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BIOINFORMATICS

生物信息学

基本信息

批准号：
7905602
负责人：
DAVID BOTSTEIN
金额：
$ 24.97万
依托单位：
PRINCETON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-09-01 至 2014-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7905602
关键词：
Address Adopted Algorithms Alleles Alternative Splicing Animal Model Area Bacteria Base Pairing Bioinformatics Biological Biological Assay Biological Process Biology Blast Cell Cellular biology Collaborations Collection Communities Complementary DNA Complex Computer software Computers Computing Methodologies Copy Number Polymorphism Custom DNA Resequencing Data Data Analyses Data Collection Data Set Data Sources Databases Dependence Detection Development Devices Disease Environment Equilibrium Escherichia coli Eukaryota Evaluation Evolution Fungal Genome Future Gene Expression Gene Expression Profile Gene Frequency Genes Genetic Genetic Screening Genome Genomics Genotype Gold Guidelines Haploidy Homeobox Human Image Analysis Imagery Individual Institutes Institution Introns Label Laboratories Laboratory mice Learning Length Libraries Malignant Neoplasms Mammals Manuals Maps Measurement Memory Messenger RNA Metabolism Methods Metric Microbe Mind Modeling Mus Mutation Mutation Detection Mutation Spectra National Institute of General Medical Sciences Nature Noise Nucleotides Nutrient Online Systems Organism Participant Pathway interactions Pattern Phenotype Positioning Attribute Process Progress Reports Proteins Reading Repetitive Sequence Reporting Research Research Personnel Ribosomal RNA Running Saccharomyces Saccharomyces cerevisiae Signal Transduction Site Source Speed Structure System Systems Integration Techniques Technology Time Tissue-Specific Gene Expression Tissues Training Transcript Untranslated Regions Variant Vibrio cholerae Weight Work Yeasts analytical tool base computer monitor computer technology development data integration data mining deletion analysis design embryonic stem cell functional genomics genome database genome sequencing genome wide association study genome-wide analysis high standard high throughput analysis human data human disease improved indexing infancy infrastructure development innovation instrument interest mutant new technology next generation novel nutrition oncology pluripotency protein function prototype repository research study spelling statistics success tool web based interface

项目摘要

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.

生物信息学：计算方法的开发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开发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路径建模的功能注释。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。子项目 1：基因组序列变异的全局识别（Kruglyak 和 Botstein）进展报告：三年前，Botsteln 和 Kruglyak 实验室开发了 SNPScanner，一种在全球范围内全面评估核苷酸变异的方法。这这种方法的基础是比较两种酵母菌株的杂交数据基因组：“参考基因组”菌株 S288c 和野生分离株 RM11。高质量序列通过原始酵母基因组测序项目[199]和RM11测序项目[200]可以获得这两种菌株的信息。这提供了 -25,000 个 SNP，用于训练算法，然后根据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说明[22]。这种在全球范围内检测酵母和细菌中核苷酸变异的公正方法越来越多地被世界各地学术和工业机构的其他研究人员采用。下面描述了刘易斯-西格勒综合基因组学研究所的三个不同应用，说明了该技术可以解决的各种问题。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]。有些菌株是彼此的镶嵌体，而另一些菌株则是明显的没有共同的近代遗传祖先；对于许多类型的实验来说，这会产生很大的差异。我们还使用这些信息来设计具有普遍非多态性探针的微阵列地区，这促进了对远缘物种的多种研究，特别是在初级物种中使用 Saccharomyces bayanus 进行项目实验室实验（见下文）。基因型和营养对饥饿酵母存活和代谢的影响[76] 遗传学研究的速度通常受到确定致病等位基因的难易程度的限制。这在表型微妙或费时费力的情况下会大大加剧来测定。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平铺微阵列。受控营养限制的进化适应的全部内容和动态酵母环境 [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进行实验进化的酵母中出现的突变。这使得能够研究与适应相关的完整突变谱，并对进化实验期间的等位基因频率进行回顾性分析。这代表了在实验进化的真核生物中全面突变识别的第一个例子——随着 SNPScanner 的发展，这个问题才变得易于处理。