Carbohydrate enzyme gene clusters in human gut microbiome

人类肠道微生物组中的碳水化合物酶基因簇

• 批准号: 10594096
• 负责人: Yanbin Yin
• 金额: $ 28.25万
• 依托单位: UNIVERSITY OF NEBRASKA LINCOLN
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10594096
• 关键词: Activities of Daily Living; Address; Applications Grants; Artificial Intelligence; Automated Annotation; Award; Bacteria; Bacterial Genome; Bioinformatics; Carbohydrates; Collection; Communities; Computers; DNA; Data; Data Reporting; Data Scientist; Databases; Development; Dietary Intervention; Documentation; Enzymes; Faculty; Funding; Gene Cluster; Gene Order; Genes; Genome; Goals; Health; Human; Human Microbiome; Interdisciplinary Study; Label; Literature; Machine Learning; Manuals; Metabolic; Metadata; Metagenomics; Nutrient; Nutritional Science; Nutritional Study; Output; Parents; Persons; Polysaccharides; Postdoctoral Fellow; Process; Publications; Published Database; Publishing; Pythons; Readability; Readiness; Research; Running; Science; Scientist; Shotguns; Software Tools; Source Code; Standardization; Students; Supervision; Text; Training; United States National Institutes of Health; Update; Whole-Genome Shotgun Sequencing; acronyms; base; bioinformatics tool; data format; data repository; data standards; data structure; design; dietary; doctoral student; electronic data; file format; gastrointestinal; graduate student; gut microbes; gut microbiome; gut microbiota; improved; interest; machine learning model; member; metagenome; microbiome; microbiome sequencing; model development; novel; nutrition; parent project; prebiotics; programs; statistical and machine learning; statistics; supervised learning; tool; unsupervised learning; web site; whole genome; Activities of Daily Living; Address; Applications Grants; Artificial Intelligence; Automated Annotation; Award; Bacteria; Bacterial Genome; Bioinformatics; Carbohydrates; Collection; Communities; Computers; DNA; Data; Data Reporting; Data Scientist; Databases; Development; Dietary Intervention; Documentation; Enzymes; Faculty; Funding; Gene Cluster; Gene Order; Genes; Genome; Goals; Health; Human; Human Microbiome; Interdisciplinary Study; Label; Literature; Machine Learning; Manuals; Metabolic; Metadata; Metagenomics; Nutrient; Nutritional Science; Nutritional Study; Output; Parents; Persons; Polysaccharides; Postdoctoral Fellow; Process; Publications; Published Database; Publishing; Pythons; Readability; Readiness; Research; Running; Science; Scientist; Shotguns; Software Tools; Source Code; Standardization; Students; Supervision; Text; Training; United States National Institutes of Health; Update; Whole-Genome Shotgun Sequencing; acronyms; base; bioinformatics tool; data format; data repository; data standards; data structure; design; dietary; doctoral student; electronic data; file format; gastrointestinal; graduate student; gut microbes; gut microbiome; gut microbiota; improved; interest; machine learning model; member; metagenome; microbiome; microbiome sequencing; model development; novel; nutrition; parent project; prebiotics; programs; statistical and machine learning; statistics; supervised learning; tool; unsupervised learning; web site; whole genome

