Computational Modelling and Prediction of Plant Genome Evolution

进化是生物体DNA随时间变化的过程。认识到基因组是如何进化的，对于那些表现出快速进化速度的作物物种尤为重要。基因组测序数量和多样性的快速增长，加上计算基础设施和计算能力的进步，为以全基因组分辨率分析进化动力学打开了大门。我的研究计划的长期目标是通过形式化建模和数学分析来提高各种生物系统中基因组进化的科学知识。特别是，进化模式将不仅从基于基因的传统方面进行审查，而且还将使用基于基因组中的转座元件（一种重复元件）的新方法。转座因子（te）是一种能够在基因组中移动或复制到新位置的DNA序列。在过去，我们的团队已经创建了理论和统计方法来预测te的相对年龄顺序和te相互作用网络从单个基因组。这些方法将扩展到六个相关的芸苔属作物物种的基因组，以帮助阐明在这些物种的分化中编织的te的进化，这将增强我们对te在定义和区分物种间基因组结构方面的影响的理解。我们还将以芸苔属为重点研究多倍体进化并建立模型，并开发相关算法来预测模型下的进化过程。预测将同时考虑最大简约性和可能性。研究了一种针对芸苔属物种全基因组三倍化问题的基因组三倍化算法，以重建这些物种的祖先基因组。解决芸苔属植物如何进化和确定它们共同的进化途径将关系到农艺性状适应的重要性，从而将为这些作物的基因改良提供许多经济效益。由于经济上重要的作物都是多倍体，因此芸苔属植物进化的计算方法、算法和祖先基因组重建可以转移到其他重要作物物种，以指导基因组辅助育种。总体而言，我的研究计划侧重于通过常规模型来理解基因组进化，直接从基因顺序研究进化机制，并考虑作为基因组祖先记录的te的进化。这项工作有可能显著提高我们对物种、亚基因组、基因组结构、保守基因以及最终表型功能之间进化的理解。此外，TE进化的研究为研究基因组进化提供了一个新的机会，这将对传统的分析产生重大影响，并为进化路径建模提供更全面的证据。

Evolution is a process that involves an organism's DNA changing over time. Recognizing how genomes evolve is particularly important for crop species that exhibit rapid rates of evolution. The fast-increasing number and diversity of sequenced genomes, in addition to advances in computational infrastructure and computing power, opens the door to analyzing the dynamics of evolution at a whole-genome resolution. The long-term objective of my research program is to improve the scientific knowledge of genome evolution in various biological systems through formal modelling and mathematical analysis. In particular, evolutionary patterns will be scrutinized not only from the conventional aspect based on genes, but also using novel approaches based on transposable elements (a type of repetitive elements) in the genome. Transposable Elements (TEs) are DNA sequences that have the ability to move or copy to new positions within a genome. In the past, our group has created theoretical and statistical methods to predict relative age order of TEs and a TE-interaction network from a single genome. The methods will be extended to the genomes of six related Brassica crop species to help elucidate the evolution of TEs woven throughout the divergence of these species, which will enhance our understanding of the impact of TEs in defining and differentiating the genome structure between species. We will also study and model polyploid evolution using Brassica species as a specific genus of focus and develop pertinent algorithms to predict evolutionary processes under the model. The prediction will take into account both maximum parsimony and likelihood. A genome tripling algorithm specifically for the whole genome triplication problem as occurred in Brassica species will be investigated to reconstruct the ancestor genome of these species. Resolving how the Brassica species evolved and identifying their shared evolutionary pathways will relate to how important agronomic traits adapt, thus will offer many economic benefits for genetically improving these crops. The computational methods, algorithms, and ancestor genome reconstruction developed for Brassica evolution could be transferred to other important crop species to guide genomic-assisted breeding, as all economically important crops are polyploids. Overall, my research program focuses on understanding genome evolution through both conventional models to study evolutionary mechanisms directly from gene orders and taking into consideration the evolution of TEs that serve as an ancestral record in genomes. This work has the potential to significantly improve our understanding of evolution among species, sub-genomes, genomic constitutions, conserved genes, and ultimately phenotypic functions. Moreover, the study of TE evolution provides a novel opportunity to examine genome evolution, which will significantly impact the conventional analysis and provide more comprehensive evidence to model evolutionary paths.