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

支持云的基因组学资源，用于现场自动病原体诊断

• 批准号: 8301079
• 负责人: W. Florian Fricke
• 金额: $ 20.53万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-01 至 2014-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8301079
• 关键词: Address; Adopted; Adoption; Antibiotic Resistance; Antimicrobial Resistance; Bioinformatics; Classification; Clinical; Communities; Community Health; Computer software; Computers; Coupled; Data; Data Set; Databases; Detection; Development; Diagnostic; Disease Outbreaks; Environment; Escherichia coli; Evaluation; Future; Generations; Genome; Genomics; Goals; Health Personnel; Health Resources; Healthcare; Human; Individual; Infection Control; Internet; Knowledge; Laboratories; Large-Scale Sequencing; Life; Linux; Maintenance; Marketing; Medical; Methodology; Methods; Microbial Genome Sequencing; Molecular; Nucleotides; Patients; Phase; Phenotype; Phylogenetic Analysis; Play; Power Sources; Preparation; Protocols documentation; Public Health; Reporting; Reproducibility; Research; Research Infrastructure; Resistance profile; Resolution; Resources; Role; Salmonella enterica; Sampling; Sequence Analysis; Services; Shigella; Simulate; Structure; System; Technology; Testing; Time; Training; Virulence; Work; base; comparative; cost; database structure; genome sequencing; graphical user interface; independency; innovation; innovative technologies; microbial; novel; open source; operation; pathogen; portability; scale up; skills; tool; usability; virtual

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.

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，特别是在现场适用的情况下。基于基因组测序的方法的优点包括提高分离分型的分辨率，在某些情况下，可降低至单核苷酸水平，全面的表型预测，例如。用于毒力和/或抗生素耐药性，并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，需要新的标准化协议，理想情况下不需要大量培训或扩大典型诊断实验室能力，并且可以在现场以最少的当地基础设施实施。为了解决这些问题，我们建议在 R21 项目的“概念验证”阶段，我们将展示建立自动化诊断管道的可行性，以支持微生物分离分型、毒力和抗菌素耐药性分析的基因组序列分析和系统发育分类。我们预计所包含的分析将影响个体患者的治疗过程，并将与公共卫生和感染控制相关。简而言之，我们将开发一个开放的数据库结构，在其中填充参考序列以进行拟议的分析，并将其记录下来作为未来扩展的社区资源（目标 1）。该数据库结构将与自动化生物信息学分析管道集成，以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）。该分析管道将利用现有的 生物信息学软件基础设施 (CloVR)，它提供可移植性、可重复性、平台独立性以及使用易于使用的图形用户界面从本地桌面使用在线云计算服务。在该项目的 R33 阶段，“向扩大开发的过渡”将通过在模拟现场环境中部署带有诊断系统的新型测序技术来完成。在此阶段，我们将提供与传统方法的比较分析，以支持所开发的诊断系统的实用性和准确性。我们将选择、实施和测试测序平台，该平台在成本、空间、工作量和数据生成方面最经济且适用于现场设置，以及设置和操作培训（目标 3）。测序平台和自动分析流程将在模拟和现实样本上进行测试，以验证诊断协议、完善报告结构并确定置信参数（目标 4）。总体而言，该研究计划的完成将导致基因组测序和分析系统的开发和实施，该系统只需要电源、互联网连接和具有最低实验室技能的个人即可将基因组测序作为诊断工具与虚拟技术相结合。在任何医疗保健环境中都有无限的应用。 公共健康相关性：本申请中提出的工作目标是开发、实施和测试可现场部署的诊断资源，该资源将利用自动序列分析管道与全基因组测序相结合来识别、分型和表征人类细菌病原体。预计高通量测序所需的成本、时间和基础设施都会减少，再加上使用云计算的便携式分析，使得这种变革性技术能够用作临床和公共卫生诊断工具，在该领域具有无限的未来应用。总体而言，拟议研究的完成将导致早期采用创新方法并创建可适用于世界各地临床环境的社区资源。