ICBiome — Department of Health and Human Services SBIR Phase I: NIAID

Description

Outbreaks have become a significant public threat in hospitalsimpacting both patient lives and creating a financial burdenWith the increasing rise in antibiotic resistancenew interventions are urgently needed to contain ongoing outbreaksRecent studies have confirmed that whole genome sequencingWGSis able to identity unique mutations within each outbreak strainwhich can then be utilized to establish transmission routesHoweversignificant bioinformatics challenges exist in utilizing WGS for outbreak analysesWGS reads are inherently noisyand traditional read mapping techniques require the careful selection of quality criteria to identify and remove artefactual SNPsThis can be a challenge when often only a single nucleotide polymorphismSNPmay separate two outbreak isolatesThis has resulted in a high barrier for routine adoption of genomics based interventions during outbreaksIn this proposalwe look to develop a fully automated cloud based bioinformatics platform that can be rapidly leveraged in the event of an outbreakOur platform will avoid current read mapping approaches to curate erroneous assemblies and instead adopt a different methodology that utilizes new Amazon Web ServicesAWScloud computing components such as AWS Lambda and DynamoDBIn a preliminary evaluationwe performed a manual analysis of our approach by processing raw sequence data from two hospital outbreaksEnterococcus faecium and Escherichia coliIn both casesour results matched the published phylogeny that was derived using read mappingThis manual evaluation strengthens the premise of our approach since the first outbreak only had a singlenon synonymous SNP that separated the three strains involved in the outbreakOur cloud based diagnostics framework will be implemented in Amazon Web ServicesAWScloudIt will be evaluated against public NCBI data from several major hospital outbreaksOur Phasebenchmark is to complete all analyses for each outbreak within one hourOur Phaseaims areDevelop an assembly module that assembles all the raw sequence data and then identifies artefactual SNPs within each outbreak assemblyDevelop a biomarker module that establishes unique biomarker sequences by removing both artefactual SNPs and low quality SNPsandDevelop a control module that combines the results of both the assembly module and the biomarker module to establish phylogenyDuring Phasedevelopmentwe look to formally evaluate the platform by sequencing bacterial cultures from historical outbreaks and then uploading the sequence data to our cloud bioinformatics platform Hospital outbreaks are a significant public health burdenWhile whole genome sequencing shows promise in controlling outbreakssignificant bioinformatics challenges exist as only a few mutations separate outbreak strainsCurrent methodologies for establishing these biomarker mutations require a complex set of tools and approaches that often need manual validationIn this proposala different approach is outlined for curating erroneous assemblies and establishing outbreak phylogenyOur bioinformatics platform will be cloud based and can be rapidly leveraged by any hospital that has an ongoing outbreakOur phylogeny results will allow infection control to identify the transmission routes between the outbreak strains and take corrective actions