Statistical assessment of complex and large networks derived from (meta)genomic data

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的无数生物体产生了大量的序列，并且已经以持续的成本降低和增加的序列读数的数量和长度来实现。此外，宏基因组学目前提供了前所未有的丰富的DNA序列直接从环境或组织样本，可用于详细描述生物系统的巨大复杂性，多样性和进化动力学。生物信息学资源已经成为组织、整合和比较这些海量数据的关键瓶颈。因此，生物学家只专注于分析部分数据，留下大量未探索的数据。该提案的目的是开发一系列统计和图论方法，以使用基于网络的方法有效地分析大型分子数据集。网络通常是指多维空间中一组离散实体及其交互的基于数学图形的表示。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;它们可以应用于基因组、宏基因组和元转录组数据，其中个体实体（节点）可以由基因、基因型、个体、物种、地理位置或这些组织级别的任何组合表示。连接这些节点的链接（边）可以表示系统发育距离，遗传相似性或地理距离。如何从如此庞大的网络中提取有用的信息，并通过时间和空间来评估它们的结构，是研究人员非常感兴趣的问题。然而，我们缺乏统计框架来识别和比较不同微生物栖息地之间的这些网络。该提案旨在开发高效和强大的统计方法，以解决大型网络数据集中的三种类型的问题：（1）如何测量网络中组成节点的多样性;（2）如何比较复杂网络的拓扑模式;（3）如何使用网络分析评估进化过程。也就是说，我打算设计新的多样性指数来描述巨型宏基因组网络的复杂网状连接。这些指数将被应用于检测基因组网络中的遗传差异进化对象，并估计准确测量网络的遗传多样性所需的采样工作。我还将开发有效比较大规模宏基因组网络拓扑结构的方法。然后，这些方法将被应用于检测在不同时间收集的环境样本中的一致模式，并比较宏基因组和元转录组网络。最后，我将提出统计零模型，用于测试代表不同生物系统的复杂网络中共同进化过程的存在。这些统计模型将被应用于检测基因组网络中的横向基因转移，并评估宏基因组网络的可塑性。随着各种基因组，宏基因组和interactomic数据集的应用，所提出的统计框架将是非常有价值的研究人员感兴趣的网络动态和复杂性的时间和空间的研究，特别是在人类微生物。这种新颖而强大的基于网络的分子多样性及其时空变化的估计对于同时分析从单个病毒或细菌细胞到微生物群落的多个生物组织水平的系统动力学和稳定性至关重要。

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 complexity, diversity, and evolutionary dynamics of biological systems. Bioinformatics resources have become a critical bottleneck for the organization, integration, and comparison of these huge amounts of data. As a consequence, biologists focus on the analysis of only part of the data, leaving a large amount of data unexplored. The objective of this proposal is to develop a series of statistical and graph-theoretical methods to efficiently analyze large molecular datasets using network-based approaches. 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, metagenomic, and metatranscriptomic data in which individual entities (nodes) can be represented by genes, genotypes, individuals, species, geographic locations, or any combination of such levels of organization. Links (edges) connecting such nodes can represent phylogenetic distances, genetic similarities, or geographic distances. How to extract useful information from such massive networks and assess their structure through time and across space is a question of acute interest for researchers. Yet we lack the statistical framework to identify and compare these networks between different microbial habitats. The proposal aims at developing efficient and powerful statistical approaches to address three types of problems in large network datasets: (1) how to measure the diversity of the constituent nodes in a network; (2) how to compare topological patterns of complex networks; and (3) how to assess evolutionary processes using network analysis. Namely, I intend to design novel diversity indices to characterize the complex reticulate connections of giant metagenomic networks. These indices will then be applied to detect genetically divergent evolving objects in genome networks, and to estimate the sampling effort required to accurately measure the genetic diversity of a network. I will also develop methods to efficiently compare topologies of massive metagenome networks. These methods will then be applied to detect congruent patterns in environmental samples collected at different times, and to compare metagenomic and metatranscriptomic networks. Finally, I will propose statistical null models for testing the presence of common evolutionary processes in complex networks that represent different biological systems. These statistical models will then be applied to detect lateral gene transfer in genome networks and to assess the plasticity of metagenome networks. With applications to various genomic, metagenomic and interactomic datasets, the proposed statistical framework will be of great value to researchers interested in the study of network dynamics and complexity in time and space, and in particular within the human microbiome. Such novel and robust network-based estimates of molecular diversity and its spatio-temporal variation will be crucial to simultaneously analyze system dynamics and stability at multiple levels of biological organization, from individual viruses or bacterial cells to microbial communities.