Novel Metrics for the Comparison of Phylogenetic Networks

在过去的两个世纪里，生物学家一直用系统发育树来模拟物种进化，这种树假定遗传物质只通过直接血统传播。然而，有许多生物的进化不能用树来表示，包括植物、细菌和病毒。对于这类物种，需要一个网络来代表非树状事件，如杂交和基因转移。一些方法利用基因组数据构建网络，但开发人员缺乏工具来对照参考或模拟网络来评估他们的算法。在这项建议中，我们将开发新的方法来比较系统发育网络。对于树的比较，Robinson-Foulds(RF)距离是事实上的标准，但在系统发育网络中，似乎没有这样强有力的度量标准。这主要是因为与射频距离不同，当前的所有网络度量都在生物相关性、较低的计算要求和可解释性之间进行权衡，我们引入了新的网络之间的操作距离，考虑到这些优势。首先，我们将通过建立它们的数学和算法属性来建立我们的新度量的理论基础。其次，开发的算法将在开放源码软件包中实现。第三，我们将在真实和模拟的生物数据集上验证和比较我们的度量标准。我们的多学科团队由数学建模、高质量软件开发和生物学方面的专家组成。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图景。这项提议也是来自不同省份的早期职业教授之间建立长期合作的绝佳机会。此外，魁北克和加拿大以其在比较基因组学方面的领先地位而闻名，这项提案中的研究将进一步加强这一点。

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.