Novel Metrics for the Comparison of Phylogenetic Networks

在过去的两个世纪里，生物学家用系统发育树来模拟物种进化，假设遗传物质只通过直系后代传播。然而，有大量的生物体的进化不能用树来表示，包括植物，细菌和病毒。对于这样的物种，需要一个网络来代表非树状事件，如杂交和基因转移。 有几种方法从基因组数据构建网络，但开发人员缺乏工具来评估他们的算法对参考或模拟networks.In这个提议，我们将开发新的方法比较系统发育网络。 对于树比较，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。这主要是因为与RF距离不同，所有当前的网络措施都在生物相关性，低计算要求和可解释性等理想属性之间进行权衡。首先，我们将通过建立新指标的数学和算法属性来建立新指标的理论基础。其次，开发的算法将在一个开源软件包中实现。第三，我们将在真实的和模拟的生物数据集上验证和比较我们的指标。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指标选择，并提供更准确的进化图景。该提案也是一个很好的机会，建立来自不同省份的早期职业教授之间的长期合作。 此外，魁北克省和加拿大以其在比较基因组学方面的领导地位而闻名，本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.