A COMPREHENSIVE STUDY ON ALU ELEMENTS

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 Alu元件是灵长类特有的短散布元件（西内斯）。每个Alu的长度约为300 bp，其名称来源于限制性内切酶BamHI的单一识别位点。Alu元件约占人类基因组的11%，在灵长类动物基因组中已扩增至超过一百万个拷贝 在过去的6500万年里，已经产生了一系列不同年龄的Alu亚科。虽然Alu元件没有已知的生物学功能，但Alu的繁殖对人类基因组的进化、结构和动力学做出了很大贡献，并且在人类基因组中占很大比例。 疾病已被归因于破坏性的Alu插入和突变。拟议的研究的目标是开发第一个定制的数据挖掘框架，可以计算表征更广泛的碱基对上的Alu插入位点的偏好，并在智力上帮助追捕致病的Alu插入突变。具体而言，我们的具体目标是： 目标1.建立一个有效的、可扩展的、无偏的判别式数据挖掘框架，用于Alu插入位点的预测。将研究的三个不可避免的不同组成部分定义如下： I. Alu序列数据的特征生成方法 二.基于频繁项集挖掘驱动的特征选择和细化机制; 三.一个由概率树集合增强的可扩展无偏判别模型。 目标二。设计一个生物学测试框架，通过系统发育足迹法彻底验证所提出的Alu插入位点预测模型。 这项研究不仅有助于了解Alu元素本身及其在人类遗传疾病中的作用，而且有助于整合和开发一系列先进的生物序列分析数据挖掘技术。实现这一目标的重大进展将有助于全面认识铝生物学和人类疾病的遗传基础。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. 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.