A COMPREHENSIVE STUDY ON ALU ELEMENTS

这个子项目是许多利用资源的研究子项目之一 由NIH/NCRR资助的中心拨款提供。子项目的主要支持 子项目的主要研究者可能是由其他来源提供的， 包括其它NIH来源。 列出的子项目总成本可能 表示子项目使用的中心基础设施的估计数量， 而不是由NCRR赠款提供给子项目或子项目工作人员的直接资金。 Alu元件是灵长类特有的短散布元件（SINE）。每个Alu 其长度约为300 bp，其名称来源于单个识别位点， 限制性内切酶BamHI。Alu大约占人类基因组的11%， 在灵长类基因组中， 在过去的6500万年里，一系列不同年龄的Alu亚科已经 被生成。虽然Alu元素没有已知的生物学功能， Alus的繁殖对Alus的进化、结构和 人类基因组的动态，以及人类基因组中很大一部分 疾病已被归因于破坏性的Alu插入和突变。的目标 建议的研究是开发第一个定制的数据挖掘框架， 可以在更宽的范围内计算表征Alu插入位点的偏好 的碱基对，并在智力上帮助狩猎致病铝插入 突变。具体而言，我们的具体目标是： 目标1。开发一种有效的、可扩展的、无偏的判别式数据挖掘方法 Alu插入位点预测的框架。三个不可避免的不同组成部分 将被调查的定义如下： I. Alu序列数据的特征生成方法 二.一种基于分治的特征选择和细化机制， 由频繁项集挖掘驱动; 三.一种基于概率树的可扩展无偏判别模型 合奏。 目标2.设计一个生物测试框架，更加注重彻底 通过系统发育验证所提出的Alu插入位点预测模型 脚印 建议的研究不仅有助于了解铝元素本身 及其对人类遗传疾病的影响，以及整合和开发 一系列先进的生物序列分析数据挖掘技术。 在实现这一目标方面取得重大进展将有助于全面承认铝 人类疾病的生物学和遗传学基础。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Alu elements are primate-specific short interspersed elements (SINEs). Each Alu is approximately 300 bp in length and derives its name from a single recognition site for the restriction enzyme AluI. Comprising roughly 11% of the human genome, Alu elements have amplified to more than one million copies in primate genomes over the last 65 million years, and a series of Alu subfamilies of different ages has been generated. Although Alu elements have no known biological function, the propagation of Alus has contributed a great deal to the evolution, structure, and dynamics of the human genome, and a significant proportion of human genetic disease has been ascribed to the disruptive Alu insertions and mutations. The goal of the proposed research is to develop the first customized data mining framework that can computationally characterize preferences of Alu insertion site on a broader range of base pairs, and intellectually assist hunting down disease-causing Alu insertion mutations. In particular, our specific aims are: Aim 1. To develop an effective, scalable and unbiased discriminative data mining framework for Alu insertion site prediction. Three inevitable and distinct components that will be investigated are defined as follows: i. Specialized feature generation methods for Alu sequence data; ii. A divide-and-conquer based feature selection and refinement mechanism which is driven by frequent-itemset mining; and iii. A scalable and unbiased discriminative model augmented by probabilistic tree ensembles. Aim 2. To design a biological testing framework with increased focus on thoroughly validating proposed Alu insertion site prediction model through phylogenetic footprinting. The proposed study will assist not only in understanding Alu elements themselves and their effects in human genetic diseases, but also in integrating and developing a series of advanced data mining techniques for biological sequence analysis. Significant progress towards this goal will contribute to the overall recognition of Alu biology and genetic basis of human diseases.