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

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Abstract

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.

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Journal

Journal ID (iso-abbrev): Bioinformatics

Title: Bioinformatics (Oxford, England)

Publisher: Oxford University Press (OUP)

ISSN (Electronic): 1367-4811

ISSN (Print): 1367-4803

Publication date (Electronic): Apr 28 2022

Volume: 38

Issue: 9

Affiliations

[1 ] Lab of Systems Neuroscience, D-HEST Institute for Neuroscience, ETH Zürich, Zürich, Switzerland.

[2 ] Neuroscience Center Zurich, ETH Zurich and University of Zurich, Zürich, Switzerland.

[3 ] Lab of Statistical Bioinformatics, IMLS, University of Zürich, Zürich, Switzerland.

[4 ] Computational Neurogenomics, D-HEST Institute for Neuroscience, ETH Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland.

Article

Publisher Item ID: 6533438

DOI: 10.1093/bioinformatics/btac110

PubMed ID: 35188178

SO-VID: 1bd8350f-f1c1-404f-9955-6e34f845cb08

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