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

对源自（元）基因组数据的复杂大型网络进行统计评估

• 批准号: RGPIN-2014-04512
• 负责人: Lapointe, FrançoisJoseph
• 金额: $ 1.75万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=566568
• 关键词: Statistical; assessment; complex; large; networks

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.

在过去的十年中，测序技术的进步从包括原核生物和真核生物在内的无数生物中产生了巨大的序列，并以持续的成本降低以及序列读取的数量和长度增加而做到了。此外，宏基因组目前正在直接从环境或组织样品中提供前所未有的DNA序列，这些序列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。生物信息学资源已成为组织，集成以及这些大量数据的关键瓶颈。结果，生物学家只专注于部分数据的分析，而没有探索大量数据。该建议的目的是开发一系列统计和图理论方法，以使用基于网络的方法有效地分析大分子数据集。网络通常是指基于数学图的一组离散实体的表示及其在多维空间中的相互作用。网络是用于分析生物系统及其成分之间复杂关系的有用概念工具；它们可以应用于基因组，元基因组和元文字数据，其中单个实体（节点）可以用基因，基因型，个体，物种，地理位置或此类组织水平的任何组合来表示。连接此类节点的链接（边缘）可以代表系统发育距离，遗传相似性或地理距离。如何从此类大规模网络中提取有用的信息并通过时间和整个空间评估其结构是研究人员急剧兴趣的问题。但是，我们缺乏识别和比较不同微生物栖息地之间这些网络的统计框架。该建议旨在开发有效而有力的统计方法，以解决大型网络数据集中三种类型的问题：（1）如何衡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，我打算设计新颖的多样性指数，以表征巨型元基因组网络的复杂网状连接。然后，这些指标将用于检测基因组网络中遗传上不同的对象，并估算准确测量网络遗传多样性所需的采样工作。我还将开发有效比较大量元基因组网络拓扑的方法。然后，这些方法将应用于在不同时间收集的环境样本中检测一致模式，并比较元基因组和元文字网络。最后，我将提出用于测试代表不同生物系统的复杂网络中常见进化过程的统计零模型。然后，这些统计模型将用于检测基因组网络中的横向基因转移并评估元基因组网络的可塑性。随着对各种基因组，元基因组和相互作用数据集的应用，提出的统计框架对对时间和空间的网络动态和复杂性的研究感兴趣，尤其是在人类微生物组中具有很大的价值。这种新型和健壮的基于网络的分子多样性及其时空变异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生物组织多个级别的系统动力学和稳定性至关重要。