Comparative Genomics to Identify Functional Blocks & HGT

随着越来越多物种的基因组被测序，很明显，其中一个MOSL 确定人类基因组中区域功能的强大技术是通过与 其他物种。这样的认识对疾病诊断和专业药物和疫苗的影响 设计是清晰的。类似地，细菌之间的基因比较可以揭示功能上的区域 在传染病的发展中起着重要作用，并再次有助于药物和疫苗的设计。这个项目有两个 主要研究目标。一个是寻找功能性预测特征的方法学的发展 非编码序列(NCs)在多种物种中高度保守，另一种是开发新的 水平基因转移检测方法(HOT)。基因组的比较是一条共同的主线 为了追求他们的第一个目标，研究人员计划整合基因组序列数据，由 他们的合作者，利用实验和文献数据，如微阵列表达数据，Go-Functive- 邻近基因的注释和ChlP-Chip数据。结果将用于评估功能相关性，如果 然后根据可测量协变量定义函数的特征预测和 序列结构。例如，如果序列签名描述了其最近的基因对 一个特定的功能，那么与具有相同签名的NCS接近的未知基因将是首选候选 对这一功能的审问。调查人员提议通过1)来解决这个问题。发展中的非标准 基于监督学习算法的聚类方法类型，例如，随机森林，2)表示 NCS通过随机模型的参数来确定合适的模型拟合阈值 重采样和其他蒙特卡罗方法。 在第二个主题下，调查人员提出了两种不同的方法来确定 在细菌中已经发生了具有重要功能的HGT。第一种方法是将已知的功能重要的 HGT存在争议的家庭(NIFgene)，并制定他们预计将 使得出坚定的结论。他们打算改进不同物种的基因之间的相似性测量，例如 BLAST分数，经过进化距离校正后，他们将计算成对NIF基因的这些度量 不同的物种，已知的一对基因是HGT(抗生素免疫授权基因)，而且基因非常 不太可能是HGT(核糖体蛋白)。第二种方法是寻找异常长的16号星。 RNA在细菌物种的大量亚群中保守，否则这些细菌物种只能是远亲。 数学和统计学方面的挑战包括：在方法一下，将基因与 不同的突变率；为HGT和非HGT设计适当的分类器，并计算适当的 对当一个基因不是HGT时将其归类为HGT的概率的估计；在方法II下， 通过考虑系统发育树拓扑和扩展现有方法来检测大型夹杂物 奥兰奇长度。

As the genomes of more and more species are sequenced it has become apparent that one of the mosl powerful techniques for determining 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 clear. Similarly, genpmic 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 two primary research goals. One 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 detecting Horizontal Gene Transfer (HOT). The comparison of genomes is the common thread in this research.In 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 two different approaches for determining whether Functionally significant HGT has occurred in bacteria. The first approach is to take a known functionally important family(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 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 oranch lengths.