Statistical and Computational Problems in Genetics and Molecular Biology

这 提案 地址 各种 的 统计 以及遗传学和分子生物学中出现的计算问题。该研究项目的一个重要目的是培训和 在这一跨学科领域培养博士后研究员。这里描述的问题是我们的一部分， 与遗传学家和 来自南加州大学和其他地方的分子生物学家。所有这些都有重要的计算和统计组成部分。 我们正在研究统计和算法方面的问题， 物理映射其中，光学 映射，通过该映射产生有序的限制性映射 使用荧光显微镜。长期目标是 开发一个在线程序，产生这样一个 有序映射 我们正在分析各种物理映射的效率 战略 基于 序列标记位点的变体 阴谋特别是，我们正在研究非同质克隆 和锚分布，虚假的，模糊的或 缺失的锚点，以及允许混合 克隆行走和随机行走。 我们正在开发统计模型， 基因组 重排和方法 为 重建 系统发育树从这些数据。这部分是出于 通过GFAP启动子中发生重排的数据， 在大脑中。 一个相关的项目涉及系统发育的其他方面， 树的重建，特别是依赖的影响 当DNA序列数据被用来构建 树 受rRNA启动子数据的启发，我们还 开发算法以拟合更详细的进化 当底层树是已知的时， 我们正在调查模式计数的统计数据， 分子 序列的 这是一个RE 频繁 使用 到 表征生物特征，例如编码区， 调控序列，剪接位点等等。我们的重点是 关于发展有用的计算算法， 当底层序列结构必须 从数据中估计。 另一个项目涉及估计删除率 在人类线粒体基因组中。PCR检测用于 检测细胞系是否含有线粒体 删除。这些数据包括 在几种细胞系上的测定。我们正在开发类似于 的 Luria-Delbruck波动分析法 估计缺失率。将结果用于 探讨缺失在Kearns-Sayre综合征中的作用 患者 我们 正在开发蒙特卡罗似然方法， 遗传学中的推理，主要目的是研究 人类线粒体和核基因变异， 用于人类进化的研究。一个重点是 估计重组率，特别是 人类疾病基因座和多种标记基因座。

This proposal addresses a variety of statistical and computational problems that arise in genetics and molecular biology. An important aim of the research project is the training and development of post-doctoral research fellows in this interdisciplinary area. The problems described here are part of our ongoing collaborations with geneticists and molecular biologists from USC and elsewhere. All of them have significant computational and statistical components. We are studying statistical and algorithmic aspects of physical mapping. Among these are optical mapping, by which ordered restriction maps are produced using fluorescence microscopy. The long term aim is the development of an online program that produces such an ordered map. We are analyzing the efficiency of various physical mapping strategies based on variants of sequence-tagged-sites schemes. In particular, we are studying inhomogeneous clone and anchor distributions, the effects of false, ambiguous or missing anchors, and strategies that allow for mixtures of clone walking and random steps. We are developing statistical models for the evolution of genome rearrangements and methods for reconstructing phylogenetic trees from such data. This is motivated in part by data on rearrangements occurring in the promoter of GFAP in the brain. A related project addresses other aspects of phylogenetic tree reconstruction, in particular the effects of dependence among sites when DNA sequence data is used to build the tree. Motivated by data on rRNA promoters, we are also developing algorithms for fitting more detailed evolutionary models when the underlying tree is known. We are investigating the statistics of pattern counts in molecular sequences. These a re frequently used to characterize biological features such as coding regions, regulatory sequences, splice sites and so on. Our focus is on the development of useful computational algorithms for pattern counts when the underlying sequence structure must be estimated from the data. A further project addresses the estimation of deletion rates in mitochondrial genomes in humans. A PCR assay is used to detect whether or not a cell line contains any mitochondria with the deletion. The data comprise the results of the assay on several cell lines. We are developing analogs of the Luria-Delbruck method of fluctuation analysis for estimating the rate of deletions. The results are used to study the role of the deletion in Kearns-Sayre Syndrome patients. We are developing Monte Carlo likelihood methods for inference in genetics, aimed primarily at the study of mitochondrial and nuclear gene variation in humans, and its use in the study of human evolution. One focus is on the estimation of recombination rates, in particular between human disease loci and a variety of marker loci.