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 journalArticlepeer-review

15 Scopus citations


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
Issue number1
StatePublished - Dec 1 2019

Bibliographical note

Funding Information:
A.K., B.A., N.R.N., M.P., and C.B. were funded by National Institutes of Health (NIH) National Institute of Allergy and Infectious Diseases (NIAID) Institute (Project No. 1R01AI141810-01, PIs: Boucher and Prosperi). In addition, S.L. was was partially supported by a National Science Foundation (NSF) (Grant No. DGE-1450032; PI: Chen). Any opinions, findings, conclusions, or recommendations expressed are those of the author(s) and do not necessarily reflect the views of the NSF or the NIH.

Publisher Copyright:
© 2019, The Author(s).


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