Although microRNAs are key post-transcriptional regulators of gene expression, finding functionally relevant targets remains difficult. Recent study has demonstrated that combining a biochemical model with experimentally generated k-mer affinity predictions improves prediction of microRNA-mediated repression; nevertheless, these findings are not easily applied.
Recently, Soutschek M. et. al.(2022) have adapted this method into a flexible and user-friendly bioconductor package, scanMiR, is also available through a web interface. scanMiR rapidly searches for binding sites, measures their affinity, and predicts aggregated transcript repression using lightweight linear models. Furthermore, the ability to predict unusual interactions, such as binds that could lead to target-directed microRNA degradation or slicing, is enabled by the flexible 3′-supplementary alignment. We demonstrate scanMiR by conducting a systematic search for unusual locations on neuronal transcripts, such as lncRNAs and circRNAs. Finally, we provide a user-friendly online application that allows for sequence scanning, visualisation of predicted bindings, and browsing of anticipated target repression, in addition to the primary bioconductor programme that implements these features.
scanMiR is available at https://bioconductor.org/packages/release/bioc/html/scanMiR.html
Reference:
Soutschek M. et. al.(2022)scanMiR: a biochemically based toolkit for versatile and efficient microRNA target prediction. Bioinformatics 38(9): 2466–2473