Statistical Methods for Microbiome and Metagenomics

摘要 该项目的广泛、长期目标涉及开发新的统计方法和计算。 用于人类微生物组和鸟枪式元基因组数据的统计和概率建模的常规工具 重要的生物学问题和实验。随着我们进入微生物组研究的下一阶段，它已经成为 越来越明显的是，缺乏适合分析如此大规模微生物组数据的方法已经成为一种 有效了解微生物区系的功能和动态的瓶颈。迫切需要发展 大规模鸟枪式元基因组数据分析的统计和计算方法 微生物组数据科学的创新。该项目旨在通过开发新的统计模型来缩小这一差距， 新颖的推理程序和快速的计算算法。当前项目的Speciific目标主要集中在两个方面 微生物组数据分析的重要方面：(1)发展新的统计方法和快速计算算法。 大规模人类微生物组研究中元基因组测序数据的基于系统基因组分析的Rithms (2)发展统计方法和推断程序，以量化和比较潜在能级土地- 微生物群落的景观和稳定性。在这两个大目标下，几种相关的统计方法 将被开发来解决如何进行基于系统基因组学的微生物组分析以及如何进行这些关键问题 量化微生物群落稳定性，并将其与疾病风险和进展联系起来。这些问题都是由 PI与宾夕法尼亚大学研究人员在克罗恩病、儿童肥胖症和儿童肥胖的元基因组研究方面的密切合作 慢性肾脏病(CKD)患者的疾病进展)。特别是fi，这个项目将开发冰毒- 使用一组通用标记基因、系统发育-伊辛模型进行基于系统发育学关联分析的ODS 以及用于评估微生物群落能量格局和稳定性的变点系统发育伊辛模型， 基于纵向微生物组理解共识分类单元-分类单元相互作用的时不变Ising模型 学习。新方法可以应用于16S rRNA和鸟枪式元基因组测序数据，并将 理想地促进了fi阳离子的微生物组成，群落稳定性和微生物网络的基础变量- 借条是人类复杂的疾病和生物过程。该项目还将调查健壮性、功率和 比较了这些方法的EFfi精度，并与现有的方法进行了比较。最后，本项目将发展出实用性和可操作性 用于实施所提出的方法和用于评估性能的可行的计算机程序 通过对PI的各种正在进行的微生物组研究进行广泛的模拟和分析，对这些方法进行了比较 与宾夕法尼亚大学的医生和生物学家合作。根据该赠款和详细文件开发的所有计划- TION将被整合到我们目前的软件中，并向感兴趣的研究人员免费提供。

Abstract The broad, long-term objective of this project concerns the development of novel statistical methods and computa- tional tools for statistical and probabilistic modeling of human microbiome and shotgun metagenomic data motivated by important biological questions and experiments. As we move to the next phase of microbiome research, it has become increasingly evident that lack of methods suitable for analyzing such large-scale microbiome data has emerged as a bottleneck to effectively understand the functions and dynamics of microbiota. There is a pressing need to develop statistical and computational methods for large-scale shotgun metagenomics data analysis in order to accelerate in- novations in microbiome data science. This project aims at narrowing this gap by developing new statistical models, novel inference procedures, and fast computational algorithms. The specific aims of the current project focus on two important aspects of microbiome data analysis: (1) developing new statistical methods and fast computational algo- rithms for phylogenomic-based analysis of metagenomic sequencing data in large-scale human microbiome studies; (2) developing statistical methods and inference procedures for quantifying and comparing the potential energy land- scape and stability of microbial communities. Under each of these two broad aims, several related statistical methods will be developed to address the key questions of how to perform phylogenomics-based microbiome analysis and how to quantify and link microbial community stability to disease risk and progression. These problems are all motivated by the PI's close collaborations with Penn investigators on metagenomic studies of Crohn disease, childhood obesity and disease progression among patients with chronic kidney disease (CKD)). Specifically, this project will develop meth- ods for phylogenomics-based association analysis using a set of universal marker genes, phylogenetic-Ising models and change-point phylogenetic-Ising models for assessing the microbial community energy landscape and stability, and time-invariant Ising models for understand consensus taxon-taxon interactions based on longitudinal microbiome studies. The new methods can be applied to both 16S rRNA and shotgun metagenomic sequencing data and will ideally facilitate the identifications of microbial composition, community stability and microbial networks underlying var- ious complex human diseases and biological processes. The project will also investigate the robustness, power and efficiencies of these methods and compare them with existing methods. Finally, this project will develop practical and feasible computer programs for the implementation of the proposed methods, and for the evaluation of the performance of these methods through extensive simulations and analysis of various on-going microbiome studies through the PI's collaborations with Penn physicians and biologists. All programs developed under this grant and detailed documenta- tion will be incorporated into our current software and made available free-of-charge to interested researchers.