# Developing Computational Methods for Surveillance of Antimicrobial Resistant Agents

> **NIH NIH R01** · UNIVERSITY OF FLORIDA · 2022 · $422,334

## Abstract

PROJECT ABSTRACT
 Antimicrobial resistance is a critical public health issue. Infections with drug resistant pathogens are estimated
to cause an additional eight million hospitalization days annually over the hospitalizations that would be seen for
infections with susceptible agents. The use of antibiotics (in both clinical and agricultural settings) is being viewed
as precursor for these infections and thus, is a major public health concern—particularly as outbreaks become
more frequent and severe. However, scientific evidence describing the hazards associated with antibiotic use
is lacking due to inability to quantify the risk of these practices. One promising avenue to elucidate this risk is
to use shotgun metagenomics to identify the AMR genes in samples taken through systematic spatiotemporal
surveillance. The goal of this proposed work is to develop algorithms that will provide such a means for
analysis. The algorithms need to be scalable to very large datasets and thus, will require the development
and use succinct data structures.
 In order to achieve this goal, the investigative team will develop the theoretical foundations and applied meth-
ods needed to study AMR through the use of shotgun metagenomics. A major focus of the proposed work is
developing algorithms that can handle very large datasets. To achieve this scalability, we will create novel means
to create, compress, reconstruct and update very large de Bruijn graphs that metagenomics data in a manner
needed to study AMR. In addition, we will pioneer the study of AMR through long read data by proposing new
algorithmic problems and solutions that use data. For example, identifying the location of specific genes in a
metagenomics sample using long read data has not been proposed or studied. Thus, the algorithmic ideas and
techniques developed in this project will not only advance the study of AMR, but contribute to the growing domain
of big data analysis and pan-genomics.
 Lastly, we plan to apply our methods to samples collected from both agricultural and clinical settings in Florida.
Analysis of preliminary and new data will allow us to conclude about (1) the public risk associated with antimicro-
bial use in agriculture; (2) the effectiveness of interventions used to reduce resistant bacteria, and lastly, (3) the
factors that allow resistant bacteria to grow, thrive and evolve.
A–1

## Key facts

- **NIH application ID:** 10292979
- **Project number:** 5R01AI141810-04
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Christina Boucher
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $422,334
- **Award type:** 5
- **Project period:** 2018-11-26 → 2023-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10292979

## Citation

> US National Institutes of Health, RePORTER application 10292979, Developing Computational Methods for Surveillance of Antimicrobial Resistant Agents (5R01AI141810-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10292979. Licensed CC0.

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