IIBR Informatics: Mixture model algorithms for inferring covariance structures and microbial associations from microbiome data

微生物群落几乎在地球上的任何地方都可以找到，它们在它们所处的环境中发挥着重要的功能作用。在一个社区中的微生物相互作用，因为他们竞争的食物和能源资源在他们的环境中。微生物之间的这些直接和间接的相互作用，称为微生物协会，在决定群落的结构，组织和功能方面发挥着重要作用。该项目解决了从使用高通量DNA测序技术生成的微生物组数据推断微生物关联的计算挑战。该项目开发的新型计算工具和资源将促进多个学科的知识进步，包括环境科学，医学和人类健康科学。该项目将有助于了解微生物生态系统的生命规则，并将进一步加深我们对微生物在环境中的生物地球化学过程和微生物相关疾病进展中所起重要作用的理解。该项目将为研究生提供跨学科培训，重点是培训代表性不足的群体（包括妇女和少数民族）。该项目还将通过开发一个教育模块，通过讲习班向高中教师介绍基因组学和生物信息学的入门主题，促进高中学生更多地参与科学、技术、工程和数学领域。微生物协会可以推断出从微生物类群丰度确定的基础协方差结构。这些丰度通常由DNA序列数据估计。然而，序列数据在本质上是组成的，在这个意义上，它们只提供相对丰度的信息分类群，这提出了挑战时，确定微生物协会。此外，微生物类群之间的关联并不总是固定的，当资源可用性和环境特征等因素发生变化时，它们会发生变化。该项目将开发新的计算方法，以确定大型微生物组数据集中协方差结构的数量，并重建微生物关联的集合。这些方法将能够从序列数据中捕获阳性和阴性微生物关联，同时处理序列数据的组成性质所带来的挑战。整体方法是基于一个混合物模型框架，结合组件分布模型微生物丰度数据。该项目将开发变分近似算法来确定给定微生物组数据集中的协方差结构的数量，快速数值优化算法来估计混合模型的参数，以及将元数据纳入分析的集成框架。该算法还将使稀疏模型的重建，从而处理的情况下，在社区中的微生物协会的数量是小的。应用这些算法分析大型微生物组数据集将对三种不同环境（人类、海洋和土壤）的微生物生态学产生新的见解。这种分析将包括在菌株水平上阐明微生物的关联，底层微生物网络的结构，以及这些环境中关键类群的身份。该项目的结果可以在www.example.com上找到https://github.com/syooseph/YoosephLab/tree/master/MixtureMicrobialNetworks.This奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

Microbial communities are found almost everywhere on earth and they play important functional roles in the environments that they are found in. Microbes in a community interact with each other as they compete for the food and energy resources available in their environment. These direct and indirect interactions between microbes, termed microbial associations, play a large role in determining the structure, organization, and function of the community. This project addresses the computational challenge of inferring microbial associations from microbiome data generated using high-throughput DNA sequencing technologies. The novel computational tools and resources developed by this project will enable the advancement of knowledge in several disciplines, including environmental sciences, medicine, and human health science. This project will contribute to understanding the rules of life for microbial ecosystems, and it will further our understanding of the important roles that microbes play in biogeochemical processes in the environment and in the progression of microbe-associated diseases. This project will provide interdisciplinary training for graduate students, with an emphasis on training under-represented groups (including women and minorities). This project will also contribute to enabling an increased level of high school student participation in STEM areas through the development of an education module that will introduce high-school teachers, via workshops, to introductory topics in genomics and bioinformatics. Microbial associations can be inferred from the underlying covariance structure that is determined from microbial taxa abundances. These abundances are often estimated from DNA sequence data. However, sequence data are compositional in nature, in the sense that they only provide relative abundance information for taxa, and this poses challenges when determining microbial associations. Furthermore, associations between groups of microbial taxa are not always fixed, and they can change when factors such as resource availability and environmental characteristics vary. This project will develop novel computational methods to determine the number of covariance structures in large microbiome datasets and to reconstruct the sets of microbial associations. These methods will be able to capture both positive and negative microbial associations from sequence data while dealing with the challenges posed by the compositional nature of sequence data. The overall approach is based on a mixture model framework incorporating component distributions that model microbial abundance data. This project will develop variational approximation algorithms to determine the number of covariance structures in a given microbiome dataset, fast numerical optimization algorithms to estimate the parameters of the mixture model, and an integrated framework to incorporate metadata in the analysis. The algorithms will also enable the reconstruction of sparse models, thus handling the scenario when the number of microbial associations in the community is small. The application of these algorithms to analyze large microbiome datasets will generate new insights into microbial ecology of three different environments (human, ocean, and soil). This analysis will include an elucidation of microbial associations at the strain level, the structures of the underlying microbial networks, and the identities of the key taxa in these environments. The results of the project can be found at https://github.com/syooseph/YoosephLab/tree/master/MixtureMicrobialNetworks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.