Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have rev...Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have revolutionized the field,enabling rapid and accurate assessment of crop traits on a large scale.The integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop improvement.AI algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic factors.These technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection,thereby reducing the time and cost required for variety development.However,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop improvement.By leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop breeding.The aim of this review is to explore the transformative potential of integrating HTP and AI in crop improvement.This review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI.展开更多
Sheep are one of the most economically important domesticated animals for human society. However, genetic improvements for the key traits associated with meat, growth, milk, wool, reproduction, horns and tails progres...Sheep are one of the most economically important domesticated animals for human society. However, genetic improvements for the key traits associated with meat, growth, milk, wool, reproduction, horns and tails progress slowly using conventional crossbreeding methods. With the development and utilization of highthroughput screening technologies over the last decade, a list of functional genes and genetic variants associated with these traits has been identified. This review covers recent genome-wide studies on sheep productive traits using high-throughput screening technologies, including those based on single-nucleotide polymorphisms and copy number variants at the whole-genome level(e.g.,genome-wide association studies), transcriptome and DNA methylation sequences. Additionally, comprehensive information on functional genes and genetic variants associated with economically important traits in sheep is provided.展开更多
The cryptic lifestyle of most fungi necessitates molecular identification of the guild in environmental studies.Over the past decades,rapid development and affordability of molecular tools have tremendously improved i...The cryptic lifestyle of most fungi necessitates molecular identification of the guild in environmental studies.Over the past decades,rapid development and affordability of molecular tools have tremendously improved insights of the fungal diversity in all ecosystems and habitats.Yet,in spite of the progress of molecular methods,knowledge about functional properties of the fungal taxa is vague and interpretation of environmental studies in an ecologically meaningful manner remains challenging.In order to facilitate functional assignments and ecological interpretation of environmental studies we introduce a user friendly traits and character database FungalTraits operating at genus and species hypothesis levels.Combining the information from previous efforts such as FUNGuild and FunFun together with involvement of expert knowledge,we reannotated 10,210 and 151 fungal and Stramenopila genera,respectively.This resulted in a stand-alone spreadsheet dataset covering 17 lifestyle related traits of fungal and Stramenopila genera,designed for rapid functional assignments of environmental stud-ies.In order to assign the trait states to fungal species hypotheses,the scientific community of experts manually categorised and assigned available trait information to 697,413 fungal ITS sequences.On the basis of those sequences we were able to summarise trait and host information into 92,623 fungal species hypotheses at 1%dissimilarity threshold.展开更多
基金supported by a grant from the Standardization and Integration of Resources Information for Seed-cluster in Hub-Spoke Material Bank Program,Rural Development Administration,Republic of Korea(PJ01587004).
文摘Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have revolutionized the field,enabling rapid and accurate assessment of crop traits on a large scale.The integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop improvement.AI algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic factors.These technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection,thereby reducing the time and cost required for variety development.However,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop improvement.By leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop breeding.The aim of this review is to explore the transformative potential of integrating HTP and AI in crop improvement.This review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI.
基金financially supported by the National Natural Science Foundation of China (31272413, 3161101336)the National Transgenic Breeding Project of China (2014ZX0800952B)+1 种基金the External Cooperation Program of Chinese Academy of Sciences (152111KYSB20150010)the Taishan Scholars Program of Shandong Province (201511085)
文摘Sheep are one of the most economically important domesticated animals for human society. However, genetic improvements for the key traits associated with meat, growth, milk, wool, reproduction, horns and tails progress slowly using conventional crossbreeding methods. With the development and utilization of highthroughput screening technologies over the last decade, a list of functional genes and genetic variants associated with these traits has been identified. This review covers recent genome-wide studies on sheep productive traits using high-throughput screening technologies, including those based on single-nucleotide polymorphisms and copy number variants at the whole-genome level(e.g.,genome-wide association studies), transcriptome and DNA methylation sequences. Additionally, comprehensive information on functional genes and genetic variants associated with economically important traits in sheep is provided.
基金Estonian Science Foundation grants PSG136,PRG632,PUT1170the University of Tartu(PLTOM20903)the European Regional Development Fund(Centre of Excellence EcolChange).
文摘The cryptic lifestyle of most fungi necessitates molecular identification of the guild in environmental studies.Over the past decades,rapid development and affordability of molecular tools have tremendously improved insights of the fungal diversity in all ecosystems and habitats.Yet,in spite of the progress of molecular methods,knowledge about functional properties of the fungal taxa is vague and interpretation of environmental studies in an ecologically meaningful manner remains challenging.In order to facilitate functional assignments and ecological interpretation of environmental studies we introduce a user friendly traits and character database FungalTraits operating at genus and species hypothesis levels.Combining the information from previous efforts such as FUNGuild and FunFun together with involvement of expert knowledge,we reannotated 10,210 and 151 fungal and Stramenopila genera,respectively.This resulted in a stand-alone spreadsheet dataset covering 17 lifestyle related traits of fungal and Stramenopila genera,designed for rapid functional assignments of environmental stud-ies.In order to assign the trait states to fungal species hypotheses,the scientific community of experts manually categorised and assigned available trait information to 697,413 fungal ITS sequences.On the basis of those sequences we were able to summarise trait and host information into 92,623 fungal species hypotheses at 1%dissimilarity threshold.
