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

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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 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. **