Comparative Genomics to Identify Functional Blocks & HGT

比较基因组学来识别功能模块

• 批准号: 7064837
• 负责人: peter J bickel
• 金额: $ 17.03万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-06-01 至 2009-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7064837
• 关键词: computer program /software; computer system design /evaluation; gene expression profiling; genetic recombination; genetics; method development; microarray technology; ribosomal RNA; statistics /biometry

DESCRIPTION (provided by applicant): As the genomes of more and more species are sequenced it has become apparent that 1 of the most powerful techniques for detemining region function in the human genome is by comparison to the genomes of other species. The implications of such understanding for disease diagnosis and specialized drug and vaccine design are dear. Similarly, genomic comparison between bacteria can reveal regions which are functionally important in the development of infectious diseases and again aid drug and vaccine design. This project has 2 primary research goals. One (1) is the development of methodology for finding functionally predictive signatures of non-coding sequences (NCS) highly conserved across multiple species, and the other is to develop novel approaches for defecting Horizontal Gene Transfer (HG'T). The comparison of genomes is the common thread in this research. ln pursuit of their first goal, the investigators plan to integrate genomic sequence data, provided by their collaborators, with experimental and literature data, such as microarray-expression data, GO-functional-annotation for nearby genes, and ChlP-Chip data. The results will be used to evaluate the functional relevance, if any, of each NCS and then to define a signature predictive of function in terms of measurable covariates and sequence structure. For instance, if a sequence signature characterizes NCS whose nearest genes contribute to a particular function then an unknown gene close to an NCS with the same signature would be a prime candidate for interrogation of that function. The Investigators propose to attack this problem by 1). Developing non standard types of clustering methods based on supervised learning algorithms, e.g., Random Forests, 2) Representing the NCS by the parameters of a stochastic model and determining appropriate thresholds for model fitting by using resampling and other Monte Carlo methods. Under the second topic, the investigators propose 2 different approaches for determining whether functionaIly significant FGT has occurred in bacteria. The first approach is to take a known functionally important famlly (NIFgenes) for which HGT is a matter of dispute, and devise quantitative measures which they expect will enable a firm conclusion. They intend to refine similarity measures between genes in different species ,such as BLAST scores, corrected for evolutionary distance. They will compute these measures for pairs of NIF genes in different species, pairs, pairs of genes known to be HGT (antibiotic immunity conferring genes) and genes very unlikely to be HGT (ribosomal proteins). The second approach is to look for anomalously long stretches of 16s RNA conserved within substantial subsets of bacterial species which are otherwise only distantly related. Mathematical and statistical challenge include: Under approach I, standardizing comparisons of genes with different mutation rates; devising an appropriate classifier for HGT vs. non HGT, and computing appropriate estimates of the probability of classifying a gene as HGT when it isn't and vice versa; Under approach II, extending existing methods for detecting large inclusions by taking into account phylogenetic tree topology and branch lengths.

描述（由申请人提供）：随着越来越多物种的基因组被测序，很明显，用于确定人类基因组中区域功能的最强大的技术之一是通过与其他物种的基因组进行比较。这种理解对疾病诊断和专门的药物和疫苗设计的影响是很大的。同样，细菌之间的基因组比较可以揭示在传染病发展中具有重要功能的区域，并再次帮助药物和疫苗设计。该项目有两个主要研究目标。一（1）是开发用于发现在多个物种中高度保守的非编码序列（NCS）的功能预测特征的方法，另一个是开发用于检测水平基因转移（HGT）的新方法。基因组比较是本研究的共同主线。为了实现他们的第一个目标，研究人员计划将合作者提供的基因组序列数据与实验和文献数据整合在一起，例如微阵列表达数据，附近基因的GO功能注释和ChIP芯片数据。结果将用于评价每个NCS的功能相关性（如有），然后根据可测量的协变量和序列结构定义预测功能的特征。例如，如果序列特征表征其最近基因有助于特定功能的NCS，则接近具有相同特征的NCS的未知基因将是询问该功能的主要候选者。研究人员建议通过1）解决这个问题。开发基于监督学习算法的非标准类型的聚类方法，例如，2）用随机模型的参数表示NCS，并通过使用响应和其他Monte Carlo方法确定用于模型拟合的适当阈值。在第二个主题下，研究人员提出了两种不同的方法来确定细菌中是否发生了功能上显著的FGT。第一种方法是采取一个已知的功能重要的家庭（NIFgenes），HGT是一个有争议的问题，并制定定量措施，他们期望将使一个坚定的结论。他们打算改进不同物种基因之间的相似性度量，如BLAST评分，并根据进化距离进行校正。他们将计算不同物种中的NIF基因对，已知是HGT（抗生素免疫赋予基因）的基因对和极不可能是HGT（核糖体蛋白）的基因对的这些度量。第二种方法是寻找在细菌物种的大量亚群中保守的16 s RNA的异常长的片段，否则它们只是远亲。 数学和统计挑战包括：在方法I下，标准化具有不同突变率的基因的比较;为HGT与非HGT设计适当的分类器，并计算将基因分类为HGT的概率的适当估计，反之亦然;在方法II下，通过考虑系统发育树拓扑结构和分支长度来扩展现有的检测大包涵体的方法。