4.7 Article Proceedings Paper

Will the emergence of core genome MLST end the role of in silico MLST?

期刊

FOOD MICROBIOLOGY
卷 75, 期 -, 页码 28-36

出版社

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.fm.2017.09.003

关键词

In silico MLST; cgMLST; Sequence type (ST); Next-generation sequencing; NGS

资金

  1. OECD Co-operative Research Programme on Biological Resource Management for Sustainable Agricultural Systems [TAD/CRP JA 87780]

向作者/读者索取更多资源

The technological advancement of molecular epidemiological analysis using next-generation sequencing (NGS) for foodborne pathogens has a groundbreaking impact over the past three years. In particular, the emergence of cg (core genome) multilocus sequence typing(MLST) has a significant impact. This is because this technology made it possible for many researchers to carry out molecular epidemiological analysis on foodborne pathogens in a common language, using common definitions. The resolution of core genome MLST (cgMLST) far surpasses that of MLST, which only uses seven (usually, in some cases five) housekeeping genes. Therefore, cgMLST would in no doubt terminate the role of conventional MLST as the molecular epidemiological tool. However, the role of MLST would probably not end all together. Rather, the sequence type (ST) of the conventional MLST is expected to be used as in silico MLST by a wider range of researchers than ever in the next 10 years. This is because, with the arrival of the NGS era, we have come to be able to obtain ST of conventional MLST by simply entering the NGS text file into one's own PC. In other words, acquisition of ST data is no longer limited to researchers aiming to conduct MLST for the first place. The impact of such a change is large. In silico MLST will continue to be used as a tool for understanding the broad characteristics of bacterial strains. This review aimed to summarize the main information on STs that have been accumulated for representative foodborne pathogens, in particular for potential NGS users in this new era who have been not familiar with MLST until now. (C) 2017 Elsevier Ltd. All rights reserved.

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