Title |
Short read sequence typing (SRST): multi-locus sequence types from short reads
|
---|---|
Published in |
BMC Genomics, July 2012
|
DOI | 10.1186/1471-2164-13-338 |
Pubmed ID | |
Authors |
Michael Inouye, Thomas C Conway, Justin Zobel, Kathryn E Holt |
Abstract |
Multi-locus sequence typing (MLST) has become the gold standard for population analyses of bacterial pathogens. This method focuses on the sequences of a small number of loci (usually seven) to divide the population and is simple, robust and facilitates comparison of results between laboratories and over time. Over the last decade, researchers and population health specialists have invested substantial effort in building up public MLST databases for nearly 100 different bacterial species, and these databases contain a wealth of important information linked to MLST sequence types such as time and place of isolation, host or niche, serotype and even clinical or drug resistance profiles. Recent advances in sequencing technology mean it is increasingly feasible to perform bacterial population analysis at the whole genome level. This offers massive gains in resolving power and genetic profiling compared to MLST, and will eventually replace MLST for bacterial typing and population analysis. However given the wealth of data currently available in MLST databases, it is crucial to maintain backwards compatibility with MLST schemes so that new genome analyses can be understood in their proper historical context. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 14% |
Unknown | 6 | 86% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 4 | 57% |
Members of the public | 2 | 29% |
Practitioners (doctors, other healthcare professionals) | 1 | 14% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 6 | 4% |
Australia | 3 | 2% |
Spain | 3 | 2% |
Denmark | 2 | 1% |
United Kingdom | 1 | <1% |
Canada | 1 | <1% |
Sweden | 1 | <1% |
Belgium | 1 | <1% |
Germany | 1 | <1% |
Other | 2 | 1% |
Unknown | 138 | 87% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 47 | 30% |
Student > Ph. D. Student | 46 | 29% |
Student > Master | 16 | 10% |
Professor > Associate Professor | 8 | 5% |
Student > Bachelor | 7 | 4% |
Other | 22 | 14% |
Unknown | 13 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 83 | 52% |
Medicine and Dentistry | 15 | 9% |
Biochemistry, Genetics and Molecular Biology | 14 | 9% |
Immunology and Microbiology | 6 | 4% |
Business, Management and Accounting | 4 | 3% |
Other | 15 | 9% |
Unknown | 22 | 14% |