ArticlePLoS computational biology2020
Robust detection of point mutations involved in multidrug-resistant Mycobacterium tuberculosis in the presence of co-occurrent resistance markers.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 22 citations in OpenAlex.
- Whole-Genome and Targeted Sequencing of 75 Drug-ResistantAnnals of laboratory medicine · 2026Article
- Generative AI in microbial evolution and resistance: toward robust, explainable, and equitable predictions.Frontiers in microbiology · 2025Article
- Feature selection and aggregation for antibiotic resistance GWAS inFrontiers in microbiology · 2025Article
- Leveraging large-scale Mycobacterium tuberculosis whole genome sequence data to characterise drug-resistant mutations using machine learning and statistical approaches.Scientific reports · 2024Article
- Genome-Wide Association Studies (GWAS) Approaches for the Detection of Genetic Variants Associated with Antibiotic Resistance: A Systematic Review.Microorganisms · 2023Review
- Machine Learning of the Whole Genome Sequence ofMicroorganisms · 2023Review
- The Detection of Mutations and Genotyping of Drug-ResistantInfectious disease reports · 2023Article
- Feature weighted models to address lineage dependency in drug-resistance prediction from Mycobacterium tuberculosis genome sequences.Bioinformatics (Oxford, England) · 2023Article
- Large-scale genomic analysis of Mycobacterium tuberculosis reveals extent of target and compensatory mutations linked to multi-drug resistant tuberculosis.Scientific reports · 2023Article
- Geographical classification of malaria parasites through applying machine learning to whole genome sequence data.Scientific reports · 2022Article
- Characterisation of drug-resistant Mycobacterium tuberculosis mutations and transmission in Pakistan.Scientific reports · 2022Article
- A modified decision tree approach to improve the prediction and mutation discovery for drug resistance in Mycobacterium tuberculosis.BMC genomics · 2022Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
Tuberculosis disease is a major global public health concern and the growing prevalence of drug-resistant Mycobacterium tuberculosis is making disease control more difficult. However, the increasing application of whole-genome sequencing as a diagnostic tool is leading to the profiling of drug resistance to inform clinical practice and treatment decision making. Computational approaches for identifying established and novel resistance-conferring mutations in genomic data include genome-wide association study (GWAS) methodologies, tests for convergent evolution and machine learning techniques. These methods may be confounded by extensive co-occurrent resistance, where statistical models for a drug include unrelated mutations known to be causing resistance to other drugs. Here, we introduce a novel 'cannibalistic' elimination algorithm ("Hungry, Hungry SNPos") that attempts to remove these co-occurrent resistant variants. Using an M. tuberculosis genomic dataset for the virulent Beijing strain-type (n = 3,574) with phenotypic resistance data across five drugs (isoniazid, rifampicin, ethambutol, pyrazinamide, and streptomycin), we demonstrate that this new approach is considerably more robust than traditional methods and detects resistance-associated variants too rare to be likely picked up by correlation-based techniques like GWAS.
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Registered trials
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