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 AND IMPLEMENTATION: 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.

BACKGROUND: Despite the importance of alternative poly-adenylation and 3' UTR length for a variety of biological phenomena, there are limited means of detecting UTR changes from standard transcriptomic data. RESULTS: We present the diffUTR Bioconductor package which streamlines and improves upon differential exon usage (DEU) analyses, and leverages existing DEU tools and alternative poly-adenylation site databases to enable differential 3' UTR usage analysis. We demonstrate the diffUTR features and show that it is more flexible and more accurate than state-of-the-art alternatives, both in simulations and in real data. CONCLUSIONS: diffUTR enables differential 3' UTR analysis and more generally facilitates DEU and the exploration of their results.