ArticleNature microbiology2020
Rapid inference of antibiotic resistance and susceptibility by genomic neighbour typing.
Article in Nature microbiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
58 citing papers in PubMed.
- Molecular characterization of virulence and resistance determinants in clinical Pseudomonas aeruginosa isolates: a cross-sectional analysis of virulence-resistance associations.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- AMR-GNN: a multi-representation graph neural network framework to enable genomic antimicrobial resistance prediction.Nature communications · 2026Article
- Prediction of genetic relatedness ofAntimicrobial agents and chemotherapy · 2026Article
- FroM Superstring to Indexing: a space-efficient index for unconstrainedBioinformatics advances · 2026Article
- Biased sampling driven by bacterial population structure confounds machine learning prediction of antimicrobial resistance.PLoS biology · 2025Article
- Inference of antimicrobial resistance (AMR) from a whole genome database outperforming AMR gene detection.iScience · 2025Article
- Efficient and robust search of microbial genomes via phylogenetic compression.Nature methods · 2025Article
- Antimicrobial minimum inhibitory concentrations can be imputed from phenotypic data using a random forest approach.American journal of veterinary research · 2025Article
- Isothermal Amplification Techniques: An Emerging Tool for On-Site Detection of Phytopathogens in Field Conditions.Methods in molecular biology (Clifton, N.J.) · 2025Review
- PathoTracker: an online analytical metagenomic platform for Klebsiella pneumoniae feature identification and outbreak alerting.Communications biology · 2024Article
- Laboratory tools for the direct detection of bacterial respiratory infections and antimicrobial resistance: a scoping review.Journal of veterinary diagnostic investigation : official publication of the American Association of Veterinary Laboratory Diagnosticians, Inc · 2024Article
- Antimicrobial resistance prediction by clinical metagenomics in pediatric severe pneumonia patients.Annals of clinical microbiology and antimicrobials · 2024Article
- Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes.Briefings in bioinformatics · 2024Article
- Optimising machine learning prediction of minimum inhibitory concentrations inMicrobial genomics · 2024Article
- Applicability of Bronchoalveolar Lavage Fluid and Plasma Metagenomic Next-Generation Sequencing Assays in the Diagnosis of Pneumonia.Open forum infectious diseases · 2024Article
- Time Required for Nanopore Whole-Genome Sequencing of Neisseria gonorrhoeae for Identification of Phylogenetic Relationships.The Journal of infectious diseases · 2023Article
- Predicting variable gene content inmSystems · 2023Article
- Real-time genomics for One Health.Molecular systems biology · 2023Review
- The diagnostic utility of nanopore targeted sequencing in suspected endophthalmitis.International ophthalmology · 2023Article
- Rapid metagenomic sequencing for diagnosis and antimicrobial sensitivity prediction of canine bacterial infections.Microbial genomics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Surveillance of drug-resistant bacteria is essential for healthcare providers to deliver effective empirical antibiotic therapy. However, traditional molecular epidemiology does not typically occur on a timescale that could affect patient treatment and outcomes. Here, we present a method called 'genomic neighbour typing' for inferring the phenotype of a bacterial sample by identifying its closest relatives in a database of genomes with metadata. We show that this technique can infer antibiotic susceptibility and resistance for both Streptococcus pneumoniae and Neisseria gonorrhoeae. We implemented this with rapid k-mer matching, which, when used on Oxford Nanopore MinION data, can run in real time. This resulted in the determination of resistance within 10 min (91% sensitivity and 100% specificity for S. pneumoniae and 81% sensitivity and 100% specificity for N. gonorrhoeae from isolates with a representative database) of starting sequencing, and within 4 h of sample collection (75% sensitivity and 100% specificity for S. pneumoniae) for clinical metagenomic sputum samples. This flexible approach has wide application for pathogen surveillance and may be used to greatly accelerate appropriate empirical antibiotic treatment.
Indexed as
Identifiers
What OpenQuestion holds
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.