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.

物种如何相关的问题是长期以来人们感兴趣的问题之一，只有了解了物种关系，才有可能了解物种如何以及为何随着时间的推移而发生变化（即通过根据它们的相关程度进行比较）。尽管进行了广泛的研究，但某些物种之间的关系仍然存在争议，例如，在过去六年中，通过对鸟类的基因组进行测序来了解鸟类之间的关系的大量努力却产生了相互矛盾的结果，尽管进行了测序。许多物种的大量基因组数据，科学家们继续努力理解一些物种关系，在这个项目中，将检查基因组序列数据的子集，以确定哪些类型的数据提供了有关物种关系的准确信息，以及哪些数据混淆了对关系和其他指示进化模式的理解。根据这些知识，将开发软件，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。这项研究强调了当前和未来科学家的需要。能够处理大型且复杂的数据集，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，目的是支持他们未来的研究职业。减少资源有限的学生参与无偿独立研究或可能因缺乏经验而被排除在有偿机会之外的障碍。这项研究的目标是使系统发育学（即对物种关系的理解）不再使用更多数据，使用数据首先，将识别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多争议关系的物种之间的替代假设的支持，并确定正确的关系。机器学习方法将用于确定基因组子集，从而根据其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，以促进其应用。在研究项目中的使用依赖于准确的将通过每半年一次的研讨会为研究人员提供计算培训；还将通过使用真实基因组数据集的课程来培养本科生的计算和研究技能。该项目的研究和教育部分将共同支持更多、更多样化的研究人员，他们拥有技能和软件，可以依靠计算技能来分析大型数据集，有效地进行进化生物学和各个研究领域的研究。有关这项研究的信息将发布在https://schwartzlaburi.github.io/。该项目由生物研究网络基础设施计划和刺激竞争性研究既定计划 (EPSCoR) 联合资助。该奖项反映了 NSF 的法定使命，并被认为值得支持。通过使用基金会的智力优点和更广泛的影响审查标准进行评估。

项目成果

Reference‐free discovery of nuclear SNPs permits accurate, sensitive identification of Carya (hickory) species and hybrids

参考 — 核单核苷酸多态性 (SNP) 的免费发现可准确、灵敏地鉴定山核桃 (山核桃) 物种和杂交种

• DOI: 10.1002/aps3.11455
• 发表时间: 2022-01
• 期刊: Applications in Plant Sciences
• 影响因子: 3.6
• 作者: Literman RA;Ott BM;Wen J;Grauke LJ;Schwartz RS;Handy SM
• 通讯作者: Handy SM

Genome-Scale Profiling Reveals Noncoding Loci Carry Higher Proportions of Concordant Data

基因组规模分析揭示非编码基因座携带更高比例的一致数据

• DOI: 10.1093/molbev/msab026
• 发表时间: 2021-05-19
• 期刊: Molecular Biology and Evolution
• 影响因子: 10.7
• 作者: Literman R;Schwartz R
• 通讯作者: Schwartz R