STing: accurate and ultrafast genomic profiling with exact sequence matches

Espitia-Navarro, Hector F.; Chande, Aroon T.; Nagar, Shashwat Deepali; Smith, Heather; Jordan, I. King; Rishishwar, Lavanya

doi:10.1093/nar/gkaa566

Cited by 6 publications

(11 citation statements)

References 20 publications

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“…The comparison between different cgMLST workflows based on accuracy is hampered by the absence of a common strategy for definition of alleles, due to cgMLST approaches (i.e. assembly-based [ 12 , 14 – 17 ] or -free [ 13 , 18 , 19 ], or combination of both [ 20 ]), as well as implemented algorithmic steps and related parameters (e.g. BLAST-based or -free algorithms, BLASTN or BLASTP, detection of open reading frames (ORFs) before BLAST step, coverage and identity of aligned sequences [ 12 – 20 ]).…”

Section: Discussionmentioning

confidence: 99%

“…These findings are consistent with previous studies on viruses [ 49 ] and bacteria [ 50 ], that did not observe improvement of the breadth of coverage above specific values of depth of coverage. Few studies recommended minimal depth of coverage for precise cgMLST typing of L. monocytogenes (40X with BIGSdb) [ 24 , 28 , 36 ], Yersinia (50X with BIGSdb) [ 42 ], Mycoplasma (47X with SeqSphere) [ 38 ], Campylobacter , Chlamydia , Neisseria and Streptococcus (20X with STing) [ 13 ]. For the first time in the present study, we recommend 40X as a suitable read depth of coverage for the highest cgMLST precision across 6 different assembly-based and -free workflows.…”

Section: Discussionmentioning

confidence: 99%

“…Further comparisons with other assembly-free cgMLST workflows would confirm the supposed absence of strain effect on precision observed with MentaLiST [ 54 ]. However, MentaLiST outperformed other workflows in terms of percentage of correct allele predictions for cgMLST in a recent benchmarking of different assembly-free approaches [ 13 ]. Here we observed that both assembly-free and -based cgMLST workflows reach ~ 100% of identical allele predicted in the processed reads with coverage ≥40X compared to reference circular genomes.…”

Section: Discussionmentioning

confidence: 99%

“…Several parameters such as genetic background of tested strains [ 32 ], successive platings [ 33 , 38 ], replicates of DNA extraction and sequencing [ 35 ], targeted depth of coverage [ 24 , 28 , 36 ], estimated depth and breadth of coverage [ 13 , 38 , 42 ] and assembly quality [ 39 ], may impact alleles called, compromising cgMLST profiles reproducibility and the definition of outbreak clusters.…”

Section: Introductionmentioning

confidence: 99%

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In vitro and in silico parameters for precise cgMLST typing of Listeria monocytogenes

et al. 2022

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Background Whole genome sequencing analyzed by core genome multi-locus sequence typing (cgMLST) is widely used in surveillance of the pathogenic bacteria Listeria monocytogenes. Given the heterogeneity of available bioinformatics tools to define cgMLST alleles, our aim was to identify parameters influencing the precision of cgMLST profiles. Methods We used three L. monocytogenes reference genomes from different phylogenetic lineages and assessed the impact of in vitro (i.e. tested genomes, successive platings, replicates of DNA extraction and sequencing) and in silico parameters (i.e. targeted depth of coverage, depth of coverage, breadth of coverage, assembly metrics, cgMLST workflows, cgMLST completeness) on cgMLST precision made of 1748 core loci. Six cgMLST workflows were tested, comprising assembly-based (BIGSdb, INNUENDO, GENPAT, SeqSphere and BioNumerics) and assembly-free (i.e. kmer-based MentaLiST) allele callers. Principal component analyses and generalized linear models were used to identify the most impactful parameters on cgMLST precision. Results The isolate’s genetic background, cgMLST workflows, cgMLST completeness, as well as depth and breadth of coverage were the parameters that impacted most on cgMLST precision (i.e. identical alleles against reference circular genomes). All workflows performed well at ≥40X of depth of coverage, with high loci detection (> 99.54% for all, except for BioNumerics with 97.78%) and showed consistent cluster definitions using the reference cut-off of ≤7 allele differences. Conclusions This highlights that bioinformatics workflows dedicated to cgMLST allele calling are largely robust when paired-end reads are of high quality and when the sequencing depth is ≥40X.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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In vitro and in silico parameters for precise cgMLST typing of Listeria monocytogenes

et al. 2022

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“…Hence, we proceeded to investigate the association of shorter sequences (k-mers) with PFGE banding patterns. k-mers have been shown to be sufficiently informative short sequences used across a number of different applications including genome assembly (Compeau et al, 2011), read-togenome mapping (Li and Durbin, 2009), taxonomic classification (Nayfach et al, 2016;Wood et al, 2019), and multi-locus sequence typing (Gupta et al, 2017;Espitia-Navarro et al, 2020). Consequently, we represent our genomes as high-dimensional k-mer presence/absence vectors and utilize these vectors within a machine learning framework.…”

Section: Leveraging Wgs For Creating High-dimensional Modelsmentioning

confidence: 99%

Genome-Enabled Molecular Subtyping and Serotyping for Shiga Toxin-Producing Escherichia coli

Gupta

Jain

et al. 2021

Front. Sustain. Food Syst.

