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Abstract Motivation MicroRNAs (miRNAs) are small noncoding RNAs that play important roles in gene regulation and phenotype development. The identification of miRNA transcription start sites (TSSs) is critical to understand the functional roles of miRNA genes and their transcriptional regulation. Unlike protein-coding genes, miRNA TSSs are not directly detectable from conventional RNA-Seq experiments due to miRNA-specific process of biogenesis. In the past decade, large-scale genome-wide TSS-Seq and transcription activation marker profiling data have become available, based on which, many computational methods have been developed. These methods have greatly advanced genome-wide miRNA TSS annotation. Results In this study, we summarized recent computational methods and their results on miRNA TSS annotation. We collected and performed a comparative analysis of miRNA TSS annotations from 14 representative studies. We further compiled a robust set of miRNA TSSs (RSmirT) that are supported by multiple studies. Integrative genomic and epigenomic data analysis on RSmirT revealed the genomic and epigenomic features of miRNA TSSs as well as their relations to protein-coding and long non-coding genes. Contact xiaoman@mail.ucf.edu, haihu@cs.ucf.edu
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This content will become publicly available on November 3, 2026
miRScore: A rapid and precise microRNA validation tool
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.
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- PAR ID:
- 10655075
- Editor(s):
- Ziemann, Mark
- Publisher / Repository:
- Public Library of Sciences
- Date Published:
- Journal Name:
- PLOS Computational Biology
- Volume:
- 21
- Issue:
- 11
- ISSN:
- 1553-7358
- Page Range / eLocation ID:
- e1013663
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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