Bioinformatics. 2022 Feb 21:btac110. doi: 10.1093/bioinformatics/btac110. Online ahead of print.
ABSTRACT
MOTIVATION: 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.
RESULTS: 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.
AVAILABILITY: 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 INFORMATION: Supplementary data are available at Bioinformatics online.
PMID:35188178 | DOI:10.1093/bioinformatics/btac110
