MentaLiST -- The MLST pipeline developed by the PathOGiST research group
MLST (multi-locus sequence typing) is a classic technique for genotyping bacteria, widely applied for pathogen outbreak surveillance. Traditionally, MLST is based on identifying strain 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. Tests 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 schema with up to thousands of genes while requiring limited computational resources.
There are two ways to install MentaLiST. The simplest way is with conda, which installs Julia and Python environments with all the necessary requirements automatically. Currently, this is only available for Linux. For macOS, please follow the manual installation.
The other way is by manually downloading and installing Julia, and the required libraries.
The MentaLiST conda package is maintained as a bioconda recipe
**Note: We've received reports that the current conda builds of MentaLiST are broken. MentaLiST was recently updated to run on Julia 1.1, so these builds are quite out-of-date in respect with the current GitHub version. Until this is resolved, we recommend installing MentaLiST with the manual installation, as described below. **
The easiest way of installing MentaLiST is by creating a new environment with Conda.
Ensure that you have both the conda-forge and bioconda channels enabled:
conda config --show channels
channels:
- conda-forge
- bioconda
- defaults
To create a new conda environment called mentalist that includes MentaLiST, run:
conda create -n mentalist mentalist
then activate it by running:
source activate mentalist
Once the mentalist conda environment is active, you should be able to run MentaLiST. Typing:
mentalist -h
produces the help output.
The conda environment can be deactivated by running:
source deactivate
MentaLiST is written in Julia, and currently runs on versions >= 1.0. If you don't have it already, install an official Julia-1.1 binary or more recent version.
Then, clone this repo to obtain the MentaLiST source. After that, run Julia-1.1 and install the required packages listed in the REQUIRE file. For macOS, you'll have to drag the Julia-1.1.app bundle from the .dmg file into your Applications folder, and Launch the app.
You can also add /Applications/Julia-1.1.app/Contents/Resources/julia/bin/julia to your PATH
julia
julia> using Pkg
julia> Pkg.add("Distributed")
julia> Pkg.add("ArgParse")
julia> Pkg.add("BioSequences")
julia> Pkg.add("JSON")
julia> Pkg.add("DataStructures")
julia> Pkg.add("JLD")
julia> Pkg.add("GZip")
julia> Pkg.add("Blosc")
julia> Pkg.add("FileIO")
julia> Pkg.add("TextWrap")
julia> Pkg.add("LightXML")
If you want to use the allele coverage method (-c option), useful for compressing very large wgMLST schemes when building a new MLST database, you will need a copy of Gurobi ILP solver, and also the Gurobi and JuMP julia packages. Please check https://github.com/JuliaOpt/Gurobi.jl for installation instructions.
After installing the dependencies, MentaLiST can be run directly from the repository: src/mentalist -h.
A notebook with basic commands to install MLST schema and running the allele calling algorithm can be found here.
You can also read more detailed instruction on how to deal with novel alleles.