Cloud-enabled genomics resource for automated pathogen diagnostics in the field

描述（由申请人提供）：微生物病原体检测和表征的诊断改进将直接影响临床和公共卫生环境，特别是如果现场适用。基于基因组测序的方法的优点包括提高分离物分型的分辨率，在某些情况下可以达到单核苷酸水平，全面的表型预测，例如毒性和/或抗生素耐药性，以及提供基因组数据库作为社区和公共卫生资源。然而，为了采用基于基因组学的诊断系统，需要新的标准化方案，理想情况下，这种方案不需要广泛的培训或扩大典型诊断实验室的能力，并且可以在现场以最小的地方基础设施实施。为了解决这些问题，我们建议在项目的R21“概念验证”阶段，我们将展示建立自动化诊断管道的可行性，以支持微生物分离物分型，毒力和抗菌素耐药性分析以及系统发育分类的基因组序列分析。我们预计纳入的分析将影响个别患者的治疗过程，并将与公共卫生和感染控制有关。简而言之，我们将开发一个开放的数据库结构，用建议分析的参考序列填充它，并将其记录为未来扩展的社区资源（目标1）。该数据库结构将与自动生物信息学分析管道集成，根据参考数据库从当前平台搜索原始序列数据，并创建可操作的诊断报告（目标2）。此分析管道将利用现有的

DESCRIPTION (provided by applicant): Diagnostic improvements for the detection and characterization of microbial pathogens will directly impact clinical and public health settings, especially if field-applicable. Advantages of genome sequencing-based approaches include increased resolution for isolate typing, in some cases, down to single nucleotide level, comprehensive phenotype prediction, e.g. for virulence and/or antibiotic resistance, and provisioning of genomic databases as community and public health resources. However, in order to adopt a genomics-based diagnostic system, new standardized protocols are needed which ideally would not require extensive training or scale up for typical diagnostic laboratory capacities, and could be implemented with minimal local infrastructure in the field. To address these issues we propose that in the R21, "proof-of-concept" phase of the project, we will demonstrate the feasibility of building an automated diagnostic pipeline to support genome sequence analysis for microbial isolate typing, virulence and antimicrobial resistance profiling and phylogenetic classification. We anticipate that the included analyses will impact the course of individual patient treatment and will be relevant to public health and infection control. Briefl, we will develop an open database structure, populate it with reference sequences for the proposed analysis, and document it to serve as a community resource for future expansions (Aim 1). This database structure will be integrated with an automated bioinformatics analysis pipeline to search raw sequence data from current platforms against the reference database and create an actionable diagnostic report (Aim 2). This analysis pipeline will utilize an existing bioinformatics software infrastructure (CloVR), which provides portability, reproducibility, platform- independence and utilization of online cloud computing services from the local desktop using an easy-to-use graphical user interface. In the R33 phase of the project, the "transition to expanded development" will be completed by deploying a novel sequencing technology with the diagnostic system in a simulated field setting. During this phase we will provide comparative analysis with traditional methods to support the utility and accuracy of the developed diagnostic system. We will select, implement and test the sequencing platform, which is most affordable and applicable to the field setting with respect to cost, space, effort, and data generation, as wel as training for setup and operation (Aim 3). Sequencing platform and automated analysis pipeline will be tested on mock and real-life samples to validate diagnostic protocols, refine report structures, and determine confidence parameters (Aim 4). Overall, completion of this research plan will result in the development and implementation of a genome sequencing and analysis system that will require little more than a power supply, internet connection and an individual with minimal laboratory skills to integrate genome sequencing as a diagnostic tool with virtually limitless applications in any healthcare setting. PUBLIC HEALTH RELEVANCE: The goal of the work proposed in this application will be to develop, implement and test a field-deployable diagnostic resource that will utilize automated sequence analysis pipelines in combination with whole-genome sequencing for the identification, typing and characterization of human bacterial pathogens. The predicted decrease in cost, time and infrastructure required for high-throughput sequencing coupled with portable analysis using cloud computing enables the use of this transformative technology as a clinical and public health diagnostic tool with limitless future applications in the field. Overall the completion of the proposed studies will result in early adoption of an innovative methodology and creation of a community resource that could be adapted for use in clinical settings around the world.