Research Center for Study of Gene Structure and Function

超出空间 但前提是。 导致传染病暴发的微生物--来自天然的，例如多重耐药的 金黄色葡萄球菌，或生物恐怖主义，如炭疽，原因-是典型的致病形式的单一 物种。种群基因组学是研究单一微生物物种的种内变异。 因此，揭示毒力因素和致病机制以及追踪 微生物疾病的源头。亨特学院的进化信息学实验室是多个 莱姆病病原伯氏疏螺旋体比较基因组学机构团队。我们是 负责生物信息学系统开发和进化统计分析，包括 实验设计、数据处理和入库，以及进化分析。通过进化 统计分析我们的实验室发现了令人惊讶的高数量的遗传交换和质粒转移 B.burgdorferi。继续我们在描述致病细菌种群结构方面的成功 基于比较基因组学，我提出了一个为期5年的项目，以发展生物信息学基础设施。 致力于病原微生物种群变异的分析。我们在这方面有两个具体目标 求婚。首先，我们将开发一种新的统计框架-非管家多位点测序 分型(NH-MLST)-分析细菌在系统发育背景下的种内变异，目标是 检测负责细菌适应性进化的基因。第二，我们将开发一个计算性的 致力于密切相关细菌的比较基因组学的基础设施。作为比较微生物 测序成为发现毒力因子和追踪微生物爆发的标准方法， 拟议的项目将提供一个强有力的进化分析框架和一个高效的计算 比较微生物基因组学系统。

EXCEED THE SPACE PROVIDED. Microbes responsible for infectious disease outbreaks - from either natural, e.g., multidrug-resistant Staphylococcus aureus, or bioterrorism, e.g.,anthrax, causes - are typically virulent forms of a single species. Population genomics, which is the study of intraspecific variations of a single microbial species, is therefore a key to uncovering virulence factors and disease-causing mechanisms, as well as tracking the sources of microbial disease. The Evolutionary Informatics Lab at Hunter College is a vital partner in a multi- institutional team of comparative genomics of Borrelia burgdorferi, the Lyme disease agent. We are responsible for the bioinformatics system development and evolutionary statistical analysis, including experimental design, data processing and warehousing, and evolutionary analysis. Through evolutionary statistical analysis our lab discovered a surprisingly high amount of genetic exchange and plasmid transfer in B. burgdorferi. To continue our success in characterizing the population structure of pathogenic bacteria based on comparative genomics, I propose a 5-years project to develop a bioinformatics infrastructure dedicated to the analysis of population variations of pathogenic microbes. We have two specific aims in this proposal. First, we will develop a novel statistical framework - non-housekeeping multilocus sequencing typing (NH-MLST) - of analyzing the bacterial intraspecific variations in a phylogenetic context, with the goal of detecting genes responsible for adaptive evolution in bacteria. Second, we will develop a computational infrastructure dedicated to the comparative genomics of closely related bacteria. As comparative microbial sequencing becomes a standard approach for discovering virulence factors and tracing microbial outbreaks, the proposed project will provide a robust evolutionary analytical framework and an efficient computational system for comparative microbial genomics.