Plant microRNA prediction tools that utilize small RNA sequencing data are emerging quickly. These existing tools have at least one of the following problems:
- high false positive rate;
- long running time;
- work only for genomes in their databases;
- hard to install or use.
Researchers from Michigan State University develop miR-PREFeR (miRNA PREdiction From small RNA-Seq data), which utilizes 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. They tested miR-PREFeR on several plant species. The results show that miR-PREFeR is sensitive, accurate, fast, and has low memory footprint.
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