Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Zhaohui Ruan
- Sun Yat-sen University Cancer Center, State Key Laboratory Oncology in South China, Collaborative Innovation Center of Cancer Medicine, 510060, Guangzhou, China
| | - Zhi Xu
- Guangxi Key Laboratory of Images and Graphics Intelligent Processing, Guilin University of Electronics Technology, Guilin, 541004, China
| | - Junlian Xing
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Guangdong Key Laboratory of New Technology in Rice Breeding, Guangdong Rice Engineering Laboratory, Guangzhou, 510640, China
| | - Chen Li
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Guangdong Key Laboratory of New Technology in Rice Breeding, Guangdong Rice Engineering Laboratory, Guangzhou, 510640, China
| | - Runfeng Wang
- Guangdong Provincial Key Laboratory of Crop Genetic Improvement, Crops Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Changjun Ding
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Yi Cao
- Microbiology and Metabolic Engineering Key Laboratory of Sichuan Province, College of Life Science, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Qi Liu
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Guangdong Key Laboratory of New Technology in Rice Breeding, Guangdong Rice Engineering Laboratory, Guangzhou, 510640, China
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12
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Cao Y, Tian D, Tang Z, Liu X, Hu W, Zhang Z, Song S. OPIA: an open archive of plant images and related phenotypic traits. Nucleic Acids Res 2024; 52:D1530-D1537. [PMID: 37930849 PMCID: PMC10767956 DOI: 10.1093/nar/gkad975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 10/11/2023] [Accepted: 10/16/2023] [Indexed: 11/08/2023] Open
Abstract
High-throughput plant phenotype acquisition technologies have been extensively utilized in plant phenomics studies, leading to vast quantities of images and image-based phenotypic traits (i-traits) that are critically essential for accelerating germplasm screening, plant diseases identification and biotic & abiotic stress classification. Here, we present the Open Plant Image Archive (OPIA, https://ngdc.cncb.ac.cn/opia/), an open archive of plant images and i-traits derived from high-throughput phenotyping platforms. Currently, OPIA houses 56 datasets across 11 plants, comprising a total of 566 225 images with 2 417 186 labeled instances. Notably, it incorporates 56 i-traits of 93 rice and 105 wheat cultivars based on 18 644 individual RGB images, and these i-traits are further annotated based on the Plant Phenotype and Trait Ontology (PPTO) and cross-linked with GWAS Atlas. Additionally, each dataset in OPIA is assigned an evaluation score that takes account of image data volume, image resolution, and the number of labeled instances. More importantly, OPIA is equipped with useful tools for online image pre-processing and intelligent prediction. Collectively, OPIA provides open access to valuable datasets, pre-trained models, and phenotypic traits across diverse plants and thus bears great potential to play a crucial role in facilitating artificial intelligence-assisted breeding research.
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Affiliation(s)
- Yongrong Cao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dongmei Tian
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Zhixin Tang
- University of Chinese Academy of Sciences, Beijing 100049, China
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Xiaonan Liu
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weijuan Hu
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhang Zhang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shuhui Song
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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