Title |
High resolution clustering of Salmonella enterica serovar Montevideo strains using a next-generation sequencing approach
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Published in |
BMC Genomics, January 2012
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DOI | 10.1186/1471-2164-13-32 |
Pubmed ID | |
Authors |
Marc W Allard, Yan Luo, Errol Strain, Cong Li, Christine E Keys, Insook Son, Robert Stones, Steven M Musser, Eric W Brown |
Abstract |
Next-Generation Sequencing (NGS) is increasingly being used as a molecular epidemiologic tool for discerning ancestry and traceback of the most complicated, difficult to resolve bacterial pathogens. Making a linkage between possible food sources and clinical isolates requires distinguishing the suspected pathogen from an environmental background and placing the variation observed into the wider context of variation occurring within a serovar and among other closely related foodborne pathogens. Equally important is the need to validate these high resolution molecular tools for use in molecular epidemiologic traceback. Such efforts include the examination of strain cluster stability as well as the cumulative genetic effects of sub-culturing on these clusters. Numerous isolates of S. Montevideo were shot-gun sequenced including diverse lineage representatives as well as numerous replicate clones to determine how much variability is due to bias, sequencing error, and or the culturing of isolates. All new draft genomes were compared to 34 S. Montevideo isolates previously published during an NGS-based molecular epidemiological case study. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Unknown | 2 | 100% |
Demographic breakdown
Type | Count | As % |
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Scientists | 1 | 50% |
Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 2% |
Brazil | 3 | 2% |
Sweden | 2 | 2% |
Denmark | 2 | 2% |
Uruguay | 1 | <1% |
Australia | 1 | <1% |
Unknown | 121 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 33 | 25% |
Student > Ph. D. Student | 30 | 23% |
Student > Master | 19 | 14% |
Student > Doctoral Student | 9 | 7% |
Student > Bachelor | 7 | 5% |
Other | 18 | 14% |
Unknown | 17 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 70 | 53% |
Biochemistry, Genetics and Molecular Biology | 15 | 11% |
Immunology and Microbiology | 8 | 6% |
Veterinary Science and Veterinary Medicine | 6 | 5% |
Computer Science | 3 | 2% |
Other | 10 | 8% |
Unknown | 21 | 16% |