Novel Metrics for the Comparison of Phylogenetic Networks

用于比较系统发育网络的新指标

• 批准号: 577123-2022
• 负责人: Lafond, ManuelM
• 金额: $ 3.28万
• 依托单位: Université de Sherbrooke
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=757274
• 关键词: Novel; Metrics; Comparison; Phylogenetic; Networks

For the last two centuries, biologists have modeled species evolution with phylogenetic trees, which assume that genetic material is exclusively transmitted through direct descent. However, there is an abundance of organisms whose evolution cannot be represented as a tree, including plants, bacteria, and viruses. For such species, a network is needed to represent non-treelike events such as hybridization and gene transfer. Several methods build a network from genomic data, but developers lack the tools to evaluate their algorithms against reference or simulated networks.In this proposal, we will develop novel approaches for the comparison of phylogenetic networks. For tree comparison, the Robinson-Foulds (RF) distance is the de facto standard, but in phylogenetic networks, there appears to be no metric that is as strongly established. This is mainly because unlike the RF distance, all current network measures impose a trade-off between desirable properties such as biological relevance, low computational requirements, and interpretability.We introduce new operational distances between networks with these advantages in mind. First, we will build the theoretical foundations of our novel metrics by establishing their mathematical and algorithmic properties. Second, the developed algorithms will be implemented in an open-source software package. Third, we will validate and compare our metrics on real and simulated biological datasets. Our multidisciplinary team is composed of experts in mathematical modeling, high-quality software development, and biology. Our research will lead to clearer comparison metric options for evolutionary biologists and provide a more accurate picture of evolution. The proposal is also an excellent opportunity to establish long-term collaborations between early career professors from different provinces. Furthermore, Québec and Canada are known for their leadership in comparative genomics, and the research in this proposal will reinforce it even further.

在过去的两个世纪中，生物学家用系统发育树对物种的进化进行了建模，这些树木假设遗传物质仅通过直接下降传播。但是，有很多生物的进化不能表示为树，包括植物，细菌和病毒。对于此类物种，需要网络来表示诸如杂交和基因转移之类的非真实事件。几种方法从基因组数据中构建了一个网络，但是开发人员缺乏评估其针对参考或模拟网络的算法的工具。在本提案中，我们将开发新的方法来比较系统发育网络。为了进行树木的比较，罗宾逊 - 范特（RF）的距离是事实上的标准，但是在系统发育网络中，似乎没有明确确定的度量。这主要是因为与RF距离不同，所有当前网络措施都在理想的属性（例如生物学相关性，低计算要求和可解释性）之间进行了权衡。我们引入了具有这些优势的网络之间的新操作距离。首先，我们将通过建立其数学和算法属性来建立新型指标的理论基础。其次，开发的算法将在开源软件包中实现。第三，我们将验证和比较我们在真实和模拟生物数据集上的指标。我们的多学科团队由数学建模，高质量软件开发和生物学专家组成。我们的研究将为进化生物学家提供更清晰的比较度量选择，并提供更准确的进化情况。该提案也是一个绝佳的机会，可以在不同提供者的早期职业教授之间建立长期合作。此外，魁北克和加拿大以其在比较基因组学方面的领导而闻名，该提案中的研究将进一步加强。