bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2022.07.27.501805

MinSNPs: an R package for derivation of resolution-optimised SNP sets from microbial genomic data

Hoon, K. S.·Holt, D. C.·Auburn, S.·Shaw, P.·Giffard, P. M.
View original source ↗Export metadata ↓

Abstract

2.Here we present the R package - MinSNPs. This is designed to assemble resolution optimised sets of single nucleotide polymorphisms (SNPs) from alignments such as genome wide orthologous SNP matrices. We also demonstrate a pipeline for assembling such matrices from multiple bio-projects, so as to facilitate SNP set derivation from globally representative data sets. MinSNPs can derive sets of SNPs optimised for discriminating any user-defined combination of sequences from all others. Alternatively, SNP sets may be optimised to discriminate all from all, i.e., to maximise diversity. MinSNPs encompasses functions that facilitate rapid and flexible SNP mining, and clear and comprehensive presentation of the results. The MinSNPs running time scales in a linear fashion with input data volume, and the numbers of SNPs and SNPs sets specified in the output. MinSNPs was tested using a previously reported orthologous SNP matrix of Staphylococcus aureus. and an orthologous SNP matrix of 3,279 genomes with 164,335 SNPs assembled from four S. aureus short read genomic data sets. MinSNPs demonstrated efficacy in deriving discriminatory SNP sets for potential surveillance targets and in identifying SNP sets optimised to discriminate isolates from different clonal complexes (CC). MinSNPs was also tested with a large Plasmodium vivax orthologous SNP matrix. A set of five SNPs was derived that reliably indicated the country of origin within 3 south-east Asian countries. In summary, we report the capacity to assemble comprehensive SNP matrices that effectively capture microbial genomic diversity, and to rapidly and flexibly mine these entities for optimised surveillance marker sets. 3. Impact statementWe present the R package "MinSNPs". This derives resolution optimised SNP sets from datasets of genome sequence variation. Such SNP sets can underpin targeted genetic analysis for high throughput surveillance of microbial variants of public health concern. MinSNPs supports considerable flexibility in search methods. The package allows non-specialist bioinformaticians to easily and quickly convert global scale data of intra-specific genomic variation into SNP sets precisely and efficiently directed towards many microbial genetic analysis tasks. 4. Data summaryO_LIThe source code for minSNPs is available from GitHub under MIT Licence (URLs - https://github.com/ludwigHoon/minSNPs and mirrored in https://cran.r-project.org/package=minSNPs) C_LIO_LIStaphylococcus aureus (STARRS data set) Orthologous SNP Matrix; (URL - https://doi.org/10.1371/journal.pone.0245790.s005) C_LIO_LIPlasmodium vivax data set (VCF file); (URL - https://www.malariagen.net/resource/24) C_LIO_LIStaphylococcus aureus short read sequences (fastq) from bioprojects: PRJEB40888 (or STARRS)(https://www.ncbi.nlm.nih.gov/bioproject/PRJEB40888), PRJEB3174 (https://www.ncbi.nlm.nih.gov/bioproject/PRJEB3174), PRJEB32286 (https://www.ncbi.nlm.nih.gov/bioproject/PRJEB32286), and PRJNA400143 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA400143) C_LI

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hoon, K. S., Holt, D. C., Auburn, S., Shaw, P., Giffard, P. M.. 2022-07-29. MinSNPs: an R package for derivation of resolution-optimised SNP sets from microbial genomic data. https://doi.org/10.1101/2022.07.27.501805

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↗