Biology-aware machine learning methods for characterizing microbiome genotype and phenotype

用于表征微生物组基因型和表型的生物学感知机器学习方法

• 批准号: 10798957
• 负责人: Siavash Mir arabbaygi
• 金额: $ 15.1万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10798957
• 关键词: Adopted; Algorithms; Area; Award; Awareness; Biological; Biology; Characteristics; Computing Methodologies; Data; Data Set; Environment; Genotype; Grant; Knowledge; Laboratories; Machine Learning; Measurable; Methods; Modeling; Modernization; Organism; Phenotype; Phylogenetic Analysis; Phylogeny; Process; Recording of previous events; Research; Sampling; Sequence Alignment; Services; Shapes; Statistical Methods; Techniques; Testing; Training; Work; design; genome-wide; genomic data; improved; interest; large datasets; machine learning method; microbiome; microbiome analysis; multiple data sources; statistics

PROJECT SUMMARY The Mirarab laboratory designs computational methods for answering biological and biomedical questions, fo- cusing on scalability and accuracy. These methods span several areas (e.g., microbiome profiling, multiple sequence alignment, and phylogenomics), and a common thread among them is evolutionary modeling. More recently, many of the developed methods are based on machine learning. The lab has developed scalable and accurate methods for reconstructing evolutionary histories (i.e., phylogenies) and using these histories in down- stream biomedical applications. Methods developed by this lab (e.g., ASTRAL, SEPP, DEPP) are at the fore- fronts of modern genome-wide phylogenetics. While the lab has previously focused more on inferring species histories, through an MIRA grant, it has shifted its focus to developing methods for microbiome analyses, which pose their a unique set of challenges. As part of the MIRA application, the Mirarab lab will focus on designing, testing, and applying improved methods for statistical analyses of microbiome data. These methods will target two questions. (i) Profiling: What organisms constitute a given sample? (ii) Association: How are samples different in their organismal composition, and how do these differences connect to measurable characteristics of their environment? While both questions have been subject to considerable research, many computational challenges remain, providing an opportunity for better methods to make a significant impact. Instead of focusing solely on new algorithms, the lab will also work on building better reference datasets and combining data from multiple sources. Thus, the project aims to harness the unprecedented computational power, large available datasets, and recent advances in machine learning to improve state-of-the-art dramatically. The project will not use off-the-shelf machine learning methods in a black-box fashion. Instead, it develops methods that incorporate biological knowledge (e.g., of the evolutionary relationships) into machine learning methods in a principled biologically-motivated fashion. Within the context of the MIRA award, this supplementary request is to purchase a computing server. The server will enable the lab to take advantage of the unprecedented level of genomic data available today to build machine learning methods that are trained on a much more representative set than existing methods. Thus, the extra computational power will not be just in the service of making analyses faster: it will enable using large datasets for training that could not be otherwise used.

项目概要 Mirarab 实验室设计了用于回答生物学和生物医学问题的计算方法，例如 使用可扩展性和准确性。这些方法跨越多个领域（例如，微生物组分析、多重 序列比对和系统基因组学），其中的共同点是进化建模。更多的 最近，许多开发的方法都是基于机器学习。该实验室开发了可扩展且 重建进化历史（即系统发育）并在下游使用这些历史的准确方法 流生物医学应用。本实验室开发的方法（例如ASTRAL、SEPP、DEPP）处于领先地位 现代全基因组系统发育学的前沿。虽然该实验室此前更多地专注于推断物种 历史上，通过 MIRA 的资助，它已将重点转向开发微生物组分析方法，该方法 给他们带来了一系列独特的挑战。 作为 MIRA 应用的一部分，Mirarab 实验室将专注于设计、测试和应用改进的 微生物组数据统计分析的方法。这些方法将针对两个问题。 (i) 分析： 给定样本由哪些生物体组成？ (ii) 关联：样品的有机体有何不同 组成，以及这些差异如何与其环境的可测量特征联系起来？尽管 这两个问题都经过了大量的研究，但仍然存在许多计算挑战，提供 一个采用更好方法产生重大影响的机会。不再仅仅关注新算法， 该实验室还将致力于构建更好的参考数据集并结合多个来源的数据。因此， 项目旨在利用前所未有的计算能力、大量可用数据集和最新进展 在机器学习领域大幅提高最先进水平。该项目不会使用现成的机器 以黑盒方式学习的方法。相反，它开发了结合生物学知识的方法 （例如，进化关系）转化为有原则的生物驱动的机器学习方法 时尚。 在 MIRA 奖项的背景下，该补充请求是购买一台计算服务器。这 服务器将使实验室能够利用当今可用的前所未有的基因组数据水平来构建 机器学习方法是在比现有方法更具代表性的集合上进行训练的。因此， 额外的计算能力不仅可以加快分析速度：它还可以使用大型 用于训练的数据集，无法以其他方式使用。