bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/663138

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

Ambroise, J.·Irenge, L. M.·Durant, J.-F.·Bearzatto, B.·Bwire, G.·Stine, O. C.·Gala, J.-L.
View original source ↗Export metadata ↓

Abstract

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.

Source connections

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ambroise, J., Irenge, L. M., Durant, J.-F., Bearzatto, B., Bwire, G., Stine, O. C., Gala, J.-L.. 2019-06-06. Backward compatibility of whole genome sequencing data with MLVA typing using a new MLVAtype shiny application: the example of Vibrio cholerae. https://doi.org/10.1101/663138

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗
bioRxiv2026

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗
bioRxiv2026

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