Statistical Methods for Molecular Evolution

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学领域。此外，将与生物学和生物化学领域的同事合作，应用各种方法更好地了解早期单细胞生物体的进化，以及选择过程如何在病原体中发挥作用。1.树推理：了解生物之间的进化关系是了解其生物学的重要一步。我们将得出新的统计检验，以确定是否有重大证据支持特定的进化关系。长期计划包括开发改进进化关系的贝叶斯统计推断的方法。2.积极选择：当生物体适应不断变化的环境条件时，就会发生这种情况，检测这种选择对了解人类进化出的病原体如何产生抗性具有重要意义。我们将开发正面选择测试，以便更好地调整其他不确定因素。选择压力会因生物和基因的不同而不同，这是很常见的。长期计划包括开发允许不同基因位置和生物体群体的不同选择压力的模型。3.蛋白质进化模型：发挥生物体功能的蛋白质进化是一个复杂的过程。了解这样的进化过程对于推断生物体之间的关系和自身的利益至关重要。我们将开发包含蛋白质三维结构的复杂模型。长期目标包括对生物体获得和丢失基因的过程以及这些基因的进化进行建模。4.距离方法：距离方法是进化推理的一类方法，通常用于在生物数量较多的情况下推断进化关系。我们在这类方法上的工作将得到扩展，我们将研究这类方法的统计性质。5.诊断学：进化推断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(一组)生物和基因中不寻常的和/或对推断有很大影响的热点。了解这些有机体或热点是什么本身可能是有意义的。移除它们后的分析可以提出有趣的替代进化场景。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体的进化模式和节奏，以及对特定氨基酸变化的正选择如何导致生物体的可观察变化。然而，这些方法将得到更广泛的应用，并将引起对进化生物学感兴趣的大量研究人员的兴趣。按照过去的做法，开发的软件将向公众开放。

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.