Developing insights into the mechanisms of evolution of bacterial pathogens from whole-genome sequences
Open accessAbstract
Evolution of bacterial pathogen populations has been detected in a variety of ways including phenotypic tests, such as metabolic activity, reaction to antisera and drug resistance and genotypic tests that measure variation in chromosome structure, repetitive loci and individual gene sequences. While informative, these methods only capture a small subset of the total variation and, therefore, have limited resolution. Advances in sequencing technologies have made it feasible to capture whole-genome sequence variation for each sample under study, providing the potential to detect all changes at all positions in the genome from single nucleotide changes to large-scale insertions and deletions. In this review, we focus on recent work that has applied this powerful new approach and summarize some of the advances that this has brought in our understanding of the details of how bacterial pathogens evolve.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In vitro evolution experiments coupled with whole genome sequencing have been extensively used to understand the emergence of antibiotic resistance in bacteria, but their use in the field of antifungal drug resistance is still in its infancy.
“In vitro evolution experiments coupled with whole genome sequencing have been extensively used to understand the emergence of antibiotic resistance in bacteria [149], but their use in the field of antifungal drug resistance is still in its infancy.”
From Evolutionary Emergence of Drug Resistance in Candida Opportunistic Pathogens (2018) · cites this paper as [149]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 98 · Cited by 98
- Authors
- Bryant, J., Chewapreecha, C., & Bentley, S. D.
- Affiliations
- Wellcome Sanger Institute (GB)
- University of Cambridge (GB)
- Corresponding author
- Bentley, Stephen D
- Venue
- Future Microbiology
- Publisher
- Informa UK Limited
- Published
- 2012-10-17
- Volume
- 7
- Issue
- 11
- Pages
- 1283-1296
- ISSN
- 1746-0913 (ISSN-L)
- 1746-0921
- PMC
- PMC3996552
- OpenAlex
- W2110803328
- Cited by
- 98
- References
- 98
- Citations by year
- 2025: 3 · 2024: 6 · 2023: 2 · 2022: 8 · 2021: 14 · 2020: 11 · 2019: 9 · 2018: 16 · 2017: 11 · 2016: 5 · 2015: 5 · 2014: 5 · 2013: 3
- FWCI
- 0.38
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Future Microbiology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Wellcome Trust (098051)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Life Sciences › Immunology and Microbiology › Microbiology › Microbial infections and disease research
- Keywords
- whole genome sequencing, single nucleotide polymorphisms, indels, population genomics, antimicrobial resistance, drug resistance, molecular epidemiology
- MeSH terms
- Bacteria, Bacterial Proteins, Base Sequence, Genome, Bacterial, Sequence Analysis, DNA, Evolution, Molecular, Polymorphism, Single Nucleotide
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review