Amy Pickering

Closed (1) Optimized guide RNA design for detecting antibiotic resistance genes and pathogens in environmental samples

Applications for fall 2021 are now closed for this project.

Environmental transmission of pathogens is a threat to human health. Infectious agents of disease are commonly studied through the host pathogen model (e.g. infection in human cells) but occurrence of microorganisms that cause infectious diseases in environmental matrices (water, soil, fomites, foods) are poorly understood. One of the main barriers to detection of pathogenic microorganisms in the environment is low abundance and inability to culture many pathogens in the laboratory. Metagenomic sequencing, or sequencing all DNA in a sample, is a promising tool for detection of pathogens and antibiotic resistance genes (ARGs). However, sequencing depth continues to be a challenge in recovering rare targets (ARGs) and genomes of pathogenic microorganisms from environmental matrices.

Targeted sequencing using Cas9 is a promising new technology for detection of low abundance organisms. However, recovery of target sequences relies heavily on the design of guide RNAs. Existing methods for guide RNA design are primarily focused on gene editing applications in single species. The goal of this work is to integrate existing tools to optimize guide RNA design in complex microbial communities, typical in environmental samples. The student will develop methods to optimize guide RNA design by accounting for off-target effects in a complex background matrix.

Student will write a program to optimize the design of guide RNAs for targeted sequencing of antibiotic resistance genes and pathogens in complex sample matrices.

Day-to-day supervisor for this project: Dr. Erica Fuhrmeister, Post-Doc

Qualifications: Seeking computer science experience. Bash, Python, and experience with GitHub required, prior work in bioinformatics or computational biology a plus.

Weekly Hours: 3-5 hrs

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