ArticlePLoS genetics2018
A fast and agnostic method for bacterial genome-wide association studies: Bridging the gap between k-mers and genetic events.
Article in PLoS genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 125 papers, 1 of them a synthesis that pooled it.
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Who cites it
125 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- BiTUGA: scalable prevalence-based unitig association testing for binary traits.G3 (Bethesda, Md.) · 2026Article
- Integrated Genome-Wide Association Study and Machine Learning Approach for Characterizing the Determinants of Biofilm Formation in Staphylococcus aureus.Interdisciplinary sciences, computational life sciences · 2026Article
- Skin Infection Pathogenicity Associated with a Canine Microbiome Resident: Polygenic Architecture of Virulence Factors inAntibiotics (Basel, Switzerland) · 2026Article
- META-DIFF: a k-mer-based pipeline that detects differentially abundant sequences in metagenomics whole genome sequencing.Scientific reports · 2026Article
- Prediction of antimicrobial minimum inhibitory concentration from bacterial genomes using a scalable and interpretable machine learning approach.npj antimicrobials and resistance · 2026Article
- A versatile multi-components mixed model for bacterial-Genome Wide association studies.Nature communications · 2026Article
- BaGPipe: an automated, reproducible, and flexible pipeline for bacterial genome-wide association studies.BMC microbiology · 2026Article
- Genome-wide association study of Mycoplasma anserisalpingitidis strains for antibiotic susceptibility.Scientific reports · 2026Article
- Genome co-adaptation and the evolution of methicillin resistant Staphylococcus aureus.Genome biology · 2026Article
- Large-scale comparative genomics and structure-function analysis enables characterization of known and novel genetic determinants of antimicrobial resistance in bacterial pathogens.Frontiers in microbiology · 2026Article
- Leveraging Artificial Intelligence to Advance Bioinformatics in Africa: Opportunities, Challenges, and Ethical Considerations in Combating Antimicrobial Resistance.Bioinformatics and biology insights · 2026Article
- BacT-Seq, a Nanopore-Based Whole-Genome Sequencing Workflow Prototype for Rapid and Accurate Pathogen Identification and Resistance Prediction from Positive Blood Cultures: A Feasibility Study.Diagnostics (Basel, Switzerland) · 2026Article
- Antimicrobial resistance is widespread among intestinal and extra-intestinalInfection and immunity · 2025Article
- Natural competence in the bacterial pathogen Xylella fastidiosa varies across genotypes and is associated with adhesins.PLoS pathogens · 2025Article
- Article
- Machine learning and statistical inference in microbial population genomics.Genome biology · 2025Review
- Bacterial Systematic Genetics and Integrated Multi-Omics: Beyond Static Genomics Toward Predictive Models.International journal of molecular sciences · 2025Review
- Identifying genetic variations ineLife · 2025Article
- Whole-genome phenotype prediction with machine learning: open problems in bacterial genomics.Bioinformatics (Oxford, England) · 2025Article
65 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genome-wide association study (GWAS) methods applied to bacterial genomes have shown promising results for genetic marker discovery or detailed assessment of marker effect. Recently, alignment-free methods based on k-mer composition have proven their ability to explore the accessory genome. However, they lead to redundant descriptions and results which are sometimes hard to interpret. Here we introduce DBGWAS, an extended k-mer-based GWAS method producing interpretable genetic variants associated with distinct phenotypes. Relying on compacted De Bruijn graphs (cDBG), our method gathers cDBG nodes, identified by the association model, into subgraphs defined from their neighbourhood in the initial cDBG. DBGWAS is alignment-free and only requires a set of contigs and phenotypes. In particular, it does not require prior annotation or reference genomes. It produces subgraphs representing phenotype-associated genetic variants such as local polymorphisms and mobile genetic elements (MGE). It offers a graphical framework which helps interpret GWAS results. Importantly it is also computationally efficient-experiments took one hour and a half on average. We validated our method using antibiotic resistance phenotypes for three bacterial species. DBGWAS recovered known resistance determinants such as mutations in core genes in Mycobacterium tuberculosis, and genes acquired by horizontal transfer in Staphylococcus aureus and Pseudomonas aeruginosa-along with their MGE context. It also enabled us to formulate new hypotheses involving genetic variants not yet described in the antibiotic resistance literature. An open-source tool implementing DBGWAS is available at https://gitlab.com/leoisl/dbgwas.
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