MOTIVATION: Accurately characterizing expressed genetic variation at the single-cell level is essential for understanding transcriptional heterogeneity, allelic regulation, and mutational dynamics within complex tissues. However, few tools enable comprehensive visualization and quantitative analysis of expressed variants across individual cells. RESULTS: scSNViz is an R package for the exploration, quantification, and visualization of expressed single-nucleotide variants (SNVs) from cell-barcoded single-cell RNA sequencing (scRNA-seq) data. The software supports estimation of variant allele fractions, clustering of SNV expression profiles, and 2D and 3D visualization of individual SNVs or user-defined SNV groups. Beyond visualization, scSNViz facilitates investigation of cell-, cluster-, or lineage-specific variant expression patterns, as well as allelic dynamics including imprinting, random allele inactivation, and transcriptional bursting. It interoperates seamlessly with established single-cell frameworks-Seurat for clustering, Slingshot for trajectory inference, scType for cell-type annotation, and CopyKat for copy-number profiling-enabling integrative multi-omic analyses of expressed variation. AVAILABILITY AND IMPLEMENTATION: scSNViz is implemented in R and freely available at https://github.com/HorvathLab/scSNViz (DOI: 10.5281/zenodo.17307516). The package includes comprehensive documentation and example workflows designed for users with limited bioinformatics experience.

RATIONALE: The growing recognition of the importance of splicing, together with rapidly accumulating RNA-sequencing data, demand robust high-throughput approaches, which efficiently analyze experimentally derived whole-transcriptome splice profiles. RESULTS: We have developed a computational approach, called SNPlice, for identifying cis-acting, splice-modulating variants from RNA-seq datasets. SNPlice mines RNA-seq datasets to find reads that span single-nucleotide variant (SNV) loci and nearby splice junctions, assessing the co-occurrence of variants and molecules that remain unspliced at nearby exon-intron boundaries. Hence, SNPlice highlights variants preferentially occurring on intron-containing molecules, possibly resulting from altered splicing. To illustrate co-occurrence of variant nucleotide and exon-intron boundary, allele-specific sequencing was used. SNPlice results are generally consistent with splice-prediction tools, but also indicate splice-modulating elements missed by other algorithms. SNPlice can be applied to identify variants that correlate with unexpected splicing events, and to measure the splice-modulating potential of canonical splice-site SNVs. AVAILABILITY AND IMPLEMENTATION: SNPlice is freely available for download from https://code.google.com/p/snplice/ as a self-contained binary package for 64-bit Linux computers and as python source-code. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.