Comparison Of Multi-locus Sequence Typing software For next generation sequencing data

Page, Andrew J.; Alikhan, Nabil-Fareed; Carleton, Heather A.; Seemann, Torsten; Keane, Jacqueline A.; Katz, Lee S.

doi:10.1101/117770

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…However, the running time of ARIBA was approximately one-fifth that of SRST2 (Table S7). See Page et al [ 39 ] for an in-depth comparison of current MLST tools, including SRST2 and ARIBA.…”

Section: Resultsmentioning

confidence: 99%

ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads

et al. 2017

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Antimicrobial resistance (AMR) is one of the major threats to human and animal health worldwide, yet few high-throughput tools exist to analyse and predict the resistance of a bacterial isolate from sequencing data. Here we present a new tool, ARIBA, that identifies AMR-associated genes and single nucleotide polymorphisms directly from short reads, and generates detailed and customizable output. The accuracy and advantages of ARIBA over other tools are demonstrated on three datasets from Gram-positive and Gram-negative bacteria, with ARIBA outperforming existing methods.

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“…However, the running time of ARIBA was approximately one-fifth that of SRST2 (Table S7). See Page et al [ 39 ] for an in-depth comparison of current MLST tools, including SRST2 and ARIBA.…”

Section: Resultsmentioning

confidence: 99%

ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads

et al. 2017

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“…Terminology. An MLST scheme is composed of a set of loci together with a database of known alleles for each locus [16]. An allele distribution for a given locus is a set of alleles for this locus together with a proportion assigned to each allele; the proportions must be non-negative and add up to 1.…”

Section: Methodsmentioning

confidence: 99%

Deconvoluting the diversity of within-host pathogen strains in a multi-locus sequence typing framework

et al. 2019

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BackgroundBacterial pathogens exhibit an impressive amount of genomic diversity. This diversity can be informative of evolutionary adaptations, host-pathogen interactions, and disease transmission patterns. However, capturing this diversity directly from biological samples is challenging.ResultsWe introduce a framework for understanding the within-host diversity of a pathogen using multi-locus sequence types (MLST) from whole-genome sequencing (WGS) data. Our approach consists of two stages. First we process each sample individually by assigning it, for each locus in the MLST scheme, a set of alleles and a proportion for each allele. Next, we associate to each sample a set of strain types using the alleles and the strain proportions obtained in the first step. We achieve this by using the smallest possible number of previously unobserved strains across all samples, while using those unobserved strains which are as close to the observed ones as possible, at the same time respecting the allele proportions as closely as possible. We solve both problems using mixed integer linear programming (MILP). Our method performs accurately on simulated data and generates results on a real data set of Borrelia burgdorferi genomes suggesting a high level of diversity for this pathogen.ConclusionsOur approach can apply to any bacterial pathogen with an MLST scheme, even though we developed it with Borrelia burgdorferi, the etiological agent of Lyme disease, in mind. Our work paves the way for robust strain typing in the presence of within-host heterogeneity, overcoming an essential challenge currently not addressed by any existing methodology for pathogen genomics.

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MentaLiST – A fast MLST caller for large MLST schemes

Feijão

Yao

Fornika

et al. 2017

Preprint

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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 small number of housekeeping genes. With the increasing 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 MLST schemes.We introduce MentaLiST , a new MLST caller, based on a k-mer voting 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.

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Comparison Of Multi-locus Sequence Typing software For next generation sequencing data

Cited by 3 publications

References 30 publications

ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads

ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads

Deconvoluting the diversity of within-host pathogen strains in a multi-locus sequence typing framework

MentaLiST – A fast MLST caller for large MLST schemes

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