CAREER: Facilitating the use of genomic data in evolutionary biology

物种如何相关的问题是一个长期关注的问题。只有当物种关系被理解时，才有可能理解物种如何以及为什么随着时间的推移而变化(即根据它们的亲缘关系有多密切来比较它们)。令人惊讶的是，尽管进行了广泛的研究，但某些物种群体之间的关系仍然存在争议。例如，在过去的六年里，通过对鸟类的基因组测序来理解鸟类是如何相关的大量努力产生了相互矛盾的结果。这项研究项目试图理解为什么尽管对许多物种的大量基因组数据进行了测序，但科学家们仍然难以理解一些物种之间的关系。在这个项目中，将检查基因组序列数据的子集，以确定哪些数据类型提供了关于物种关系的准确信息，哪些数据混淆了对关系的理解并指示了其他进化模式。利用这一知识，将开发出软件，使生物学家能够快速处理基因组数据，以快速准确地了解他们所研究的物种之间的关系，从而了解这些群体的进化方式。由于这项研究强调了现在和未来的科学家能够处理大型和复杂的数据集的需要，该项目将(1)为数百名生物学家提供计算培训，(2)支持不同本科生作为包容性社区的一部分发展计算和研究技能，目的是支持他们未来的研究事业。为本科生开设的研究型课程将减少学生面临的障碍，这些学生资源有限，无法从事无偿的独立研究，或者可能因为缺乏经验而被排除在有偿机会之外。这项研究的目标是将系统发育学(即对物种关系的理解)从使用更多的数据，转向使用具有关于物种关系的最佳信息的数据。首先，将确定支持已建立的物种关系的基因组的子集。这些子集将被用来评估具有更多争议关系的物种对替代假说的支持，并识别正确的关系。其次，机器学习方法将被用来确定基因组子集，从而根据它们的特征产生更准确的系统发育。最后，这些数据过滤方法将在免费、易于使用的开源软件中实现自动化，以促进它们在依赖于对物种关系的准确估计的研究项目中的使用。将通过每半年为研究人员举办一次讲习班提供计算培训；还将为领导世界各地类似讲习班的其他人提供培训。本科生的计算和研究技能将通过使用真实基因组数据集进行新研究的课程来发展。总而言之，该项目的研究和教育部分将支持更多和更多样化的研究人员，他们拥有有效开展进化生物学研究的技能和软件，以及依靠计算技能分析大数据集的各个研究领域。这项研究的定期更新将在https://schwartzlaburi.github.io/.上提供该项目由生物研究网络基础设施计划和已建立的激励竞争性研究计划(EPSCoR)共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The question of how species are related is one of long-standing interest. Only when species relationships are understood is it possible to understand how and why species have changed over time (i.e. by comparing them based on how closely they are related). Surprisingly, despite extensive study, the relationships among some groups of species remain controversial. For example, in the last six years, large efforts to understand how birds are related by sequencing their genomes have produced conflicting results. This research project seeks to understand why, despite sequencing large amounts of genomic data for many species, scientists continue to struggle to understand some species relationships. In this project, subsets of genome sequence data will be examined to determine which types of data provide accurate information about species relationships, and which data confound the understanding of relationships and indicate other evolutionary patterns. Using this knowledge, software will be developed that allows biologists to rapidly process genomic data to quickly and accurately understand relationships among species they work on, and therefore the ways in which these groups evolved. As this research highlights the need for current and future scientists to be able to work with large and complex datasets, the project will (1) provide computational training for hundreds of biologists, and (2) support the development of computational and research skills for diverse undergraduates as part of an inclusive community, with the objective of supporting their future research careers. Research-based courses for undergraduates will reduce the hurdles faced by students who have limited resources to engage in unpaid independent research, or may be excluded from paid opportunities due to a lack of experience.The goal of this research is to move phylogenetics (i.e. the understanding of species relationships) from using more data, to using data with optimal information about species relationships. First, subsets of the genome that support well-established species relationships will be identified. These subsets will be used to evaluate support for alternative hypotheses among species with more controversial relationships, and identify the correct relationships. Second, machine learning methods will be used to determine genomic subsets that result in more accurate phylogenies based on their characteristics. Finally, these approaches to data filtering will be automated in freely-available, easy-to-use, open-source software, to facilitate their use in research projects relying on accurate estimates of species relationships. Computational training will be provided through semi-annual workshops for researchers; training for others to lead similar workshops around the world will also be provided. Undergraduates’ computational and research skills will be developed through courses using real genomic datasets for novel research. Together, the research and education components of this project will support a greater number and diversity of researchers who have the skills and software to effectively conduct research in evolutionary biology and various areas of research relying on computational skills to analyze large datasets. Regular updates on this research will be made available at https://schwartzlaburi.github.io/. This project is jointly funded by the Cyber-infrastructure for Biological Research program and the Established Program to Stimulate Competitive Research (EPSCoR).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.