Statistical and Computational Problems in Genetics and Molecular Biology

这项建议解决了遗传学和分子生物学中出现的各种统计和计算问题。该研究项目的一个重要目标是培训和发展这一跨学科领域的博士后研究员。这里描述的问题是我们与南加州大学和其他地方的遗传学家和分子生物学家正在进行的合作的一部分。所有这些指标都有重要的计算和统计部分。我们正在研究物理映射的统计和算法方面。其中包括光学图谱，通过这种图谱，利用荧光显微镜可以产生有序的限制图谱。长期目标是开发一个在线程序，生成这样一个有序的地图。我们正在分析基于序列标签位点方案变体的各种物理作图策略的效率。特别是，我们正在研究不均匀的克隆和锚定分布，错误、模棱两可或丢失锚定的影响，以及允许克隆行走和随机步骤混合的策略。我们正在开发基因组重排进化的统计模型，并根据这些数据重建系统发育树的方法。这在一定程度上是由大脑中GFAP启动子发生重排的数据推动的。一个相关的项目涉及系统发育树重建的其他方面，特别是当使用DNA序列数据来构建树时，站点之间的相关性的影响。在rRNA启动子数据的推动下，我们还在开发算法，当潜在树已知时，用于拟合更详细的进化模型。我们正在研究分子序列中模式计数的统计。它们经常被用来表征生物特征，如编码区、调控序列、剪接位点等。我们的重点是开发有用的模式计数的计算算法，当必须从数据估计潜在的序列结构时。另一个项目涉及对人类线粒体基因组缺失率的估计。用聚合酶链式反应检测细胞系是否含有缺失的线粒体。这些数据包括对几个细胞系的分析结果。我们正在开发与Luria-Delbruck波动分析方法类似的方法来估计缺失率。这些结果被用来研究缺失在Kearns-Sayre综合征患者中的作用。我们正在开发用于遗传学推断的蒙特卡罗似然方法，主要目的是研究人类线粒体和核基因的变异，并将其用于人类进化的研究。其中一个重点是重组率的估计，特别是人类疾病基因座和各种标记基因座之间的重组率。

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.