Statistical tools for learning about trait evolution across species

进化生物学的研究一直集中在理解物种间的性状变异及其对地球上观察到的非凡生物多样性的贡献。然而，这种冒险是复杂的计算工具，可以有效地导航物种之间的进化关系或纳入大型和多样化的性状数据集产生的现代实验研究的不足。因此，该项目的目标是通过设计一套新颖的统计和机器学习方法来扩展现有的工具包，用于研究跨物种的性状进化。这些工具将通过计算机模拟进行彻底测试，与其他最先进的方法进行比较，并应用于公开的数据集，以解决特定的进化问题。该项目生成的所有工具和数据集将免费广泛传播给科学界，为研究人员提供一个强大的框架，以回答有关生命树性状进化的各种令人兴奋的问题。该项目还将通过招募西班牙裔女高中生和本科生加入研究小组，促进代表性不足的群体参与科学和工程。此外，项目负责人将为当地社区的退休老年人和全国各地的美洲原住民社区设计和教授进化基因组学和生物信息学的实践课程。 阐明物种间性状变异的潜在过程是进化生物学中的一个基本问题，现有工具远远落后于现代数据集。目前的项目将通过设计统计和监督的机器学习方法来解决这个问题，以稳健和准确地预测跨物种性状进化的一般和特定进化机制。特别是，开发的工具将适当考虑物种系统发育关系，整合不同的组学和其他性状数据，并为测试专门的进化假说创造新的途径。这些方法的可用性将促进性状相关的遗传学，过程和驱动这些性状的进化，和适应性性状的进化所产生的不同类型的结构变异之间的关联的研究。此外，将这些工具应用于经验数据集的结果将阐明不同层次的生物组织和不同生物系统之间的联系。最后，开发的工具将适用于广泛的不断增加的基因组学，转录组学和其他现代组学和性状数据，促进未来的研究过程中塑造跨物种的性状分布及其在进化创新中的作用。该项目的结果将在www.example.com上公布http://assisgroup.fau.edu/.This奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

Research on evolutionary biology has been centered on understanding trait variation across species and its contribution to the extraordinary biodiversity observed on Earth. However, this venture is complicated by a deficiency in computational tools that can effectively navigate evolutionary relationships among species or incorporate large and diverse trait datasets generated by modern experimental studies. Hence, the objective of this project is to expand existing toolkit through the design of a suite of novel statistical and machine learning methods for studying trait evolution across species. These tools will be thoroughly tested through computer simulations, compared to alternative state-of-the-art approaches, and applied to publicly available datasets to address specific evolutionary problems. All tools and datasets generated by the project will be freely and widely disseminated to the scientific community, providing researchers with a powerful framework in which to answer a variety of exciting questions about trait evolution across the tree of life. The project will also advance the participation of underrepresented groups in science and engineering through recruitment of female Hispanic high school and undergraduate students to the research team. Additionally, the project leaders will design and teach hands-on courses in evolutionary genomics and bioinformatics for retired senior citizens in the local community and for Native American communities across the country. Elucidating the processes underlying trait variation across species is a fundamental problem in evolutionary biology, and one for which existing tools lag far behind modern datasets. The current project will address this issue through the design of statistical and supervised machine learning approaches for robustly and accurately predicting the general and specific evolutionary mechanisms by which traits evolve across species. In particular, the tools developed will properly account for species phylogenetic relationships, integrate diverse omics and other trait data, and create new avenues for testing specialized evolutionary hypotheses. Availability of these methods will facilitate studies of associations between traits related by a phylogeny, processes and forces driving the evolution of such traits, and adaptive trait evolution arising from different types of structural variations. Moreover, findings from applications of these tools to empirical datasets will illuminate connections across different levels of biological organization and diverse biological systems. Finally, the developed tools will be applicable to a wide range of ever-increasing genomic, transcriptomic, and other modern omics and trait data, promoting future research in the processes shaping the distribution of traits across species and their roles in evolutionary innovation. Results from the project will be available at http://assisgroup.fau.edu/.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.