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Mendeley readers
| Title |
Construction of a Pan-Genome Allele Database of Salmonella enterica Serovar Enteritidis for Molecular Subtyping and Disease Cluster Identification
|
|---|---|
| Published in |
Frontiers in Microbiology, December 2016
|
| DOI | 10.3389/fmicb.2016.02010 |
| Pubmed ID | |
| Authors |
Yen-Yi Liu, Chih-Chieh Chen, Chien-Shun Chiou |
| Abstract |
We built a pan-genome allele database with 395 genomes of Salmonella enterica serovar Enteritidis and developed computer tools for analysis of whole genome sequencing (WGS) data of bacterial isolates for disease cluster identification. A web server (http://wgmlst.imst.nsysu.edu.tw) was set up with the database and the tools, allowing users to upload WGS data to generate whole genome multilocus sequence typing (wgMLST) profiles and to perform cluster analysis of wgMLST profiles. The usefulness of the database in disease cluster identification was demonstrated by analyzing a panel of genomes from 55 epidemiologically well-defined S. Enteritidis isolates provided by the Minnesota Department of Health. The wgMLST-based cluster analysis revealed distinct clades that were concordant with the epidemiologically defined outbreaks. Thus, using a common pan-genome allele database, wgMLST can be a promising WGS-based subtyping approach for disease surveillance and outbreak investigation across laboratories. |
Mendeley readers
The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 49 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 10 | 20% |
| Researcher | 9 | 18% |
| Student > Master | 6 | 12% |
| Student > Bachelor | 4 | 8% |
| Other | 3 | 6% |
| Other | 7 | 14% |
| Unknown | 10 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 12 | 24% |
| Biochemistry, Genetics and Molecular Biology | 9 | 18% |
| Immunology and Microbiology | 9 | 18% |
| Computer Science | 4 | 8% |
| Business, Management and Accounting | 2 | 4% |
| Other | 3 | 6% |
| Unknown | 10 | 20% |