MentaLiST – A fast MLST caller for large MLST schemes

Feijão, Pedro; Yao, Hua-Ting; Fornika, Daniel; Gardy, Jennifer L.; Hsiao, William; Chauve, Cédric; Chindelevitch, Leonid

doi:10.1099/mgen.0.000146

Cited by 53 publications

(58 citation statements)

References 39 publications

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“…To measure the performance of our application on the traditional seven loci MLST analysis, we compared STing (v0.24.2) in two execution modes, fast and sensitive, along with six applications able to perform sequence typing (stringMLST 4 , MentaLiST 11 , Kestrel 12 , SRST2 13 , ARIBA 14 , and Offline CGE/DTU; Supplementary Table 3). These applications can be classified into five groups depending on the strategy (algorithmic paradigm) used to predict the sequence types of whole genome sequencing data samples from bacterial isolates: k -mer, k -mer plus alignment, mapping, mapping plus local assembly, and assembly (Supplementary Table 3).…”

Section: Methodsmentioning

confidence: 99%

STing: accurate and ultrafast genomic profiling with exact sequence matches

Espitia-Navarro

Chande

Nagar

et al. 2019

Preprint

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17Genome-enabled approaches to molecular epidemiology have become essential to public health agencies 18 and the microbial research community. We developed the algorithm STing to provide turn-key solutions 19for molecular typing and gene detection directly from next-generation sequence data of microbial 20 pathogens. Our implementation of STing uses an innovative k-mer search strategy that eliminates the 21 computational overhead associated with the time consuming steps of quality control, assembly, and 22

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Section: Methodsmentioning

confidence: 99%

STing: accurate and ultrafast genomic profiling with exact sequence matches

Espitia-Navarro

Chande

Nagar

et al. 2019

Preprint

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“…The MentaLiST pipeline generates MLST allelic calls directly from read data, avoiding slow and computationally expensive genome assembly. MentaLiST's allele calling concordance with other popular genome-scale MLST programs is greater than 99% (36).…”

Section: Irida Performs Analyses Of Sequence Data and Metadata With Vmentioning

confidence: 99%

“…IRIDA facilitates cgMLST analysis through the integration of MentaLiST, a fast k-mer based MLST and cgMLST calculation engine enabling genotyping of bacterial samples directly from read data (36). MentaLiST's ability to call alleles directly from raw sequence reads bypasses time-consuming assembly, and is specifically designed and implemented to handle large typing schemes (i.e.…”

Section: Multi-locus Sequence Typingmentioning

confidence: 99%

The Integrated Rapid Infectious Disease Analysis (IRIDA) Platform

Fr²,

Ej³

et al. 2018

Preprint

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Whole genome sequencing (WGS) is a powerful tool for public health infectious disease investigations owing to its higher resolution, greater efficiency, and cost-effectiveness over traditional genotyping methods. Implementation of WGS in routine public health microbiology laboratories is impeded by a lack of user-friendly automated and semi-automated pipelines, restrictive jurisdictional data sharing policies, and the proliferation of non-interoperable analytical and reporting systems. To address these issues, we developed the Integrated Rapid Infectious Disease Analysis (IRIDA) platform (irida.ca), a user-friendly, decentralized, open-source bioinformatics and analytical web platform to support real-time infectious disease outbreak investigations using WGS data. Instances can be independently installed on local highperformance computing infrastructure, enabling private and secure data management and analyses according to organizational policies and governance. IRIDA's data management capabilities enable secure upload, storage and sharing of all WGS data and metadata. The core platform currently includes pipelines for quality control, assembly, annotation, variant detection, phylogenetic analysis, in silico serotyping, multi-locus sequence typing, and genome distance calculation. Analysis pipeline results can be visualized within the platform through dynamic line lists and integrated phylogenomic clustering for research and discovery, and for enhancing decision-making support and hypothesis generation in epidemiological investigations. Communication and data exchange between instances are provided through customizable access controls. IRIDA complements centralized systems, empowering local analytics and visualizations for genomics-based microbial pathogen investigations. IRIDA is currently transforming the Canadian public health ecosystem and is freely available at https://github.com/phac-nml/irida and www.irida.ca. Impact StatementWhole genome sequencing (WGS) is revolutionizing infectious disease analysis and surveillance due to its cost effectiveness, utility, and improved analytical power. To date, no . CC-BY-NC-ND 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/381830 doi: bioRxiv preprint first posted online Jul. 31, 2018; 3 "one-size-fits-all" genomics platform has been universally adopted, owing to differences in national (and regional) health information systems, data sharing policies, computational infrastructures, lack of interoperability and prohibitive costs. The Integrated Rapid Infectious Disease Analysis (IRIDA) platform is a user-friendly, decentralized, open-source bioinformatics and analytical web platform developed to support real-time infectious disease outbreak investigations using WGS data. IRIDA empowers public health, regulatory and clinical microbiology laboratory personnel to bett...

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“…As a result, they have proven to be valuable typing methods in many studies, and they are becoming standard approaches for pathogen surveillance [7]. In this study, we used the recently published MentaLiST [9], an in-house k-mer based MLST caller designed specifically for handling large MLST schemes.…”

Section: Multilocus Sequence Typingmentioning

confidence: 99%

PathOGiST: A Novel Method for Clustering Pathogen Isolates by Combining Multiple Genotyping Signals

Katebi

Feijão

Booth

et al. 2020

Algorithms for Computational Biology

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In this work, we study the problem of clustering bacterial isolates into epidemiologically related groups from next-generation sequencing data. Existing methods for this problem mainly use a single genotyping signal, and either use a distance-based method with a pre-specified number of clusters, or a phylogenetic tree-based method with a pre-specified threshold.We propose PathOGiST, an open-source algorithmic framework for clustering bacterial isolates by leveraging multiple genotypic signals and calibrated thresholds. PathOGiST uses different genotypic signals, clusters the isolates based on these individual signals with correlation clustering, and combines the clusterings based on the individual signals with consensus clustering.We implemented and tested PathOGiST on three different bacterial pathogens -Escherichia coli, Yersinia pseudotuberculosis, and Mycobacterium tuberculosis -and found that it outperforms most existing methods. We conclude by discussing how our framework can be extended and some of the challenges that remain to be addressed.

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MentaLiST – A fast MLST caller for large MLST schemes

Cited by 53 publications

References 39 publications

STing: accurate and ultrafast genomic profiling with exact sequence matches

STing: accurate and ultrafast genomic profiling with exact sequence matches

The Integrated Rapid Infectious Disease Analysis (IRIDA) Platform

PathOGiST: A Novel Method for Clustering Pathogen Isolates by Combining Multiple Genotyping Signals

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