Research Center for Study of Gene Structure and Function

超出空间 提供了 导致传染病爆发的微生物-来自自然界，例如，多重耐 金黄色葡萄球菌或生物恐怖主义，例如，炭疽热，原因-是典型的有毒形式的单一 物种种群基因组学是研究单个微生物物种的种内变异， 因此，这是揭示毒力因子和致病机制以及追踪 微生物疾病的来源。亨特学院的进化信息学实验室是一个多方面的重要合作伙伴， 莱姆病病原体伯氏疏螺旋体的比较基因组学机构小组。我们 负责生物信息学系统开发和进化统计分析，包括 实验设计，数据处理和仓储，以及进化分析。通过进化 统计分析我们的实验室发现了惊人的高数量的遗传交换和质粒转移， B。burgdorferi。为了继续我们在表征致病菌种群结构方面的成功， 在比较基因组学的基础上，我提出了一个5年计划，以发展生物信息学基础设施 致力于分析病原微生物的群体变异。我们有两个具体目标 提议首先，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）-在系统发育背景下分析细菌种内变异，目标是 检测细菌适应性进化的基因。其次，我们将开发一个计算 致力于密切相关的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大的进化分析框架和一个有效的计算 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.