BIOINFORMATICS

生物信息学：预防方法的发展 用于稳健的功能分析 项目负责人：O. Troyanskaya，（CompSci/LSI） 我们的中心提供高度协作的环境和必要的计算和实验 生物信息学方法的发展和全球传播的基础设施，帮助我们回答从基因组规模的进化动力学到 对通路建模的功能注释。这些方法的范围从解决基因组中的核苷酸变异，到其功能表征，再到推断调控网络。 分项目1：全球基因组序列变异鉴定（Kruglyak和Botstein） 进展报告：三年前，Botsteln和Kruglyak实验室开发了 SNPScanner是一种在全球范围内全面评估核苷酸变异的方法。的 这种方法的基础是比较来自两种具有分歧的酵母菌株的杂交数据。 基因组：“参考基因组”菌株S288 c和野生分离株RM 11。高质量序列 通过原始酵母基因组测序计划[199]和RM 11测序计划[200]，可以获得这两种菌株的信息。这提供了~ 25，000个SNP，用其训练算法，然后基于从单个微阵列获得的杂交数据预测基因组中SNP的存在。2006年报道了该方法的描述及其应用举例[22]。越来越多地，这种在全球范围内检测酵母和细菌中核苷酸变异的无偏见方法已被世界各地的学术和工业机构的其他研究人员所采用。下面描述了来自Lewis-Sigler综合基因组学研究所的三个不同的应用，说明了可以用这种技术解决的各种问题。 常用酵母菌核苷酸水平变异的全基因组分析 酿酒酵母菌株[39]我们使用平铺阵列分析了常用的实验室菌株， SNPScanner算法[22]。有些菌株是彼此的嵌合体，而另一些菌株则明显地 没有最近的遗传祖先;对于许多种类的实验来说，这会产生非常大的差异。我们 我也利用这些信息设计了微阵列， 这促进了对远亲物种的许多研究，特别是在小岛屿国家， 使用贝酵母的项目实验室实验（见下文）。 基因型和营养对饥饿酵母存活和代谢的影响[76] 遗传学研究的步伐通常受到确定致病等位基因的容易程度的限制。这 在表型不明显或费时费力的情况下， 化验。在这项研究中，平铺阵列-SNP扫描仪突变检测系统使我们能够识别 几十个基因通过突变，其唯一的表型是抑制一个微妙的表型， 单个平铺微阵列。 控制营养限制的进化适应的剧目和动力学 [201]在这项研究中，我们使用SNPScanner算法全面地 检测在各种营养限制条件下经历实验进化的酵母中出现的突变。这使得研究与适应相关的突变的完整谱和对进化实验期间等位基因频率的回顾性分析成为可能。这代表了在实验进化的真核生物中全面鉴定突变的第一个例子-这个问题只有随着SNPScanner的发展才变得容易处理。

BIOINFORMATICS: DEVELOPMENT OF COMPUTATIONAL METHODS FOR ROBUST FUNCTIONAL ANALYSIS Project Leader: O. Troyanskaya, (CompSci/LSI) Our Center provides a highly collaborative environment and necessary computing and experimental Infrastructure for the development and global dissemination of bioinformatics methods that help us to answer diverse systems-level questions ranging from genome-scale evolutionary dynamics to functional annotation to pathway modeling. These methods range from addressing nucleotide variation in the genome, to its functional characterization, to inferring regulatory networks. Subproject 1: Global identification of genome sequence variation (Kruglyak and Botstein) Progress Report: Three years ago, the Botsteln and Kruglyak laboratories developed SNPScanner, a method for comprehensively assessing nudeotide-variation on a global scale. The basis of this approach was to compare hybridization data from two yeast strains with diverged genomes: the "reference genome" strain S288c and a wild isolate, RM11. High quality sequence information was available for these two strains through the original yeast genome sequencing project [199] and the RM11 sequencing project [200]. This provided -25,000 SNPs with which to train an algorithm that then predicted the presence of SNPs in a genome based on hybridization data obtained from a single microarray. The description of this method and illustration of its applications was reported in 2006 [22]. Increasingly, this unbiased approach to detecting nucleotide variation on a global scale in yeast and bacteria has been adopted by other researchers in both academic and industrial institutions around the worid. Three distinct applications from the Lewis-Sigler Institute for Integrative Genomics illustrating the variety of questions that can be addressed with this technology are described below. Genome-wide analysis of nucleotide-level variation in commonly used Saccharomyces cerevisiae strains [39] We analyzed the commonly used laboratory strains using the tiling arrays and the SNPScanner algorithm [22]. Some strains are mosaics of each other, whereas others cleariy share no recent genetic ancestors; for many kinds of experiments, this makes a very big difference. We have used this information also to design microarrays that have probes in universally nonpolymorphic regions, which has facilitated many kinds of studies with distantly related species, notably in the Junior Project Laboratory experiments using Saccharomyces bayanus (see below). Influence of genotype and nutrition on survival and metabolism of starving yeast [76] The pace of genetics studies is typically limited by the ease of determining of the causative allele. This is greatly exacerbated in cases in which the phenotype is either subtle or time-consuming and laborious to assay. In this study the tiling array-SNP scanner mutation detection system allowed us to identify dozens of genes through mutations whose only phenotype is suppression of a subtle phenotype on a single tiling microarray. The repertoire and dynamics of evolutionary adaptations to controlled nutrient-limited environments in yeast [201] In this study we used the SNPScanner algorithm to comprehensively detect mutations that had arisen in yeast subjected to experimental evolution under a variety of nutrient limiting conditions. This enabled a study of the complete spectrum of mutations associated with adaptation and a retrospective analysis of the allele frequencies during the evolution experiment. This represented the first example of comprehensive mutation identification in an experimentally evolved eukaryotic organism - a problem that only became tractable with the development of SNPScanner.