ATD: Collaborative Research: Statistical Ensembles for the Identification of Bacterial Genomes

ATD：合作研究：用于鉴定细菌基因组的统计集合

• 批准号: 1120404
• 负责人: Jennifer Clarke
• 金额: $ 70.84万
• 依托单位: University of Miami School of Medicine
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-15 至 2014-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1120404&HistoricalAwards=false
• 关键词: ATD; Collaborative; Research; Statistical; Ensembles

This research focuses on reducing bioterrorism threat by integrating tools from genomics and statistics in ways that have not been previously examined. The investigators develop novel statistical theory and computational tools for accurate pathogen detection based on next generation sequencing data. Key research directions involve (i) classification by sequence enrichment; (ii) comparison of empirical clusterings and reference genomes; and (iii) shrinkage estimation and model selection in hierarchical log-linear models. In addition to an in-depth characterization of the theoretical properties of these new statistical inference techniques, the investigators perform a thorough assessment of their practical importance in the context of the detection and identification of bacterial genomes. This assessment is done using publicly available data from sources such as the Human Microbiome Project, the NCBI Short Read Archive, the European Bioinformatics Institute, and the Broad Institute. The applicability of this new methodology is broad and relates to high-dimensional settings in which choosing an appropriate class of candidate statistical models is difficult. The investigators study statistical ensembles, combinations of techniques that have been shown to provide more reliable inferences than any single statistical approach. As opposed to existing work which combines models from the same class, this new framework concerns ensembles that cross class boundaries and optimally combine inferences from multiple models from several model classes. These ensembles are expected to have distinct advantages over existing approaches, such as robustness to model misspecification and improved predictive performance.The new statistical methodology developed in this proposal has the potential to substantially improve the response of federal and international agencies to a bioterrorism attack through a rapid identification of differences in microbial genomes and their accurate classification as harmless or potentially pathogenic. The impact of these algorithms for pathogen detection on both information technology and civil infrastructure is maximized through their implementation in user-friendly, open-source computational tools and software that will be freely available to the public. The project also has a significant educational and mentorship component for students and postdoctoral fellows who are interested in enhancing our ability to respond rapidly and appropriately to (i) incidents of bioterrorism, and (ii) microbial threats to public health.

这项研究的重点是通过以前尚未检查的方式将工具从基因组学和统计数据中整合起来来减少生物恐怖主义威胁。研究人员开发了基于下一代测序数据的精确病原体检测的新型统计理论和计算工具。主要的研究方向涉及（i）按顺序富集进行分类； （ii）比较经验聚类和参考基因组； （iii）分层对数线性模型中的收缩估计和模型选择。除了对这些新统计推断技术的理论特性的深入表征外，研究人员还对细菌基因组的检测和鉴定进行了彻底评估其实际重要性。该评估是使用来自人类微生物组项目，NCBI Short Read Archive，欧洲生物信息学研究所和Broad Institute等消息来源的公开数据进行的。这种新方法的适用性广泛，与高维度相关，其中选择适当的候选统计模型非常困难。研究人员研究了统计组合，与任何单一统计方法相比，已证明可以提供更可靠的推论的技术组合。与现有的工作结合了同一类的模型相反，这个新框架涉及跨越界限的合奏，并最佳地结合了来自多个模型类的多个模型的推论。预计这些合奏将在现有方法上具有明显的优势，例如建模错误指定和改进的预测性能。该提案中开发的新统计方法学有可能通过快速识别微生物基因组及其准确的潜在的病情良好的差异来大大改善联邦和国际机构对生物恐怖攻击的反应。这些算法对病原体检测对信息技术和民用基础设施的影响通过它们在用户友好，开源计算工具和软件中的实施来最大化，这些工具和软件将免费提供给公众。该项目还为学生和博士后研究员提供了重要的教育和指导组成部分，他们有兴趣增强我们对生物恐怖主义事件的快速和适当反应的能力，以及（ii）对公共卫生的微生物威胁。