Jikai Lei1, Yanni Sun1. 1. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.
Abstract
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
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
Authors: MeiYee Law; Kevin L Childs; Michael S Campbell; Joshua C Stein; Andrew J Olson; Carson Holt; Nicholas Panchy; Jikai Lei; Dian Jiao; Carson M Andorf; Carolyn J Lawrence; Doreen Ware; Shin-Han Shiu; Yanni Sun; Ning Jiang; Mark Yandell Journal: Plant Physiol Date: 2014-11-10 Impact factor: 8.340