Sequence similarity networks and their large-scale applications in evolutionary biology, microbiology and ecology

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The number of molecular sequences of interest to biologists and bioinformaticians stemming from microbial genomes and the environment continues to increase exponentially. Analysis of these sequences is commonly carried out using traditional methods based on phylogenetic trees and phylogenetic networks which can take into account a relatively small number of taxa, even using computer clusters. Thus, thousands of environmental specimens and sequenced microbial genomes remain unexplored. At the same time, a promising alternative approach based on the use of sequence similarity networks is still largely under-exploited. In the framework of this Discovery grant research program, I will develop new methods and open-source software for analyzing large evolutionary and ecological datasets using sequence similarity networks. I will first design a very fast distance method for inferring and validating horizontal gene transfer (HGT) events which will be applicable to datasets comprising thousands of species and millions of sequences. This method will use the concepts of sequence similarity network and phylogenetic tree, which will be combined to analyze the mechanism of horizontal gene transfer at the metagenomic scale. Sequence similarity networks will also be used along with graph theory methods to identify mosaic genes (or chimeras), formed from DNA fragments of various origins as a result of gene fusion process (i.e. the process in which a gene transfer is followed by intragenic recombination). The proposed bioinformatics methods, accompanied by user-friendly software, will be rigorously tested through simulations, and then applied to infer and analyze: (i) Individual similarity networks built for all genes of the SARS-CoV-2 pathogen responsible for the COVID-19 pandemic, helping retrace the evolution and shed light on the origin of this dangerous virus. (ii) Intergenic and intragenic gene transfer networks of prokaryotes to estimate and compare their HGT rates at different phylogenetic and ecological levels. (iii) Interaction networks adapted to microbial ecology, by studying the microbiomes of corn and soybeans - crops of major importance in Canada. (iv) Directed biogeographic similarity networks to infer the patterns of historical dispersal of a group of species within a geographic region. An improved understanding of the mechanisms of horizontal gene transfer and recombination, enabled by the described methods and software, should have a direct impact on biologists' ability to study the processes of microbial adaptation to new ecological niches, emergence of mosaic genes through HGT and intragenic recombination, evolution and spread of pathogens, and competition between microbes. One important part of my Discovery grant research program is the training of highly qualified scientific personnel who should have a strategic importance, both for the academic and industrial sectors of Canada.