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.
Highlights
The characterization of antimicrobial resistance genes from high-throughput sequencing data has become foundational in public health research and regulation
The removed sequence was added to the test set for that model, along with Antimicrobial resistance (AMR) sequences published in CARD between August 2016 and January 201723 (See https://github.com/lakinsm/metamarc-publication/blob/master/analytic_data/mmarc_test_set. fasta)
These additional sequences were chosen because they were not included in the construction of the Meta-MARC Hidden Markov Models (HMMs) but were annotated by AMR Class, Mechanism and Group
Summary
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. Meta-MARC has higher sensitivity relative to competing methods This sensitivity allows for detection of sequences that are divergent from known antimicrobial resistance genes. AMR in bacteria occurs either by mutation or acquisition of genes that circumvent or counteract an antimicrobial’s biological mechanism of action or decrease the concentration of antimicrobial compounds within bacterial cells The collection of these AMR genes in both pathogenic and nonpathogenic microbes is commonly referred to as the resistome and defines a population’s potential resistance to known antimicrobials. Resfams uses Hidden Markov Models (HMMs) to classify AMR-related protein sequences from high-throughput sequence data by assembling the sequence reads using a standard genome assembler, translating the resulting contigs into amino acid sequences, and classifying these translated sequences[21]
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