Statistical Methods for Molecular Evolution

分子进化的统计方法

• 批准号: RGPIN-2014-04447
• 负责人: Susko, Edward
• 金额: $ 1.68万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=611042
• 关键词: Statistical; Methods; Molecular; Evolution

Statistical issues in evolutionary inference using aligned molecular DNA sequence data will be the focus of research. Five methodological areas of importance will be developed. In addition, working with colleagues in biology and biochemistry, methods will be applied to obtain a better understanding of the evolution of early single-celled organisms as well as how processes of selection work in pathogens. 1. Tree Inference: Understanding what the evolutionary relationships are between organisms is an important step in understanding their biology. We will derive new statistical tests of whether there is significant evidence in favour of a particular evolutionary relationship. Long term plans include developing methods that will improve Bayesian statistical inference of evolutionary relationships. 2. Positive Selection: This occurs when organisms adapt to changing environmental conditions and detecting it is of importance, for instance, in understanding how the pathogens that humans evolve resistance. We will develop tests for positive selection that better adjust for additional sources of uncertainty. It is common that selection pressure will vary across organisms and genes. Long term plans include developing models that allow varying selection pressure across gene positions and groups of organisms. 3. Protein Evolution Models: Evolution of the proteins that perform the functions of organisms is a complex process. Understanding such evolutionary processes is crucial to inference about the relationships between organisms and of interest in itself. We will develop sophisticated models that incorporate the three-dimensional structure of proteins. Long term goals include modeling the processes by which organisms gain and lose genes jointly with the evolution of those genes. 4. Distance Methods: Distance methods are a class of methods for evolutionary inference that are commonly used to infer evolutionary relationships when there are large numbers of organisms. Our work on this class of methods will be extended and we will study the statistical properties of this class of methods. 5. Diagnostics: Evolutionary inference can be adversely affected by unusual organisms or evolutionary behaviour at positions in genes. We will develop methods to detect (groups of) organisms and hot spots in genes that are unusual and/or have a large influence on inferences. Knowing what these organisms or hotspots are can be of interest in itself. Analysis after removing them can raise interesting alternative evolutionary scenarios. Application of the methods will help us to better understand the mode and tempo of evolution of early single-celled organisms as well as how positive selection on specific amino acid changes can lead to observable changes in organisms. Methods will be much more broadly applicable, however, and will be of interest to the large number of researchers interested in evolutionary biology. As has been past practice, the software developed will be publicly available.

利用比对的分子DNA序列数据进行进化推断的统计问题将是研究的重点。将制定五个重要的方法领域。此外，与生物学和生物化学的同事合作，将应用方法来更好地了解早期单细胞生物的进化以及选择过程如何在病原体中发挥作用。 1.树推理：理解生物体之间的进化关系是理解生物学的重要一步。我们将得出新的统计检验，以确定是否有支持特定进化关系的重要证据。长期计划包括开发将改进进化关系的贝叶斯统计推断的方法。 2.正选择：当生物体适应不断变化的环境条件时，就会发生这种情况，检测它非常重要，例如，在了解人类如何进化出抵抗力的病原体方面。我们将开发积极选择的测试，更好地调整额外的不确定性来源。选择压力因生物体和基因而异，这是很常见的。长期计划包括开发模型，允许在基因位置和生物群体之间改变选择压力。 3.蛋白质进化模型：执行生物体功能的蛋白质的进化是一个复杂的过程。理解这样的进化过程对于推断生物体之间的关系至关重要，并且本身也很有趣。我们将开发包含蛋白质三维结构的复杂模型。长期目标包括模拟生物体获得和失去基因的过程以及这些基因的进化。 4.距离方法：距离方法是一类用于进化推理的方法，通常用于在存在大量生物体时推断进化关系。我们对这类方法的工作将得到扩展，我们将研究这类方法的统计特性。 5.诊断：进化推理可能会受到不寻常的生物体或基因位置上的进化行为的不利影响。我们将开发方法来检测（组）生物体和基因中的热点是不寻常的和/或有很大的影响推断。了解这些生物体或热点本身就很有趣。移除它们后的分析可以提出有趣的替代进化情景。 这些方法的应用将有助于我们更好地了解早期单细胞生物的进化模式和克里思，以及对特定氨基酸变化的正选择如何导致生物体中可观察到的变化。然而，这些方法将有更广泛的适用性，并将引起大量对进化生物学感兴趣的研究人员的兴趣。按照以往的做法，开发的软件将公开提供。