Comparative Genomics to Identify Functional Blocks & HGT

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，确定人类基因组中区域功能的最有效技术之一是与其他物种的基因组进行比较。这种认识对疾病诊断和专门的药物和疫苗设计的意义是宝贵的。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，并再次有助于药物和疫苗的设计。这个项目有两个主要的研究目标。一是开发寻找跨物种高度保守的非编码序列（NCS）功能预测特征的方法，二是开发检测水平基因转移（HG'T）的新方法。基因组的比较是本研究的主线。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相结合，如微阵列表达数据、附近基因的go功能注释和chlp芯片数据。结果将用于评估每个NCS的功能相关性（如果有的话），然后根据可测量的协变量和序列结构定义功能的签名预测。例如，如果序列特征表征了与其最近的基因对特定功能有贡献的NCS，那么具有相同特征的NCS附近的未知基因将是该功能的主要候选者。研究者们建议用1)来解决这个问题。2)用随机模型的参数表示NCS，并通过重采样和其他蒙特卡罗方法确定合适的模型拟合阈值。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具有重要功能的FGT。第一种方法是采用一个已知的功能重要的家族（NIFgenes），其中HGT是一个有争议的问题，并设计出定量的措施，他们期望这些措施能够得出确切的结论。他们打算改进不同物种基因之间的相似性度量，如BLAST分数，校正进化距离。他们将计算不同物种的NIF基因对，已知是HGT（抗生素免疫基因）的对，以及非常不可能是HGT（核糖体蛋白）的基因对。第二种方法是寻找在细菌物种的大量亚群中保守的异常长的16s RNA，否则这些亚群只是远亲。数学和统计方面的挑战包括：在方法一中，对不同突变率的基因进行标准化比较；为HGT和非HGT设计一个合适的分类器，并计算一个基因在非HGT时被分类为HGT的概率，反之亦然；在方法II中，通过考虑系统发育树拓扑结构和分支长度，扩展现有的检测大型内含物的方法。

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.