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Bioinformatics. 2020 Jan 13. pii: btaa007. doi: 10.1093/bioinformatics/btaa007. [Epub ahead of print]

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

Author information

1
School of Biological Sciences, University of East Anglia, Norwich, UK. NR4 7TJ.
2
Earlham Institute, Norwich Research Park, Norwich, UK. NR4 7UZ.

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 reannotation; extending or truncating default annotations based on RNA-Seq read evidence. and 2) filter putative 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.

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

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