Chunhui Xu, Trey Shaw, Sai Akhil Choppararu, Yiwei Lu, Shaik Naveed Farooq, Yongfang Qin, Matt Hudson, Brock Weekley, Michael Fisher, Fei He, Jose Roberto Da Silva Nascimento, Nicholas Wergeles, Trupti Joshi, Philip D Bates, Abraham J Koo, Doug K Allen, Edgar B Cahoon, Jay J Thelen, Dong Xu
{"title":"FatPlants：植物脂质相关基因和代谢途径综合信息系统。","authors":"Chunhui Xu, Trey Shaw, Sai Akhil Choppararu, Yiwei Lu, Shaik Naveed Farooq, Yongfang Qin, Matt Hudson, Brock Weekley, Michael Fisher, Fei He, Jose Roberto Da Silva Nascimento, Nicholas Wergeles, Trupti Joshi, Philip D Bates, Abraham J Koo, Doug K Allen, Edgar B Cahoon, Jay J Thelen, Dong Xu","doi":"10.1093/database/baae074","DOIUrl":null,"url":null,"abstract":"<p><p>FatPlants, an open-access, web-based database, consolidates data, annotations, analysis results, and visualizations of lipid-related genes, proteins, and metabolic pathways in plants. Serving as a minable resource, FatPlants offers a user-friendly interface for facilitating studies into the regulation of plant lipid metabolism and supporting breeding efforts aimed at increasing crop oil content. This web resource, developed using data derived from our own research, curated from public resources, and gleaned from academic literature, comprises information on known fatty-acid-related proteins, genes, and pathways in multiple plants, with an emphasis on Glycine max, Arabidopsis thaliana, and Camelina sativa. Furthermore, the platform includes machine-learning based methods and navigation tools designed to aid in characterizing metabolic pathways and protein interactions. Comprehensive gene and protein information cards, a Basic Local Alignment Search Tool search function, similar structure search capacities from AphaFold, and ChatGPT-based query for protein information are additional features. Database URL: https://www.fatplants.net/.</p>","PeriodicalId":3,"journal":{"name":"ACS Applied Electronic Materials","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11300840/pdf/","citationCount":"0","resultStr":"{\"title\":\"FatPlants: a comprehensive information system for lipid-related genes and metabolic pathways in plants.\",\"authors\":\"Chunhui Xu, Trey Shaw, Sai Akhil Choppararu, Yiwei Lu, Shaik Naveed Farooq, Yongfang Qin, Matt Hudson, Brock Weekley, Michael Fisher, Fei He, Jose Roberto Da Silva Nascimento, Nicholas Wergeles, Trupti Joshi, Philip D Bates, Abraham J Koo, Doug K Allen, Edgar B Cahoon, Jay J Thelen, Dong Xu\",\"doi\":\"10.1093/database/baae074\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>FatPlants, an open-access, web-based database, consolidates data, annotations, analysis results, and visualizations of lipid-related genes, proteins, and metabolic pathways in plants. Serving as a minable resource, FatPlants offers a user-friendly interface for facilitating studies into the regulation of plant lipid metabolism and supporting breeding efforts aimed at increasing crop oil content. This web resource, developed using data derived from our own research, curated from public resources, and gleaned from academic literature, comprises information on known fatty-acid-related proteins, genes, and pathways in multiple plants, with an emphasis on Glycine max, Arabidopsis thaliana, and Camelina sativa. Furthermore, the platform includes machine-learning based methods and navigation tools designed to aid in characterizing metabolic pathways and protein interactions. Comprehensive gene and protein information cards, a Basic Local Alignment Search Tool search function, similar structure search capacities from AphaFold, and ChatGPT-based query for protein information are additional features. Database URL: https://www.fatplants.net/.</p>\",\"PeriodicalId\":3,\"journal\":{\"name\":\"ACS Applied Electronic Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-08-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11300840/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Electronic Materials\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1093/database/baae074\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Electronic Materials","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/database/baae074","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
FatPlants: a comprehensive information system for lipid-related genes and metabolic pathways in plants.
FatPlants, an open-access, web-based database, consolidates data, annotations, analysis results, and visualizations of lipid-related genes, proteins, and metabolic pathways in plants. Serving as a minable resource, FatPlants offers a user-friendly interface for facilitating studies into the regulation of plant lipid metabolism and supporting breeding efforts aimed at increasing crop oil content. This web resource, developed using data derived from our own research, curated from public resources, and gleaned from academic literature, comprises information on known fatty-acid-related proteins, genes, and pathways in multiple plants, with an emphasis on Glycine max, Arabidopsis thaliana, and Camelina sativa. Furthermore, the platform includes machine-learning based methods and navigation tools designed to aid in characterizing metabolic pathways and protein interactions. Comprehensive gene and protein information cards, a Basic Local Alignment Search Tool search function, similar structure search capacities from AphaFold, and ChatGPT-based query for protein information are additional features. Database URL: https://www.fatplants.net/.
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