ArticleBMC genomics2012
An approach to identifying drug resistance associated mutations in bacterial strains.
Article in BMC genomics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
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Who cites it
6 citing papers in PubMed, 16 citations in OpenAlex.
- Identifying genetic determinants of complex phenotypes from whole genome sequence data.BMC genomics · 2019Article
- The ribosomal S10 protein is a general target for decreased tigecycline susceptibility.Antimicrobial agents and chemotherapy · 2015Article
- General Overview on Nontuberculous Mycobacteria, Biofilms, and Human Infection.Journal of pathogens · 2015Review
- eCAMBer: efficient support for large-scale comparative analysis of multiple bacterial strains.BMC bioinformatics · 2014Article
- GWAMAR: genome-wide assessment of mutations associated with drug resistance in bacteria.BMC genomics · 2014Article
- Advances in translational bioinformatics and population genomics in the Asia-Pacific.BMC genomics · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDrug resistance in bacterial pathogens is an increasing problem, which stimulates research. However, our understanding of drug resistance mechanisms remains incomplete. Fortunately, the fast-growing number of fully sequenced bacterial strains now enables us to develop new methods to identify mutations associated with drug resistance.
resultsWe present a new comparative approach to identify genes and mutations that are likely to be associated with drug resistance mechanisms. In order to test the approach, we collected genotype and phenotype data of 100 fully sequenced strains of S. aureus and 10 commonly used drugs. Then, applying the method, we re-discovered the most common genetic determinants of drug resistance and identified some novel putative associations.
conclusionsFirstly, the collected data may help other researchers to develop and verify similar techniques. Secondly, the proposed method is successful in identifying drug resistance determinants. Thirdly, the in-silico identified genetic mutations, which are putatively involved in drug resistance mechanisms, may increase our understanding of the drug resistance mechanisms.
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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.