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

这项研究的重点是通过整合基因组学和统计学的工具来减少生物恐怖主义的威胁，这是以前没有研究过的。研究人员开发了新的统计理论和计算工具，用于基于下一代测序数据的准确病原体检测。重点研究方向包括(1)序列富集分类；（ii）经验聚类和参考基因组的比较；（三）层次对数线性模型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进行深入表征外，研究人员还对其在细菌基因组检测和鉴定方面的实际重要性进行了全面评估。这项评估是利用来自人类微生物组项目、NCBI短读档案、欧洲生物信息学研究所和布罗德研究所等来源的公开数据完成的。这种新方法的适用性是广泛的，并且与高维设置有关，在高维设置中选择适当的候选统计模型类别是困难的。研究人员研究统计集成，技术组合，已被证明提供更可靠的推论比任何单一的统计方法。与现有的将来自同一类的模型组合在一起的工作相反，这个新框架关注跨类边界的集成，并以最佳方式组合来自几个模型类的多个模型的推断。这些集成预计比现有方法具有明显的优势，例如对模型错误规范的鲁棒性和改进的预测性能。本提案中开发的新的统计方法有可能通过快速识别微生物基因组的差异并将其准确分类为无害或潜在致病性，从而大大提高联邦和国际机构对生物恐怖主义袭击的反应。这些病原体检测算法对信息技术和民用基础设施的影响通过在用户友好的开源计算工具和软件中实现最大化，这些工具和软件将免费向公众提供。该项目还有一个重要的教育和指导组成部分，面向有兴趣提高我们迅速和适当地应对（一）生物恐怖主义事件和（二）微生物对公共卫生的威胁的能力的学生和博士后研究员。

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.