BioinformaticsBioinformaticsPeer-reviewed journal article
FilTar: Using RNA-Seq data to improve microRNA target prediction accuracy in animals
Abstract
MicroRNAs (miRNAs) are a class of small non-coding RNA molecule, approximately 22nt in length, which guide the repression of mRNA transcripts. A number of tools have been developed to predict miRNA targets in animals which do not account for the effects of a specific cellular context on miRNA targeting. We present FilTar (Filtering of predicted miRNA Targets), a method which utilises available RNA-Seq information to filter non- or lowly expressed transcripts and refine existing 3UTR annotations for a given cellular context, to increase miRNA target prediction accuracy in animals.\n\nThe FilTar tool is available at https://github.com/TBradley27/FilTar.
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 13
- FWCI
- 1.03
- Field percentile
- top 31%
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