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


Soutschek, Michael; Gross, Fridolin; Schratt, Gerhard; Germain, Pierre-Luc (2022). scanMiR: a biochemically based toolkit for versatile and efficient microRNA target prediction. Bioinformatics, 38(9):2466-2473.

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 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.

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 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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Molecular Life Sciences
Dewey Decimal Classification:570 Life sciences; biology
Scopus Subject Areas:Physical Sciences > Statistics and Probability
Life Sciences > Biochemistry
Life Sciences > Molecular Biology
Physical Sciences > Computer Science Applications
Physical Sciences > Computational Theory and Mathematics
Physical Sciences > Computational Mathematics
Language:English
Date:28 April 2022
Deposited On:17 Feb 2023 12:53
Last Modified:28 Jun 2024 01:42
Publisher:Oxford University Press
ISSN:1367-4803
OA Status:Closed
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1093/bioinformatics/btac110
PubMed ID:35188178
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