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

Steven M. Lakin, Alan Kuhnle, Bahar Alipanahi, Noelle R. Noyes, Chris Dean, Martin Muggli, Rob Raymond, Zaid Abdo, Mattia Prosperi, Keith E. Belk, Paul S. Morley, Christina Boucher

Research output: Contribution to journalArticle

3 Scopus citations

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.

Original languageEnglish (US)
Article number294
JournalCommunications biology
Volume2
Issue number1
DOIs
StatePublished - Dec 1 2019

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

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    Lakin, S. M., Kuhnle, A., Alipanahi, B., Noyes, N. R., Dean, C., Muggli, M., Raymond, R., Abdo, Z., Prosperi, M., Belk, K. E., Morley, P. S., & Boucher, C. (2019). Hierarchical Hidden Markov models enable accurate and diverse detection of antimicrobial resistance sequences. Communications biology, 2(1), [294]. https://doi.org/10.1038/s42003-019-0545-9