Collaborative Research: Bayesian Model Checking for Phylogenetics in the Post-Genomic Era

合作研究：后基因组时代系统发育的贝叶斯模型检查

• 批准号: 1355071
• 负责人: Jeremy Brown
• 金额: $ 41.83万
• 依托单位: Louisiana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1355071&HistoricalAwards=false
• 关键词: Collaborative; Research; Bayesian; Model; Checking

Diagrams of evolutionary relationships (phylogenetic trees) for species and genes are widely employed in biological research, including the fields of medicine, epidemiology, forensics, conservation, evolutionary biology and agriculture. This research project will explore new ideas and develop new software tools to improve the accuracy by which phylogenetic relationships are determined; in this way the research will contribute to improved understanding and decision-making for a broad range of scientific disciplines and practical applications. Results from this research will be broadly disseminated, including in-person and online training opportunities to familiarize researchers in the relevant disciplines with these newly developed computer-based analytical tools. Further, the research activities will involve the participation and training of a postdoctoral scholar, a graduate student, and several undergraduates at Louisiana State University (LSU) and the University of Hawaii at Manoa. This project will be incorporated into a seminar series at LSU focused on increasing awareness of computational biology among undergraduate students.Phylogenetic trees are now routinely inferred from enormous genome-scale data sets, revealing extensive variation in apparent phylogenetic signal across loci. However, no general tools currently exist to objectively and quantitatively assess how much of this variation is due to biological processes and how much is caused by methodological error. Distinguishing between true variation and error is the problem to be studied in this project, as resolving this issue is essential for robustly resolving the Tree of Life and for understanding genomic evolution. The goal of this work is to give researchers the tools to identify and avoid situations where phylogenetic inferences are unreliable. These tools will be implemented in open-source software (RevBayes and R), and will be easily extensible to many types of phylogenetic inference beyond those in this project. This research will implement suites of existing, alternative statistical approaches employing Bayesian posterior prediction to rigorously assess absolute fit of phylogenetic models to evolutionary data, and how this fit impacts the reliability of inference. Simulations comparing performance of alternative models will focus on three types of inferences: (i) estimation of individual gene trees, (ii) estimation of species trees from many genes, and (iii) comparative analysis of continuous traits. These approaches will be applied to exemplar empirical questions, including the placement of turtles among amniotes using several recently published genome-scale data sets. These data contain surprising and massive heterogeneity in phylogenetic signal regarding the placement of turtles, and thus form an excellent case study.

物种和基因的进化关系图（系统发育树）被广泛用于生物学研究中，包括医学，流行病学，取证，保护，进化生物学和农业领域。该研究项目将探索新的想法并开发新的软件工具，以提高确定系统发育关系的准确性；通过这种方式，研究将有助于改善广泛的科学学科和实际应用的理解和决策。这项研究的结果将广泛传播，包括面对面和在线培训机会，以使这些新开发的基于计算机的分析工具在相关学科中熟悉研究人员。此外，研究活动将涉及路易斯安那州立大学（LSU）和Manoa大学的博士后学者，研究生和几名本科生的参与和培训。该项目将纳入LSU的研讨会系列中，重点是提高本科生中对计算生物学的认识。现在，从巨大的基因组尺度数据集中通常可以推断出晶状体发育树，从而揭示了整个基因座的明显的系统发育信号的广泛差异。但是，目前尚无通用工具可以客观和定量评估这种变异的多少是由于生物过程以及方法上的错误引起的。区分真正的变化和误差是在这个项目中要研究的问题，因为解决此问题对于坚强解决生命之树和理解基因组进化至关重要。 这项工作的目的是为研究人员提供识别和避免系统发育推断不可靠的情况。这些工具将在开源软件（Revbayes和R）中实施，并且对于超出该项目的系统发育推断，很容易扩展。 这项研究将实施采用贝叶斯后部预测的现有替代统计方法的套件，以严格评估系统发育模型对进化数据的绝对拟合，以及这如何影响推理的可靠性。比较替代模型性能的模拟将重点介绍三种推论：（i）单个基因树的估计，（ii）从许多基因中估算物种树的估计，以及（iii）连续特征的比较分析。这些方法将应用于示例性的经验问题，包括使用几种最近发表的基因组尺度数据集将乌龟放置在羊膜中。这些数据包含有关乌龟放置的系统发育信号中令人惊讶和巨大的异质性，因此形成了一个很好的案例研究。