PROJECT SUMMARY Carbohydrate enzyme gene clusters in human gut microbiome Our R01 parent project (R01GM140370) intends to develop four bioinformatics tools for automated annotation of CAZymes (Carbohydrate Active Enzymes) and CAZyme Gene Clusters (CGCs) in human gut microbiome. These automated tools will enhance: (i) the basic biomedical science to characterize new polysaccharide (or glycan) metabolic enzymes and polysaccharide utilization loci (PULs, gene clusters with known carbohydrate substrates) in the human gut microbiome, and (ii) the emerging personalized nutrition practice (e.g., using gut microbiome sequencing to infer if a person is a responder to certain dietary glycans or prebiotics). Two types of data are needed for AI/ML applications: (1) PULs (experimentally characterized gene clusters with known carbohydrate substrates) curated from literature, and (2) CGCs (without known carbohydrate substrates) predicted from human microbiome. Although the parent R01 project focuses on the development of new ML tools, challenges exist and additional support is needed to enable AI/ML-readiness for the data used/produced in the parent project. These challenges include: (i) the training data size is small, and more PULs await to be curated from literature which is not supported by the parent R01 project, (ii) the parent R01 project does not consider making the PULs and CGCs AI/ML-ready to other data scientists than ourselves, (iii) a significant improvement is needed for the PUL/CGC data representation, documentation, and pre-processing, as the current data structure is only designed for domain experts of CAZymes and PULs, (iv) an update of existing software tools will be necessary to output CGCs in a more computer-readable format, in order to enable AI/ML-readiness. Therefore, the major goal of this AI/ML-readiness project is to develop a consistent, standardized, and systematic format of PULs and CGCs to make them AI/ML ready to not only the parent R01 project but also to other data scientists and nutrition scientists. To achieve this goal, we have assembled a multi-disciplinary research team including three faculty, one postdoc, and three graduate students. These members have all necessary expertise in nutritional science and CAZymes, statistical ML model development, and bioinformatics and ML application development. Two Aims with four subtasks and four milestones are planned to address the aforementioned challenges and make the PUL and CGC data formatted and documented in a way that they can be readily available to other data scientists and nutrition scientists. All AI/ML-ready data will be freely available on two online data repositories: dbCAN-PUL (http://bcb.unl.edu/dbCAN_PUL/) and dbCAN-seq (http://bcb.unl.edu/dbCAN_seq/). This project will contribute to the basic understanding of dietary modulation of human microbiome and applied personalized nutrition research.

项目摘要 人肠道微生物组中的碳水化合物酶基因簇 我们的R01父项目（R01GM140370）打算开发四种自动注释的生物信息学工具 人类肠道微生物组中的Cazymes（碳水化合物活性酶）和Cazyme基因簇（CGC）。 这些自动化工具将增强：（i）特征新多糖的基本生物医学科学（或 Glycan）代谢酶和多糖利用基因座（脉冲，具有已知碳水化合物的基因簇 底物）在人类肠道微生物组中，以及（ii）新兴的个性化营养实践（例如，使用肠 微生物组测序是否是对某些饮食类似物或益生元的响应者的响应者。 AI/ML应用需要两种类型的数据：（1）PUL（实验表征的基因簇 通过文献策划的已知碳水化合物底物和（2）CGC（没有已知的碳水化合物 底物）由人类微生物组预测。 尽管父母R01项目的重点是开发新的ML工具，但存在挑战，并且 需要额外的支持以启用对父项目中使用/生成的数据的AI/ML准备就绪。 这些挑战包括：（i）培训数据大小很小，更多的脉冲正在等待文献来策划 父母R01项目不支持这一点，（ii）父级R01项目不考虑制作 除我们自己以外的其他数据科学家，PULS和CGCS AI/ML准备就绪，（iii）需要显着改进 对于PUL/CGC数据表示，文档和预处理，因为当前数据结构仅是 （iv）为Cazymes和pul的领域专家设计，需要对现有软件工具的更新进行更新 以更加可读的格式输出CGC，以启用AI/ML-RERESTINSE。 因此，该AI/ML RERRITINSE项目的主要目标是开发一致，标准化和 脉冲和CGC的系统格式使它们不仅可以让父级R01项目，还可以 其他数据科学家和营养科学家。为了实现这一目标，我们组装了一个多学科 研究团队包括三个教职员工，一名博士后和三名研究生。这些成员有 营养科学和Cazymes，统计ML模型开发和生物信息学的必要专业知识 和ML应用程序开发。计划针对四个子任务和四个里程碑的两个目标来解决 前面提到的挑战，使PUL和CGC数据以他们 其他数据科学家和营养科学家可以很容易获得。所有AI/ML准备就绪的数据将是自由的 可在两个在线数据存储库中提供：DBCAN-PUL（http://bcb.unl.edu/dbcan_pul/）和dbcan-seq （http://bcb.unl.edu/dbcan_seq/）。该项目将有助于对饮食调制的基本理解 人类微生物组和应用个性化营养研究。