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

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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.