Multilocus sequence typing (MLST) is a technique in molecular biology for the typing of multiple loci, using DNA sequences of internal fragments of multiple housekeeping genes to characterize isolates of microbial species. The first MLST scheme to be developed was for Neisseria meningitidis, the causative agent of meningococcal meningitis and septicaemia. Since its introduction for the research of evolutionary history, MLST has been used not only for human pathogens but also for plant pathogens. MLST directly measures the DNA sequence variations in a set of housekeeping genes and characterizes strains by their unique allelic profiles. The principle of MLST is simple: the technique involves PCR amplification followed by DNA sequencing. Nucleotide differences between strains can be checked at a variable number of genes depending on the degree of discrimination desired. The workflow of MLST involves: 1) data collection, 2) data analysis and 3) multilocus sequence analysis. In the data collection step, definitive identification of variation is obtained by nucleotide sequence determination of gene fragments. In the data analysis step, all unique sequences are assigned allele numbers and combined into an allelic profile and assigned a sequence type (ST). If new alleles and STs are found, they are stored in the database after verification. In the final analysis step of MLST, the relatedness of isolates are made by comparing allelic profiles. Researchers do epidemiological and phylogenetical studies by comparing STs of different clonal complexes. A huge set of data is produced during the sequencing and identification process so bioinformatic techniques are used to arrange, manage, analyze and merge all of the biological data. To strike the balance between the acceptable identification power, time and cost for the strain typing, about seven to eight house-keeping genes are commonly used in the laboratories. Quoting Staphylococcus aureus as an example, seven housekeeping genes are used in MLST typing.

About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.