CAREER: Facilitating the use of genomic data in evolutionary biology

职业：促进基因组数据在进化生物学中的使用

• 批准号: 1942273
• 负责人: Rachel Schwartz
• 金额: $ 124.04万
• 依托单位: University of Rhode Island
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942273&HistoricalAwards=false
• 关键词: CAREER; Facilitating; use; genomic; data

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.

物种是如何相互联系的问题是一个长期存在的问题。只有当物种关系被理解时，才有可能理解物种是如何以及为什么随着时间的推移而变化的（即通过根据它们的亲缘关系进行比较）。令人惊讶的是，尽管进行了广泛的研究，一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，通过测序鸟类的基因组来了解鸟类之间的关系的大量努力产生了相互矛盾的结果。这个研究项目试图理解为什么，尽管对许多物种进行了大量的基因组数据测序，科学家们仍然在努力理解一些物种之间的关系。在这个项目中，基因组序列数据的子集将被检查，以确定哪些类型的数据提供了关于物种关系的准确信息，哪些数据混淆了对物种关系的理解，并表明了其他的进化模式。利用这些知识，软件将被开发出来，使生物学家能够快速处理基因组数据，从而快速准确地理解他们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当前和未来的科学家需要能够处理大型和复杂的数据集，该项目将(1)为数百名生物学家提供计算培训，(2)作为包容性社区的一部分，支持不同本科生的计算和研究技能的发展，以支持他们未来的研究事业。面向本科生的研究性课程将减少学生面临的障碍，这些学生在从事无薪独立研究方面资源有限，或可能因缺乏经验而被排除在有薪机会之外。本研究的目标是将系统发育（即对物种关系的理解）从使用更多的数据转移到使用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子集。这些子集将用于评估对具有更有争议关系的物种之间替代假设的支持，并确定正确的关系。其次，机器学习方法将用于确定基因组子集，从而根据它们的特征产生更准确的系统发育。最后，这些数据过滤的方法将在免费的、易于使用的开源软件中自动化，以促进它们在依赖于物种关系准确估计的研究项目中的使用。将通过半年一次的研究人员讲习班提供计算培训；还将为在世界各地领导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。该项目的研究和教育部分将共同支持更多和多样化的研究人员，他们拥有技能和软件，可以有效地进行进化生物学和依赖计算技能分析大型数据集的各种研究领域的研究。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刺激竞争性研究的既定计划（EPSCoR）联合资助。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。