Statistical Methods for Molecular Evolution

从比对的分子序列数据中推断进化的统计方法将是这项研究的重点。此外，将与生物学和生物化学领域的同事合作，应用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。虽然统计理论和方法的发展将考虑到进化推理问题，但统计方法和理论将更广泛地适用于其他科学领域。将针对四个项目领域：*1.树测试：了解有机体之间的进化关系是了解其生物学的重要一步。我们将推导出新的统计方法，产生可能包括真实关系的进化树的置信度集，并提供关于进化关系的不确定性所在的信息。正向选择：当生物体适应不断变化的环境条件时，就会发生正向选择。例如，检测阳性选择对于了解感染人类的病原体如何进化抗药性具有重要意义。目前的方法有时会给出生物学上不合理的选择强度估计，也可能给出不可靠的测试结果。我们正在开发不太容易受到稀疏数据问题影响的方法，而稀疏数据问题会导致此类问题。另一个令人感兴趣的领域是开发联合模型，对生物体基因执行的功能的变化和DNA的变化进行建模。这样的模型将更好地理解基因中的哪些位置对于特定的生物功能是重要的。*3.蛋白质进化模型：执行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。我们将开发更现实的模型，以适应经常观察到的进化过程随着蛋白质和蛋白质中的位置而变化的现象。*4.模型选择：模型选择：模拟进化的一个重要任务是选择能够适应重要过程的模型类别，但不会变得太复杂，以至于数据不足以估计它们。我们将开发惩罚过度复杂性的方法，并通过考虑验证数据的关键性能度量来测试模型性能。

Statistical methods for inference about evolution from aligned molecular sequence data will be the focus of this research. In addition, working with colleagues in biology and biochemistry, methods will be applied to better understand the evolution of early single-celled organisms and how processes of selection work in pathogens. While statistical theory and methods will be developed with evolutionary inference questions in mind, the statistical methods and theory will be more broadly applicable to other areas of science. Four project areas will be targeted:******1. Tree Testing: Understanding what the evolutionary relationships are between organisms is an important step in understanding their biology. We will derive new statistical methods that yield confidence sets of evolutionary trees that are likely to include the true relationship and provide information about where uncertainty about evolutionary relationships lies. ******2. Positive Selection: This occurs when organisms adapt to changing environmental conditions. Detecting positive selection is of importance, for instance, in understanding how pathogens that infect humans can evolve resistance. Current methodology sometimes gives biologically unreasonable estimates of the strength of selection and can give unreliable testing results. We are developing methods that are less susceptible to the sparse-data issues that cause such problems. Another area of interest is developing models that jointly models changes in the functions that an organism's genes performs and changes in DNA. Such models will give a better understanding of which locations in a gene are important for particular biological functions. ******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 more realistic models that accommodate the frequently observed phenomenon that evolutionary processes vary over proteins and positions within proteins.******4. Model Selection: An important task in modeling evolution is to select classes of models that adjust for important processes but without becoming so complex that data is insufficient for their estimation. We will develop methods that penalize excess complexity and test model performance by considering key performance measures on validation data.