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Despite their central roles, current snoRNA annotations remain incomplete. Several eukaryote genome annotations contain few or no snoRNAs, and none distinguish expressed snoRNAs from their pseudogenes\u2014a recently characterized snoRNA subclass with distinct features and expression levels. To address this, we developed SnoBIRD, a BERT-based C\/D box snoRNA predictor trained on snoRNAs spanning all eukaryote kingdoms. We show that SnoBIRD outperforms existing tools and is the only predictor capable of identifying snoRNA pseudogenes using biologically relevant signal. Applied on the fission yeast and human genomes, we demonstrate that only SnoBIRD scales well with genome size in terms of runtime, and we identify and experimentally validate several new SnoBIRD-predicted C\/D box snoRNAs. By running SnoBIRD on multiple eukaryote genomes, we identify hundreds of novel snoRNA candidates and highlight SnoBIRD\u2019s usefulness to determine the evolutionary paths of snoRNAs distributed across different species. Overall, SnoBIRD represents a user-friendly and efficient tool for reliably predicting C\/D box snoRNAs and their pseudogenes across any eukaryote genome.<\/jats:p>","DOI":"10.1093\/nar\/gkaf708","type":"journal-article","created":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T14:17:23Z","timestamp":1752502643000},"source":"Crossref","is-referenced-by-count":5,"title":["SnoBIRD: a tool to identify C\/D box snoRNAs and refine their annotation across all eukaryotes"],"prefix":"10.1093","volume":"53","author":[{"given":"\u00c9tienne","family":"Fafard-Couture","sequence":"first","affiliation":[{"name":"D\u00e9partement de biochimie et de g\u00e9nomique fonctionnelle, Facult\u00e9 de m\u00e9decine et des sciences de la sant\u00e9, Universit\u00e9 de Sherbrooke , Sherbrooke , Qu\u00e9bec\u00a0 J1E 4K8 ,","place":["Canada"]},{"name":"Centre de recherche du Centre hospitalier universitaire de Sherbrooke (CRCHUS) , Sherbrooke , Qu\u00e9bec\u00a0 J1H 5N3 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