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

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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.