TY - JOUR T1 - MentaLiST – A fast MLST caller for large wgMLST schemes JF - bioRxiv DO - 10.1101/172858 SP - 172858 AU - Pedro Feijao AU - Hua-Ting Yao AU - Dan Fornika AU - Jennifer Gardy AU - Will Hsiao AU - Cedric Chauve AU - Leonid Chindelevitch Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/08/06/172858.abstract N2 - MLST (multi-locus sequence typing) is a classic technique for genotyping bacteria, widely applied for pathogen outbreak surveillance. Traditionally, MLST is based on identifying sequence types from a set of a small number of housekeeping genes. With the increased availability of whole-genome sequencing (WGS) data, MLST methods have evolved toward larger typing schemes, based on a few hundred genes (core genome MLST, cgMLST) to a few thousand genes (whole genome MLST, wgMLST). Such large-scale MLST schemes have been shown to provide a finer resolution and are increasingly used in various contexts such as hospital outbreaks or foodborne pathogen outbreaks. This methodological shift raises new computational challenges, especially given the large size of the schemes involved. Very few available MLST callers are currently capable of dealing with large cgMLST and wgMLST schemes.We introduce MentaLiST, a new MLST caller, based on a k-mer counting algorithm and written in the Julia language, specifically designed and implemented to handle large typing schemes. We test it on real and simulated data to show that MentaLiST is faster than any other available MLST caller while providing the same or better accuracy, and is capable of dealing with MLST scheme with up to thousands of genes while requiring limited computational resources. MentaLiST source code and easy installation instructions using a Conda package are available at https://github.com/WGS-TB/MentaLiST. ER -