ArticleNature communications2015
Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis.
Article in Nature communications, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 287 papers, 3 of them syntheses that pooled it.
What it found
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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
287 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Open-access genomic drug resistance prediction tools forJournal of clinical microbiology · 2026Pooled it
- Nanopore Sequencing for Mycobacterium tuberculosis: a Critical Review of the Literature, New Developments, and Future Opportunities.Journal of clinical microbiology · 2022Pooled it
- Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data.PLoS computational biology · 2019Pooled it
- Dietary and nutritional strategies for reducing disease risk associated with antibiotic-induced dysbiosis.Gut microbes · 2026Review
- Algorithmic Determinants of Performance Heterogeneity in Whole-Genome Sequencing-Based Prediction of Drug Resistance inMicroorganisms · 2026Review
- Phenotypic discordance in rifampicin resistance detection amongMicrobiology spectrum · 2026Review
- A lab-on-a-silicon-chip platform for all-electrical antibiotic susceptibility tests with a sample-to-results time within 20 minutes.Science advances · 2026Article
- Machine learning method for the prediction of Bedaquiline-resistantLife science alliance · 2026Article
- Prediction of antimicrobial minimum inhibitory concentration from bacterial genomes using a scalable and interpretable machine learning approach.npj antimicrobials and resistance · 2026Article
- Uncovering resistance pathways to first- and last-line antibiotics inMicrobial genomics · 2026Article
- Artificial Intelligence for Tuberculosis Screening and Detection: From Evidence to Policy and Implementation.Diagnostics (Basel, Switzerland) · 2026Review
- A tiled amplicon protocol for culture-free whole-genome sequencing ofJournal of clinical microbiology · 2026Article
- Targeted Next-Generation Sequencing in Drug-Resistant Tuberculosis: WHO Guidance and Practical Implementation Priorities.Biomedicines · 2026Review
- FroM Superstring to Indexing: a space-efficient index for unconstrainedBioinformatics advances · 2026Article
- Gene copy-number features generalize better than SNPs for antimicrobial resistance prediction in Staphylococcus aureus.npj antimicrobials and resistance · 2025Article
- An explainable artificial intelligence framework reveals mutations associated with drug resistance inBiosafety and health · 2025Article
- Harnessing machine learning to predict antibiotic susceptibility in Pseudomonas aeruginosa biofilms.NPJ biofilms and microbiomes · 2025Article
- Typhi Mykrobe: fast and accurate lineage identification and antimicrobial resistance genotyping directly from sequence reads for the typhoid fever agent Salmonella Typhi.Genome medicine · 2025Article
- Accuracy of SAM-TB Platform as a Diagnostic Assay for Detection of Mycobacterium tuberculosis Drug Resistance Versus TB-Proflier, PhyResSE, and Mykrobe: A Head-to-Head Comparison.Current microbiology · 2025Article
- Prediction of antimicrobial resistance inMicrobiology spectrum · 2025Article
227 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
28 authors.
Funding
Abstract
The rise of antibiotic-resistant bacteria has led to an urgent need for rapid detection of drug resistance in clinical samples, and improvements in global surveillance. Here we show how de Bruijn graph representation of bacterial diversity can be used to identify species and resistance profiles of clinical isolates. We implement this method for Staphylococcus aureus and Mycobacterium tuberculosis in a software package ('Mykrobe predictor') that takes raw sequence data as input, and generates a clinician-friendly report within 3 minutes on a laptop. For S. aureus, the error rates of our method are comparable to gold-standard phenotypic methods, with sensitivity/specificity of 99.1%/99.6% across 12 antibiotics (using an independent validation set, n=470). For M. tuberculosis, our method predicts resistance with sensitivity/specificity of 82.6%/98.5% (independent validation set, n=1,609); sensitivity is lower here, probably because of limited understanding of the underlying genetic mechanisms. We give evidence that minor alleles improve detection of extremely drug-resistant strains, and demonstrate feasibility of the use of emerging single-molecule nanopore sequencing techniques for these purposes.
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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.