ArticleBMC genomics2014
GWAMAR: genome-wide assessment of mutations associated with drug resistance in bacteria.
Article in BMC genomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Computational Genomics for Resistome Characterization: Current Advancements and Future Challenges Under a One Health Perspective.Antibiotics (Basel, Switzerland) · 2026Review
- Genome-wide association study of Mycoplasma anserisalpingitidis strains for antibiotic susceptibility.Scientific reports · 2026Article
- Machine learning and phylogenetic analysis allow for predicting antibiotic resistance in M. tuberculosis.BMC microbiology · 2023Article
- Genome-Wide Association Studies (GWAS) Approaches for the Detection of Genetic Variants Associated with Antibiotic Resistance: A Systematic Review.Microorganisms · 2023Review
- Antimicrobial resistance genetic factor identification from whole-genome sequence data using deep feature selection.BMC bioinformatics · 2019Article
- Current Affairs of Microbial Genome-Wide Association Studies: Approaches, Bottlenecks and Analytical Pitfalls.Frontiers in microbiology · 2019Review
- Characterization of Mutations Conferring Resistance to Rifampin in Mycobacterium tuberculosis Clinical Strains.Antimicrobial agents and chemotherapy · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDevelopment of drug resistance in bacteria causes antibiotic therapies to be less effective and more costly. Moreover, our understanding of the process remains incomplete. One promising approach to improve our understanding of how resistance is being acquired is to use whole-genome comparative approaches for detection of drug resistance-associated mutations.
resultsWe present GWAMAR, a tool we have developed for detecting of drug resistance-associated mutations in bacteria through comparative analysis of whole-genome sequences. The pipeline of GWAMAR comprises several steps. First, for a set of closely related bacterial genomes, it employs eCAMBer to identify homologous gene families. Second, based on multiple alignments of the gene families, it identifies mutations among the strains of interest. Third, it calculates several statistics to identify which mutations are the most associated with drug resistance.
conclusionsBased on our analysis of two large datasets retrieved from publicly available data for M. tuberculosis, we identified a set of novel putative drug resistance-associated mutations. As a part of this work, we present also an application of our tool to detect putative compensatory mutations.
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