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Commun Biol. 2019 Aug 6;2:294. doi: 10.1038/s42003-019-0545-9. eCollection 2019.

Hierarchical Hidden Markov models enable accurate and diverse detection of antimicrobial resistance sequences.

Author information

1
1Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO 80523 USA.
2
2Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611 USA.
3
3Department of Veterinary Population Medicine, University of Minnesota, St. Paul, MN 55108 USA.
4
4Department of Computer Science, Colorado State University, Fort Collins, CO 80523 USA.
5
5Department of Epidemiology, University of Florida, Gainesville, FL 32611 USA.
6
6Department of Animal Sciences, Colorado State University, Fort Collins, CO 80523 USA.
7
7VERO Center, Texas A&M University and West Texas A&M University, Canyon, TX 79016 USA.

Abstract

The characterization of antimicrobial resistance genes from high-throughput sequencing data has become foundational in public health research and regulation. This requires mapping sequence reads to databases of known antimicrobial resistance genes to determine the genes present in the sample. Mapping sequence reads to known genes is traditionally accomplished using alignment. Alignment methods have high specificity but are limited in their ability to detect sequences that are divergent from the reference database, which can result in a substantial false negative rate. We address this shortcoming through the creation of Meta-MARC, which enables detection of diverse resistance sequences using hierarchical, DNA-based Hidden Markov Models. We first describe Meta-MARC and then demonstrate its efficacy on simulated and functional metagenomic datasets. Meta-MARC has higher sensitivity relative to competing methods. This sensitivity allows for detection of sequences that are divergent from known antimicrobial resistance genes. This functionality is imperative to expanding existing antimicrobial gene databases.

KEYWORDS:

Antimicrobial resistance; Machine learning; Microbial communities; Microbial genetics

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