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Stine, O. C.

Publications and source records attributed to Stine, O. C..

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Backward compatibility of whole genome sequencing data with MLVA typing using a new MLVAtype shiny application: the example of Vibrio cholerae

Multiple-Locus Variable Number of Tandem Repeats (VNTR) Analysis (MLVA) is widely used by laboratory-based surveillance networks for subtyping pathogens causing foodborne and water-borne disease outbreaks. However, Whole Genome Sequencing (WGS) has recently emerged as the new more powerful reference for pathogen subtyping, making a data conversion method necessary which enables the users to compare the MLVA identified by either method. The MLVAType shiny application was designed to extract MLVA profiles from WGS data while ensuring backward compatibility with traditional MLVA typing methods.\n\nTo test and validate the MLVAType algorithm, WGS-derived MLVA profiles of nineteen Vibrio cholerae isolates from Democratic Republic of the Congo (n=9) and Uganda (n=10) were compared to MLVA profiles generated by microchip electrophoresis (Bioanalyzer Agilent 2100), GeneScan analysis, and Sanger sequencing as the reference method. Unlike amplicon-size derived MLVA profiles, results obtained by Sanger sequencing and WGS were totally concordant. However, the latter were affected by censored estimations whose percentage was inversely proportional to the k-mer parameter used during genome assembly. With a k-mer of 127, less than 15% estimation of V. cholerae VNTR was censored. Preventing censored estimation was only achievable when using a longer k-mer size (i.e. 175), which is not proposed in the SPAdes v.3.13.0 software.\n\nIn silico analysis showed that this limitation does not apply to other microbial species (e.g. Mycobacterium, Streptococcus, Staphylococcus, and Pseudomonas) characterized by smaller lengths of motif repeats. As NGS read lengths and qualities tend to increase with time, one may expect the increase of k-mer size in a near future. Using MLVAType application with a longer k-mer size will then efficiently retrieve MLVA profiles from WGS data while avoiding censored estimation irrespective of the microbial species.\n\nAuthor summaryNext Generation Sequencing (NGS) has emerged as a powerful high throughput genomic approach enabling the Whole Genome Sequence (WGS) of pathogens to be assembled in a relatively short time. A major advantage of WGS, compared to traditional genotypic identification and typing methods, is its ability to generate data that can be exploited in silico for multiple bacterial tests including accurate subtyping, determination of genetic relatedness, and characterization of virulence and antimicrobial resistance determinants. Accordingly, WGS is now rapidly replacing traditional methods like Multi-Locus Variable Number of Tandem Repeats Analysis (MLVA) that has long been used in the public health sector for laboratory-based surveillance of pathogens and outbreak response. While these missions require maintenance of data comparability within networks, the lack of backward compatibility between WGS-derived and traditional MLVA methods is a well-recognized issue. As illustrated here with Vibrio cholerae isolates from DRC and Uganda, the MLVAType software application analyzes WGS data to generate MLVA profiles that are identical to those determined with traditional typing. Interestingly, this tool has also the potential to extract MLVA profiles from any bacterial genome that are characterized by a small number of tandem repeats, e.g. Streptococcus, Staphylococcus, Pseudomonas, and Mycobacterium species. This restriction can be lifted if subsequences of length k, called k-mers, are longer than what is currently proposed by genome assembly algorithm like SPAdes v.3.13.0.

bioinformatics↗

Assessing 16S marker gene survey data analysis methods using mixtures of human stool sample DNA extracts.

BackgroundAnalysis of 16S rRNA marker-gene surveys, used to characterize prokaryotic microbial communities, may be performed by numerous bioinformatic pipelines and downstream analysis methods. However, there is limited guidance on how to decide between methods, appropriate data sets and statistics for assessing these methods are needed. We developed a mixture dataset with real data complexity and an expected value for assessing 16S rRNA bioinformatic pipelines and downstream analysis methods. We generate an assessment dataset using a two-sample titration mixture design. The sequencing data were processed using multiple bioinformatic pipelines, i) DADA2 a sequence inference method, ii) Mothur a de novo clustering method, and iii) QIIME with open-reference clustering. The mixture dataset was used to qualitatively and quantitatively assess count tables generated using the pipelines.\n\nResultsThe qualitative assessment was used to evalute features only present in unmixed samples and titrations. The abundance of Mothur and QIIME features specific to unmixed samples and titrations were explained by sampling alone. However, for DADA2 over a third of the unmixed sample and titration specific feature abundance could not be explained by sampling alone. The quantitative assessment evaluated pipeline performance by comparing observed to expected relative and differential abundance values. Overall the observed relative abundance and differential abundance values were consistent with the expected values. Though outlier features were observed across all pipelines.\n\nConclusionsUsing a novel mixture dataset and assessment methods we quantitatively and qualitatively evaluated count tables generated using three bioinformatic pipelines. The dataset and methods developed for this study will serve as a valuable community resource for assessing 16S rRNA marker-gene survey bioinformatic methods.

bioinformatics↗