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Foodborne pathogens are a major public health burden in the United States, leading to 9.4 million illnesses annually. Since 1996, a national laboratory-based surveillance program, PulseNet, has used molecular subtyping and serotyping methods with the aim to reduce the burden of foodborne illness through early detection of emerging outbreaks. PulseNet affiliated laboratories have used pulsed-field gel electrophoresis (PFGE) and immunoassays to subtype and serotype bacterial isolates. Widespread use of serotyping and PFGE for foodborne illness surveillance over the years has resulted in the accumulation of a wealth of routine surveillance and outbreak epidemiological data. This valuable source of data has been used to understand seasonal frequency, geographic distribution, demographic information, exposure information, disease severity, and source of foodborne isolates. In 2019, PulseNet adopted whole genome sequencing (WGS) at a national scale to replace PFGE with higher-resolution methods such as the core genome multilocus sequence typing. Consequently, PulseNet's recent shift to genome-based subtyping methods has rendered the vast collection of historic surveillance data associated with serogroups and PFGE patterns potentially unusable. The goal of this study was to develop a bioinformatics method to associate the WGS data that are currently used by PulseNet for bacterial pathogen subtyping to previously characterized serogroup and PFGE patterns. Previous efforts to associate WGS to PFGE patterns relied on predicting DNA molecular weight based on restriction site analysis. However, these approaches failed owing to the non-uniform usage of genomic restriction sites by PFGE restriction enzymes. We developed a machine learning approach to classify isolates to their most probable serogroup and PFGE pattern, based on comparisons of genomic k-mer signatures. We applied our WGS classification method to 5,970 Shiga toxin-producing Escherichia coli (STEC) isolates collected as part of PulseNet's routine foodborne surveillance activities between 2003 and 2018. Our machine learning classifier is able to associate STEC WGS to higher-level serogroups with very high accuracy and lower-level PFGE patterns with somewhat lower accuracy. Taken together, these classifications support the ability of public health investigators to associate currently generated WGS data with historical epidemiological knowledge linked to serogroups and PFGE patterns in support of outbreak surveillance for food safety and public health.

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Systems-based approach for optimization of a scalable bacterial ST mapping assembly-free algorithm

Pavlovikj

Gomes-Neto

Deogun

et al. 2021

Preprint

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Epidemiological surveillance of bacterial pathogens requires real-time data analysis with a fast turn-around, while aiming at generating two main outcomes: 1) Species level identification; and 2) Variant mapping at different levels of genotypic resolution for population-based tracking, in addition to predicting traits such as antimicrobial resistance (AMR). With the recent advances and continual dissemination of whole-genome sequencing technologies, large-scale population-based genotyping of bacterial pathogens has become possible. Since bacterial populations often present a high degree of clonality in the genomic backbone (i.e., low genetic diversity), the choice of genotyping scheme can even facilitate the understanding of ancestral relationships and can be used for prediction of co-inherited traits such as AMR. Multi-locus sequence typing (MLST) fits that purpose and can identify sequence types (ST) based on seven ubiquitous genome-scattered loci that aid in genotyping isolates beneath the species level. ST-based mapping also standardizes genotyping across laboratories and is used by laboratories worldwide. However, algorithms for inferring ST from Illumina paired-end sequencing data typically rely on genome assembly prior to classification. Genome assembly is computationally intensive and is a bottleneck for speed and scalability, which are important aspects of genomic epidemiology. The stringMLST program uses an assembly-free, kmer-based algorithm for inferring STs, which can overcome the speed and scalability bottlenecks. Here we have systematically studied the accuracy and scalability of stringMLST relative to the standard MLST program across a wide array of phylogenetically divergent Public Health-relevant bacterial pathogens. Our data shows that optimal kmer length for stringMLST is species-specific and that genome-intrinsic and -extrinsic features can affect performance and accuracy of the program. While suitable parameters could be identified for most organisms, there were a few instances where this program may not be directly deployable in its current format. More importantly, we integrated stringMLST into our freely available and scalable hierarchical-based population genomics platform, ProkEvo, and further demonstrated how the implementation facilitates automated, reproducible bacterial population analysis. The ProkEvo implementation provides a rapidly deployable genomic epidemiology tool for ST mapping along with other pan-genomic data mining strategies, while providing specific guidance on how to optimize stringMLST performance for a wide variety of bacterial pathogens.

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STing: accurate and ultrafast genomic profiling with exact sequence matches

Cited by 6 publications

References 20 publications

In vitro and in silico parameters for precise cgMLST typing of Listeria monocytogenes

In vitro and in silico parameters for precise cgMLST typing of Listeria monocytogenes

Genome-Enabled Molecular Subtyping and Serotyping for Shiga Toxin-Producing Escherichia coli

Systems-based approach for optimization of a scalable bacterial ST mapping assembly-free algorithm

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