ArticleJournal of clinical microbiology2018
Accuracy of Different Bioinformatics Methods in Detecting Antibiotic Resistance and Virulence Factors from Staphylococcus aureus Whole-Genome Sequences.
Article in Journal of clinical microbiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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
44 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Artificial Intelligence and Bioengineering Approaches for Antimicrobial Resistance Prediction.Medicina (Kaunas, Lithuania) · 2026Review
- Review
- AMRnet: a data visualization platform to interactively explore pathogen variants and antimicrobial resistance.Nucleic acids research · 2026Article
- Comparative genomic analysis ofFrontiers in public health · 2026Article
- Genetic Characterization ofToxins · 2025Article
- Genomic and phenotypic characterization of staphylococci isolated from the skin of non-human primates.Microbiology (Reading, England) · 2025Article
- Detecting antibiotic resistance: classical, molecular, advanced bioengineering, and AI-enhanced approaches.Frontiers in microbiology · 2025Review
- From Data to Decisions: Leveraging Artificial Intelligence and Machine Learning in Combating Antimicrobial Resistance - a Comprehensive Review.Journal of medical systems · 2024Review
- Antimicrobial Resistance Genes in Respiratory Bacteria from Weaned Dairy Heifers.Pathogens (Basel, Switzerland) · 2024Article
- Historic methicillin-resistant Staphylococcus aureus: expanding current knowledge using molecular epidemiological characterization of a Swiss legacy collection.Genome medicine · 2024Article
- Current and Future Technologies for the Detection of Antibiotic-Resistant Bacteria.Diagnostics (Basel, Switzerland) · 2023Review
- Identification of Glucocorticoid Receptor Target Genes That Potentially Inhibit Collagen Synthesis in Human Dermal Fibroblasts.Biomolecules · 2023Article
- Advantages of Whole Genome Sequencing in Mitigating theMicroorganisms · 2023Article
- Benchmarking of two bioinformatic workflows for the analysis of whole-genome sequenced Staphylococcus aureus collected from patients with suspected sepsis.BMC infectious diseases · 2023Observational
- Next Generation and Other Sequencing Technologies in Diagnostic Microbiology and Infectious Diseases.Genes · 2022Review
- A Practical Approach for Predicting Antimicrobial Phenotype Resistance inFrontiers in microbiology · 2022Article
- One Day in Denmark: Comparison of Phenotypic and Genotypic Antimicrobial Susceptibility Testing in Bacterial Isolates From Clinical Settings.Frontiers in microbiology · 2022Article
- Genomic characterization and assessment of pathogenic potential ofFrontiers in microbiology · 2022Article
- The Genetic Relatedness and Antimicrobial Resistance Patterns of Mastitis-CausingVeterinary sciences · 2021Article
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
In principle, whole-genome sequencing (WGS) can predict phenotypic resistance directly from a genotype, replacing laboratory-based tests. However, the contribution of different bioinformatics methods to genotype-phenotype discrepancies has not been systematically explored to date. We compared three WGS-based bioinformatics methods (Genefinder [read based], Mykrobe [de Bruijn graph based], and Typewriter [BLAST based]) for predicting the presence/absence of 83 different resistance determinants and virulence genes and overall antimicrobial susceptibility in 1,379
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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.