ArticleCommunications biology2019
Hierarchical Hidden Markov models enable accurate and diverse detection of antimicrobial resistance sequences.
Article in Communications biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Approaches for Microorganism Identification, Virulence Assessment, and Antimicrobial Susceptibility Evaluation Using DNA Sequencing Methods: A Systematic Review.Molecular biotechnology · 2025Pooled it
- Predicting the plastic biodegradation potential within microbial lineages and across global ecosystems.Microbial genomics · 2026Article
- Vibrio Community Structure Shapes the Diversity of Carbenicillin-Hydrolysing Class A β-Lactamase Circulating in European Coastal Environments.Environmental microbiology · 2026Article
- resLens: genomic language models to enhance antibiotic resistance gene detection.npj antimicrobials and resistance · 2026Article
- Pathogenomic Insights intoLife (Basel, Switzerland) · 2024Article
- Artificial intelligence tools for the identification of antibiotic resistance genes.Frontiers in microbiology · 2024Review
- Silicon versus Superbug: Assessing Machine Learning's Role in the Fight against Antimicrobial Resistance.Antibiotics (Basel, Switzerland) · 2023Review
- Article
- MEGARes and AMR++, v3.0: an updated comprehensive database of antimicrobial resistance determinants and an improved software pipeline for classification using high-throughput sequencing.Nucleic acids research · 2023Article
- TheFrontiers in microbiology · 2023Article
- Machine learning algorithm to characterize antimicrobial resistance associated with the International Space Station surface microbiome.Microbiome · 2022Article
- AMR-meta: a k-mer and metafeature approach to classify antimicrobial resistance from high-throughput short-read metagenomics data.GigaScience · 2022Article
- Responses of the Soil Bacterial Community, Resistome, and Mobilome to a Decade of Annual Exposure to Macrolide Antibiotics.Applied and environmental microbiology · 2022Article
- Towards routine employment of computational tools for antimicrobial resistance determination via high-throughput sequencing.Briefings in bioinformatics · 2022Article
- BacARscan: anBiology methods & protocols · 2022Article
- Metagenomic-based surveillance systems for antibiotic resistance in non-clinical settings.Frontiers in microbiology · 2022Review
- β-LacFamPred: An online tool for prediction and classification of β-lactamase class, subclass, and family.Frontiers in microbiology · 2022Article
- KARGAMobile: Android app for portable, real-time, easily interpretable analysis of antibiotic resistance genesFrontiers in bioengineering and biotechnology · 2022Article
- A review: antimicrobial resistance data mining models and prediction methods study for pathogenic bacteria.The Journal of antibiotics · 2021Review
- KARGA: Multi-platform Toolkit for... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2021Article
Corrections and comments
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Authors and funding
12 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.