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。对这些序列的分析通常使用基于系统发育树和系统发育网络的传统方法，这些方法可以考虑相对较少的分类群，甚至使用计算机集群。因此，数以千计的环境标本和已测序的微生物基因组仍未被探索。与此同时，一种基于序列相似性网络的有前景的替代方法仍在很大程度上未得到充分利用。在这个探索基金研究计划的框架内，我将开发新的方法和开源软件，利用序列相似性网络分析大型进化和生态数据集。我将首先设计一种非常快速的距离方法来推断和验证水平基因转移(HGT)事件，该方法将适用于包含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，在元基因组水平上分析基因水平转移的机制。序列相似性网络还将结合图论方法来识别马赛克基因(或嵌合体)，这些基因片段是基因融合过程(即基因转移之后是基因内重组的过程)的结果，由不同来源的DNA片段形成。建议的生物信息学方法将结合用户友好的软件通过模拟进行严格测试，然后应用于推断和分析：(I)为导致新冠肺炎大流行的SARS-CoV-2病原体的所有基因建立的个体相似网络，有助于追溯进化过程，揭示这种危险病毒的起源。(2)原核生物的基因间和基因内基因转移网络，以在不同的系统发育和生态水平上估计和比较它们的HGT率。(Iii)适应微生物生态的相互作用网络，通过研究玉米和大豆这两种在加拿大很重要的作物的微生物群。(4)建立生物地理相似性网络，以推断某一地理区域内一组物种的历史扩散模式。通过所述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的生态位、通过HGT和基因内重组出现马赛克基因、病原体的进化和传播以及微生物之间的竞争的过程产生直接影响。我的发现拨款研究计划的一个重要部分是培养高素质的科学人员，他们应该具有战略重要性，无论是对加拿大的学术界还是工业部门。

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.