FilTar: Using RNA-Seq data to improve microRNA target prediction accuracy in animals

Bradley, Thomas and Moxon, Simon (2020) FilTar: Using RNA-Seq data to improve microRNA target prediction accuracy in animals. Bioinformatics. ISSN 1367-4803

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Abstract

Motivation: microRNA (miRNA) target prediction algorithms do not generally consider biological context and therefore generic target prediction based on seed binding can lead to a high level of false positive predictions. Here we present FilTar, a method that incorporates RNA-Seq data to make miRNA target prediction specific to a given cell type or tissue of interest. Results: We demonstrate that FilTar can be used to 1) provide sample specific 3’UTR reannota-tion; extending or truncating default annotations based on RNA-Seq read evidence. and 2) filter pu-tative miRNA target predictions by transcript expression level, thus removing putative interactions where the target transcript is not expressed in the tissue or cell-line of interest. We test the method on a variety of miRNA transfection datasets and demonstrate increased accuracy versus generic miRNA target prediction methods. Availability: FilTar is freely available and can be downloaded from https://github.com/TBradley27/FilTar. The tool is implemented using the Python and R programming languages, and is supported on GNU/Linux operating systems. Contact: s.moxon@uea.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Item Type: Article
Faculty \ School: Faculty of Science > School of Biological Sciences
Depositing User: LivePure Connector
Date Deposited: 03 Jan 2020 04:00
Last Modified: 28 Mar 2020 01:27
URI: https://ueaeprints.uea.ac.uk/id/eprint/73450
DOI: 10.1093/bioinformatics/btaa007

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