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

来自微生物基因组和环境的生物学家和生物信息学家感兴趣的分子序列的数量继续呈指数级增长。这些序列的分析通常使用基于系统发生树和系统发生网络的传统方法进行，即使使用计算机集群，也可以考虑相对少量的分类群。因此，数以千计的环境标本和测序的微生物基因组仍然未被探索。与此同时，基于使用序列相似性网络的有前途的替代方法仍然在很大程度上未被开发。在这个发现资助研究计划的框架内，我将开发新的方法和开源软件，用于使用序列相似性网络分析大型进化和生态数据集。我将首先设计一个非常快速的距离方法来推断和验证水平基因转移（HGT）事件，这将适用于包括数千个物种和数百万个序列的数据集。该方法将利用序列相似性网络和系统发育树的概念，将二者结合起来，在宏基因组的尺度上分析基因水平转移的机制。序列相似性网络也将与图论方法一起沿着用于鉴定嵌合基因（或嵌合体），嵌合基因由基因融合过程（即基因转移后进行基因内重组的过程）导致的各种来源的DNA片段形成。拟议的生物信息学方法，以及用户友好的软件，将通过模拟进行严格测试，然后应用于推断和分析：（i）为导致COVID-19大流行的SARS-CoV-2病原体的所有基因构建的个体相似性网络，帮助追溯进化并揭示这种危险病毒的起源。(ii)原核生物基因间和基因内的基因转移网络，以估计和比较其HGT率在不同的系统发育和生态水平。(iii)相互作用网络适应微生物生态学，通过研究玉米和大豆的微生物组-在加拿大的主要重要作物。(iv)定向地理相似性网络来推断一个地理区域内一组物种的历史扩散模式。通过所描述的方法和软件，对水平基因转移和重组机制的更好理解，应该对生物学家研究微生物适应新生态位的过程、通过HGT和基因内重组出现嵌合基因、病原体的进化和传播以及微生物之间的竞争的能力产生直接影响。我的发现资助研究计划的一个重要组成部分是培养高素质的科学人才，他们应该对加拿大的学术和工业部门具有战略重要性。

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.