scanMiR: a biochemically based toolkit for versatile and efficient microRNA target prediction

microRNAs are important post-transcriptional regulators of gene expression, but the identification of functionally relevant targets is still challenging. Recent research has shown improved prediction of microRNA-mediated repression using a biochemical model combined with empirically-derived k-mer affinity predictions; however, these findings are not easily applicable.

We translate this approach into a flexible and user-friendly bioconductor package, scanMiR, also available through a web interface. Using lightweight linear models, scanMiR efficiently scans for binding sites, estimates their affinity and predicts aggregated transcript repression. Moreover, flexible 3'-supplementary alignment enables the prediction of unconventional interactions, such as bindings potentially leading to target-directed microRNA degradation or slicing. We showcase scanMiR through a systematic scan for such unconventional sites on neuronal transcripts, including lncRNAs and circRNAs. Finally, in addition to the main bioconductor package implementing these functions, we provide a user-friendly web application enabling the scanning of sequences, the visualization of predicted bindings and the browsing of predicted target repression.

scanMiR and companion packages are implemented in R, released under the GPL-3 and accessible on Bioconductor (https://bioconductor.org/packages/release/bioc/html/scanMiR.html) as well as through a shiny web server (https://ethz-ins.org/scanMiR/).

Supplementary data are available at Bioinformatics online.