{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T05:08:59Z","timestamp":1770181739242,"version":"3.49.0"},"reference-count":25,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T00:00:00Z","timestamp":1768348800000},"content-version":"vor","delay-in-days":11,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000054","name":"NCI","doi-asserted-by":"publisher","award":["R21CA271066"],"award-info":[{"award-number":["R21CA271066"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"name":"McCormick Genomic and Proteomic Center at George Washington University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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\u2014Seurat for clustering, Slingshot for trajectory inference, scType for cell-type annotation, and CopyKat for copy-number profiling\u2014enabling integrative multi-omic analyses of expressed variation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag023","type":"journal-article","created":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T12:44:35Z","timestamp":1768394675000},"source":"Crossref","is-referenced-by-count":0,"title":["scSNViz: visualization and analysis of cell-specific expressed SNVs"],"prefix":"10.1093","volume":"42","author":[{"given":"Siera","family":"Martinez","sequence":"first","affiliation":[{"name":"McCormick Genomics and Proteomics Center, Department of Biochemistry and Molecular Medicine, School of Medicine and Health Sciences, The George Washington University , Washington, DC 20037,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tushar","family":"Sharma","sequence":"additional","affiliation":[{"name":"McCormick Genomics and Proteomics Center, Department of 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