{"title":"Identification, characterization, and design of plant genome sequences using deep learning.","authors":"Zhenye Wang, Hao Yuan, Jianbing Yan, Jianxiao Liu","doi":"10.1111/tpj.17190","DOIUrl":null,"url":null,"abstract":"<p><p>Due to its excellent performance in processing large amounts of data and capturing complex non-linear relationships, deep learning has been widely applied in many fields of plant biology. Here we first review the application of deep learning in analyzing genome sequences to predict gene expression, chromatin interactions, and epigenetic features (open chromatin, transcription factor binding sites, and methylation sites) in plants. Then, current motif mining and functional component design and synthesis based on generative adversarial networks, large models, and attention mechanisms are elaborated in detail. The progress of protein structure and function prediction, genomic prediction, and large model applications based on deep learning is also discussed. Finally, this work provides prospects for the future development of deep learning in plants with regard to multiple omics data, algorithm optimization, large language models, sequence design, and intelligent breeding.</p>","PeriodicalId":233,"journal":{"name":"The Plant Journal","volume":" ","pages":""},"PeriodicalIF":6.2000,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Plant Journal","FirstCategoryId":"2","ListUrlMain":"https://doi.org/10.1111/tpj.17190","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PLANT SCIENCES","Score":null,"Total":0}
Identification, characterization, and design of plant genome sequences using deep learning.
Due to its excellent performance in processing large amounts of data and capturing complex non-linear relationships, deep learning has been widely applied in many fields of plant biology. Here we first review the application of deep learning in analyzing genome sequences to predict gene expression, chromatin interactions, and epigenetic features (open chromatin, transcription factor binding sites, and methylation sites) in plants. Then, current motif mining and functional component design and synthesis based on generative adversarial networks, large models, and attention mechanisms are elaborated in detail. The progress of protein structure and function prediction, genomic prediction, and large model applications based on deep learning is also discussed. Finally, this work provides prospects for the future development of deep learning in plants with regard to multiple omics data, algorithm optimization, large language models, sequence design, and intelligent breeding.
期刊介绍:
Publishing the best original research papers in all key areas of modern plant biology from the world"s leading laboratories, The Plant Journal provides a dynamic forum for this ever growing international research community.
Plant science research is now at the forefront of research in the biological sciences, with breakthroughs in our understanding of fundamental processes in plants matching those in other organisms. The impact of molecular genetics and the availability of model and crop species can be seen in all aspects of plant biology. For publication in The Plant Journal the research must provide a highly significant new contribution to our understanding of plants and be of general interest to the plant science community.