Journal Article

miR-PREFeR: an accurate, fast and easy-to-use plant miRNA prediction tool using small RNA-Seq data

Jikai Lei and Yanni Sun

in Bioinformatics

Volume 30, issue 19, pages 2837-2839
Published in print October 2014 | ISSN: 1367-4803
Published online June 2014 | e-ISSN: 1460-2059 | DOI: https://dx.doi.org/10.1093/bioinformatics/btu380
miR-PREFeR: an accurate, fast and easy-to-use plant miRNA prediction tool using small RNA-Seq data

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Summary: Plant microRNA prediction tools that use small RNA-sequencing data are emerging quickly. These existing tools have at least one of the following problems: (i) high false-positive rate; (ii) long running time; (iii) work only for genomes in their databases; (iv) hard to install or use. We developed miR-PREFeR (miRNA PREdiction From small RNA-Seq data), which uses expression patterns of miRNA and follows the criteria for plant microRNA annotation to accurately predict plant miRNAs from one or more small RNA-Seq data samples of the same species. We tested miR-PREFeR on several plant species. The results show that miR-PREFeR is sensitive, accurate, fast and has low-memory footprint.

Availability and implementation: https://github.com/hangelwen/miR-PREFeR

Contact: yannisun@msu.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  1487 words. 

Subjects: Bioinformatics and Computational Biology

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