Statistical Analysis of the DN/dS Ratio in DNA Sequences: Structure, Function and Evolution.

通过在DNA序列水平上识别自然选择，可以解决有关分子进化的问题，并识别DNA序列中具有特殊功能重要性的基因组区域或位点。在这个项目中，将开发新的统计方法来识别和解释选择模式。 将开发一种基于马尔可夫链蒙特卡罗（MCMC）的贝叶斯方法，这将为在DNA序列水平上识别选择提供更强大和通用的工具。 估计突变年龄、估计选择系数分布和检测DNA序列相关进化的方法也将得到发展。新方法将应用于几个数据集，特别是病毒序列如HIV-1序列的数据集。 将解决的问题之一是在HIV-1病毒的耐药性演变中发生补偿突变的频率。 新方法将适用于许多基因组研究，特别是用于确定功能重要性的区域或位点。

By identifying natural selection at the level of DNA sequences, it possible to address questions regarding molecular evolution and to identify genomic regions or sites in the DNA sequence of special functional importance. In this project new statistical methods for identifying and interpreting patterns of selection will be developed. A Bayesian method based on Markov chain Monte Carlo (MCMC) will be developed that will provide a more powerful and versatile tool for identifying selection at the level of the DNA sequence. Methods for estimating the age of a mutation, for estimating the distribution of selection coefficients and for detecting correlated evolution in DNA sequences will also be developedThe new methods will be applied to several data sets, particularly data sets of viral sequences such as HIV-1 sequences. One of the questions that will be addressed is how often compensatory mutations occur in the evolution of drug resistance in the HIV-1 virus. The new methods will be applicable in many genomic studies, particularly for identifying regions or sites of functional importance.