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

Bioinformatics. 2014 Oct;30(19):2837-9. doi: 10.1093/bioinformatics/btu380. Epub 2014 Jun 14.

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

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms
  • Base Sequence
  • Benchmarking
  • Computational Biology / methods*
  • False Positive Reactions
  • Genome
  • MicroRNAs / metabolism*
  • Plants / genetics
  • Reproducibility of Results
  • Sequence Analysis, RNA / methods*
  • Software

Substances

  • MicroRNAs