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MicroRNAs (miRNAs) are small non-protein-coding RNAs that regulate gene expression in many eukaryotes. Next-generation sequencing of small RNAs (small RNA-seq) is central to the discovery and annotation of miRNAs. Newly annotated miRNAs and their longer precursors encoded byMIRNAloci are typically submitted to databases such as the miRBase microRNA registry following the publication of a peer-reviewed study. However, genome-wide scans using small RNA-seq data often yield high rates of false-positiveMIRNAannotations, highlighting the need for more robust validation methods. miRScore was developed as an independent and efficient tool for evaluating newMIRNAannotations using sRNA-seq data. miRScore combines structural and expression-based analyses to provide rapid and reliable validation of newMIRNAannotations. By providing users with detailed metrics and visualization, miRScore enhances the ability to assess confidence inMIRNAannotations. miRScore has the potential to advance the overall quality ofMIRNAannotations by improving accuracy of new submissions to miRNA databases and serving as a resource for re-evaluating existing annotations.