henhaobio
Sign in

PLoS Computational BiologyBioinformaticsPeer-reviewed journal article

miRScore: a rapid and precise microRNA validation tool

Allison VanekiD, Sam Griffiths‐JonesiD, Blake C. MeyersiD, Saima ShahidiD, Michael J. AxtelliD

PDFDOIPreprint
Citations
6
FWCI
2.69
Field percentile
top 9%

Abstract

MicroRNAs (miRNAs) are small non-protein-coding RNAs that regulate gene expression in many eukaryotes. Next-generation sequencing of small RNAs (small RNA-seq) has accelerated discovery and annotation of novel miRNAs. Newly discovered miRNAs 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-positive miRNA annotations, highlighting the need for more robust validation methods. miRScore was developed as an independent and efficient tool for evaluating miRNA annotations using sRNA-seq data. miRScore combines structural and expression-based analyses to provide rapid and reliable validation of miRNA annotations. By providing users with detailed metrics and visualization, miRScore enhances the ability to assess confidence in novel and existing miRNA annotations. miRScore has the potential to advance the overall quality of miRNA annotations by improving accuracy of new submissions to miRNA databases and serving as a resource for re-evaluating existing annotations.

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISLu '18Zhang '20Hammond '21Axtell '17Moraga '22Fromm '19
65% semantic matchBioinformatics
Yin Lu, Alexander Spyridon Baras, Marc K. Halushka·bioRxiv·Jan 19, 2018· 7 citations