A COMPREHENSIVE STUDY ON ALU ELEMENTS

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 Alu元素是灵长类特有的短散布元素(Sine)。每个Alu的长度约为300bp，其名称来源于限制酶Alui的单个识别位点。Alu元素约占人类基因组的11%，在灵长类基因组中已扩增到100多万个拷贝 在过去的6500万年中，已经产生了一系列不同年龄的Alu亚家族。虽然Alu元素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巨大贡献，并在人类遗传中占有相当大的比例。 疾病被归因于破坏性的Alu插入和突变。这项研究的目标是开发第一个定制的数据挖掘框架，该框架可以在更大范围的碱基对上计算表征Alu插入位点的偏好，并在智能上帮助寻找致病的Alu插入突变。具体来说，我们的目标是： 目的1.建立一个有效的、可扩展的、无偏见的Alu插入位点预测的判别数据挖掘框架。将被调查的三个不可避免和不同的组成部分定义如下： I.ALU序列数据的专用特征生成方法； 二、一种基于分治的频繁项集挖掘驱动的特征选择和求精机制； 三、一种基于概率树集成的可扩展且无偏的判别模型。 目的2.设计一个生物学测试框架，重点是通过系统发育足迹对所提出的Alu插入位点预测模型进行全面验证。 这项拟议的研究不仅将有助于了解Alu元素本身及其在人类遗传病中的作用，还将有助于整合和开发一系列用于生物序列分析的先进数据挖掘技术。实现这一目标的重大进展将有助于全面认识人类疾病的ALU生物学和遗传基础。

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.