Hierarchical Hidden Markov models enable accurate and diverse detection of antimicrobial resistance sequences
| Autor: | Paul S. Morley, Keith E. Belk, Zaid Abdo, Bahar Alipanahi, Christopher J Dean, Rob Raymond, Christina Boucher, Steven M. Lakin, Mattia Prosperi, Martin D. Muggli, Noelle R. Noyes, Alan Kuhnle |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
Sequencing data Medicine (miscellaneous) Microbial communities Computational biology Antimicrobial resistance Article General Biochemistry Genetics and Molecular Biology Machine Learning 03 medical and health sciences Antibiotic resistance Databases Genetic Data Mining Hidden Markov model lcsh:QH301-705.5 Gene 030304 developmental biology Sequence (medicine) 0303 health sciences 030306 microbiology High-Throughput Nucleotide Sequencing Reproducibility of Results Drug Resistance Microbial Sequence Analysis DNA Antimicrobial Markov Chains 3. Good health lcsh:Biology (General) Metagenomics Reference database General Agricultural and Biological Sciences Microbial genetics |
| Zdroj: | Communications Biology, Vol 2, Iss 1, Pp 1-11 (2019) Communications Biology |
| ISSN: | 2399-3642 |
| DOI: | 10.1038/s42003-019-0545-9 |
| Popis: | 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. Steven Lakin et al. present Meta-MARC, a computational method for identifying antimicrobial resistance sequences using DNA-based Hidden Markov Models. Because of its increased sensitivity, Meta-MARC is able to detect novel antimicrobial resistance sequences that are divergent from all known sequences. |
| Databáze: | OpenAIRE |
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