A statistical framework for the evaluation and comparison of complex networks and its application to microbiome research

在过去的十年中，测序技术的进步已经从包括原核生物和真核生物在内的无数生物中产生了大量的序列，并且随着成本的不断降低和序列读取的数量和长度的增加而实现了这一点。此外，元基因组学目前直接从环境或组织样本中提供了前所未有的丰富的DNA序列，可以用来详细描述使用复杂网络的生物系统的巨大多样性。*网络一般指的是一组离散实体及其在多维空间中的交互的基于图的数学表示。网络是分析生物系统及其组成部分之间复杂关系的有用概念工具；它们可用于基因组和元基因组数据，在这些数据中，单个实体(节点)可由序列、个体、物种、地理位置或这些组织级别的任何组合来表示。连接这些节点的链接(边)可以代表系统发育距离、遗传和功能相似性或地理距离。如何从这样的网络中提取有用的信息是研究人员非常感兴趣的问题。然而，我们缺乏一个适当的统计框架来识别和比较这些网络。*这个建议的目标有两个：首先，我将开发一系列统计和图论方法，使用基于网络的方法来有效地分析大型基因组和元基因组数据集。其次，我将应用这些技术来描述和比较在各种微生物组项目中产生的网络。也就是说，该提案分为五项活动。在练习1中，我将根据路径长度分布的新分类公式开发一个统计框架来评估网络。在活动2中，我将开发新的指标来基于图形主题比较网络，并提供一个统计程序来进行比较。在活动3中，我将应用这些新工具，通过比较病毒群和微生物组网络来评估蝙蝠新出现的疾病的风险。在活动4中，我将应用相同的技术来评估受白鼻子综合症影响的蝙蝠物种的皮肤微生物群和真菌生物群之间的联系。在活动5中，我将研究一种受威胁的火蜥蜴物种的皮肤微生物组，以衡量杂交事件对受到棘球菌病影响的同域和异地种群的影响。

During the last decade, advances in sequencing technologies have generated an enormous volume of sequences from a myriad of organisms including both prokaryotes and eukaryotes, and have done so with continuous cost reduction and increased numbers and lengths of sequence reads. In addition, metagenomics is currently providing an unprecedented richness of DNA sequences directly from environmental or tissue samples that can be used to describe in detail the enormous diversity of biological systems using complex networks. ***Networks generally refer to mathematical graph-based representations of a set of discrete entities and their interactions in multidimensional space. Networks are useful conceptual tools for analyzing biological systems and the complex relationships among their constituents; they can be applied to genomic and metagenomic data in which individual entities (nodes) can be represented by sequences, individuals, species, geographic locations, or any combination of such levels of organization. The links (edges) connecting such nodes can represent phylogenetic distances, genetic and functional similarities, or geographic distances. How to extract useful information from such networks is a question of acute interest for researchers. Yet we lack a proper statistical framework to identify and compare these networks.***The goal of this proposal is two fold: First, I will develop a series of statistical and graph-theoretical methods to efficiently analyze large genomic and metagenomic datasets using network-based approaches. Second, I will apply such techniques to characterize and compare networks generated in a variety of microbiome projects. Namely, the proposal is broken down in five activities. In activity 1, I will develop a statistical framework to evaluate networks based on a new categorical formulation of path length distributions. In activity 2, I will develop new metrics to compare networks based on graph motifs and a statistical procedure for doing so. In activity 3, I will apply these new tools to assess the risk of emerging diseases in bats by comparing virome and microbiome networks. In activity 4, I will apply the same techniques to evaluate the link between the skin microbiome and mycobiome in bat species affected by the white nose syndrome. In activity 5, I will study the skin microbiome of a threatened salamander species to measure the impact of hybridization events in sympatric and allopatric populations affected by chytridiomycosis. **