Early pathogen detection and disease diagnosis is key to preventing the exponential spread of infectious diseases such as COVID-19. If not detected and contained early, the pathogen not only affects local communities but can cross national and international borders to become a global pandemic. Existing approaches for early pathogen detection can delay diagnosis during a pandemic. These identify regions of interest specific to a pathogen type/sub-type and amplify these regions in target specimens (if present), making them inapplicable to unknown pathogens. Another limitation is the dependence on the design of primer chemistry, which is a complex, error-prone, and time-consuming process that can delay pathogen detection in the early stages of a pandemic. Further, it is also important to track the accumulation of novel mutations to identify disease hotspots, monitor virus evolution, guide immunization efforts, and inform public healthcare plans. It has been estimated that RNA coronaviruses such as SARS-CoV-2 can accumulate 30 substitutions/year. The proposed approach will enable design for fast and accurate whole-genome sequencing (WGS) analysis pipelines for strain typing and phylogenetic classification. The proposed real-time diagnosis pipeline can potentially serve as a "digital sniffer" and be used to detect potential outbreaks early.

The researchers propose to build real-time diagnosis pipelines for early pathogen detection using the portable Oxford Nanopore Sequencer MinION. The read-until functionality of the sequencer can be used to adaptively target sequencing towards genome regions with low-coverage and accelerate whole-genome sequencing of unknown pathogens. The ultimate goal of this project is to develop point-of-care mobile rapid pathogen diagnosis and analysis. This will be realized by developing novel hardware-acceleration techniques and algorithms for quick classification of viral/bacterial pathogens, followed by strain detection and epidemiology analysis. The proposed techniques include hardware-software co-design for squiggle-space classification algorithms, refined indexes, exploiting real-time feedback feature of MinION sequencing devices (read-until and run-until), and variant calling to achieve rapid analysis. The project will also explore direct RNA sequencing without reserve transcriptase step to reduce library-preparation time.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
Division of Computer and Communication Foundations (CCF)
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Almadena Chtchelkanova
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Regents of the University of Michigan - Ann Arbor
Ann Arbor
United States
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