Pilot study of a novel whole-genome sequencing based rapid bacterial identification assay in patients with bacteremia
IDWeek 2020
This is the first proof-of-concept feasibility study in an inpatient clinical setting of our culture-free, species agnostic process using whole genome sequencing and algorithmic tools for identifying the species and antimicrobial resistance profile of a bloodstream infection in real-time. The results suggest the approach is potentially more sensitive and significantly faster than gold standard culture-based diagnostics.
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Blood2Bac: species ID and AMR prediction of bacterial pathogens at low concentrations in blood using a rapid ultra-high enrichment process and nanopore sequencing
IDWeek 2020
This study demonstrated Blood2Bac's ability, when paired with Keynome, to test for a broad range of infections (50 bacterial species) and recover almost the entirety of the pathogen's genome directly from whole blood. The results represent a significant advancement over current molecular diagnostic approaches that are limited to testing just a handful of species and recover a very small portion of the pathogen's genetic code.
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ASM 2020
To address slow turnaround times related to culture-based diagnostics for blood stream infections, we have developed BacDetect, a rapid DNA amplification-based detection method that determines the presence of gram-positive or gram-negative bacteria in a sample in 30 minutes.
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SHEA 2020
Whole genome sequencing (WGS) is a high-resolution method for measuring bacterial relatedness to better understand infection transmission in suspected HAI cases. However, WGS is rarely used in HAI investigations due to a lack of access to computational analysis platforms with actionable turnaround times. Our ksim algorithm is a novel approach for rapidly and automatically determining pathogen relatedness using WGS data that has the potential to scale to tens of thousands of samples.
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Rapid ultra-high enrichment of bacterial pathogens at low concentration from whole blood for species ID and AMR prediction using Oxford Nanopore sequencing
London Calling 2020
Blood2BacTM is our species-agnostic culture-free method for enriching bacterial DNA directly from whole blood samples by a factor of 100,000,000. When this is coupled with rapid whole genome sequencing and our algorithmic tools, we can determine bacterial species identification and antimicrobial resistance within hours instead of days with culture.
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Multi-copy qnrA1 Plasmid Causes Elevated Quinolone Resistance in E. coli
London Calling 2019
Analyzing the role large multi-drug resistance (MDR) plasmids play in cultivating resistance can be tremendously challenging. In this work, we leverage nanopore long-read sequencing to overcome these challenges and produced a complete sequence of an MDR plasmid with multiple copies of resistance genes that appeared to significantly increase resistance to ciprofloxacin in E. coli strains carrying the plasmid.
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Achievement of rapid whole genome coverage of bacterial pathogens at 1 CFU/mL in blood
NCM 2018
Using whole genome sequencing for culture-free diagnosis of bacterial bloodstream infections is difficult because very little bacterial DNA is present in a clinical sample. Human DNA outnumbers bacterial DNA by 8-9 orders of magnitude. This work describes how we are able to rapidly enrich and sequence bacterial DNA for diagnosing bacterial bloodstream infections directly from clinical blood samples.
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counterr: Characterization of Context Dependent MinION Sequencing Errors
NCM 2018
In this work we described how we used counterr, our lightweight command line tool to characterize the error distributions in both amplified and native microbial ONT MinION sequencing data. Our results confirm a widely held belief that errors in MinION data strongly depend on sequence context. We hope that this improved error characterization can be useful for read error correction.
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Quantification of Predictive Power of Genomic Resistance Locus Databases Reveals Potential Limitations of Marker-Based Diagnostics
ASM 2017
Tools that rapidly and accurately predict the antibiotic resistance of a clinical infection promise to allow healthcare providers to quickly provide targeted, effective treatments to patients. However, most emerging diagnostic approaches detect the presence or absence of only a limited panel of resistance markers. In the this work we analyze the power of genomic resistance locus databases to predict the resistance profiles of Staphylococcus aureus, Streptococcus pneumonia, and Mycobacterium tuberculosis clinical isolates.
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