NSF Postdoctoral Fellowship in Biology FY 2021: Comparative methods and model clades for evolutionary developmental biology

这一行动为NSF 2021财年生物学博士后研究奖学金提供了资金，综合研究调查了支配基因组、环境和表型之间相互作用的生命规则。该奖学金支持研究员的研究和培训，这些研究员将以创新的方式为生活规则领域做出贡献。这项受资助的研究将使用大型数据集来研究进化如何导致生物多样性。收集数以千计的测量生物特征的大量数据的方法正变得越来越普遍，而且生成成本也越来越低。这类数据的例子包括基因表达的发育分析。随着这些数据变得越来越普遍，这些数据在不同物种之间的比较也越来越普遍。然而，进化生物学家多年来一直警告说，必须使用解释物种亲缘关系的数学模型来进行物种间的比较，否则就有可能错误识别不同物种的模式。该奖学金将专注于开发跨物种数据比较的新方法。这项研究很重要，因为在这个全球快速变化的时代，需要强大的进化模型来准确预测生物多样性未来可能发生的变化。此外，这项研究将通过从计算生物学分析中代表性不足的群体中招募和指导本科生来扩大对科学的参与。这一研究项目将产生一个新的比较框架，用于分析跨物种的基因表达的高维数据。基因表达数据在发育生物学的分析中很常见，但目前对这些数据的跨分类群比较往往没有考虑到观察到的进化的非独立性。有大量的比较方法被设计用来对不同类群的性状进行统计比较，但它们对高维连续性状的实施面临技术限制。为了克服这些限制，首先，研究员将识别和扩展进化模型，以描述表达数据在分类群中的分布。这种分析将要求研究员实现具体的培训目标，包括寻找数学建模方面的高级知识，以克服在分析具有数千个参数的数据集方面的常见挑战。其次，这位研究员将把这些方法应用于跨组织和物种的基因表达的真实数据，测试关于系统发育、基因途径和基因产品类型解释变异的程度的假设。这项研究的结果将是一个比较框架，可以立即应用于跨物种的基因表达数据集，该数据集将作为免费、公开可访问的开源软件发布。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。

This action funds an NSF Postdoctoral Research Fellowship in Biology for FY 2021, Integrative Research Investigating the Rules of Life Governing Interactions Between Genomes, Environment and Phenotypes. The fellowship supports research and training of the fellow that will contribute to the area of Rules of Life in innovative ways. The funded research will use large datasets to investigate how evolution leads to biodiversity. Methods for collecting large amounts of data with thousands of measured biological traits are becoming increasingly common and inexpensive to generate. Examples of such data include developmental assays of gene expression. As this data becomes more common, so do comparisons of these data across species. However, evolutionary biologists have warned for years that comparisons across species must be performed using mathematical models that account for how related species are, or they risk misidentifying patterns across species. This fellowship will focus on developing new methods for comparing data across species. This research is important given that, in this era of rapid global change, robust models of evolution are needed to accurately predict how biodiversity is likely to change in the future. Furthermore, this research will broaden participation in science by recruiting and mentoring undergraduate students from underrepresented groups in computational biological analysis. This research project will result in a new comparative framework for analyzing high-dimensional data on gene expression across species. Gene expression data are common in assays of developmental biology, yet current comparisons of these data across taxa often fail to consider the evolutionary non-independence of observations. There exists a rich literature of comparative methods designed to statistically compare traits across taxa, but their implementation for high-dimensional continuous traits faces technical limitations. To overcome these limitations, first the fellow will identify and extend evolutionary models to describe the distribution of expression data across taxa. This analysis will require the fellow to achieve specific training objectives, including seeking out advanced knowledge in mathematical modeling, to overcome common challenges in analyzing datasets that have many thousands of parameters. Second, the fellow will apply these methods to real-world data on gene expression across tissues and species, testing hypotheses about the degree to which phylogeny, gene pathway, and gene product type explain variation. The results of this study will be a comparative framework that can be immediately applied to datasets of gene expression across species, which will be released as free, publicly accessible, open-source software.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.