MentaLiST – A fast MLST caller for large MLST schemes

• DOI: 10.1099/mgen.0.000146
• Publication Date: 2018-01-10T16:12:39Z
• Authors: Pedro Feijao; Hua-Ting Yao; Dan Fornika; Jennifer Gardy; William Hsiao; Cedric Chauve; Leonid Chindelevitch
• Keywords: Multilocus sequence typing; Housekeeping gene

MLST (multi-locus sequence typing) is a classic technique for genotyping bacteria, widely applied pathogen outbreak surveillance. Traditionally, based on identifying types from small number of housekeeping genes. With the increasing availability whole-genome sequencing data, methods have evolved towards larger typing schemes, few hundred genes [core genome (cgMLST)] to thousand [whole (wgMLST)]. Such large-scale schemes been shown provide finer resolution and are increasingly used in various contexts such as hospital outbreaks or foodborne outbreaks. This methodological shift raises new computational challenges, especially given large size involved. Very available callers currently capable dealing with schemes. We introduce MentaLiST, caller, k-mer voting algorithm written Julia language, specifically designed implemented handle test it real simulated data show that MentaLiST faster than any other caller while providing same better accuracy, up thousands requiring limited resources. source code easy installation instructions using Conda package at https://github.com/WGS-TB/MentaLiST.