title: miR-PREFeR: an accurate, fast and easy-to-use plant miRNA prediction tool using small RNA-Seq data.
abstract: CONCLUSIONS
  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.
  
  
  METHODS
  https://github.com/hangelwen/miR-PREFeR
authors.affiliation: Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.
authors.forename: Jikai
authors.initials: J
authors.lastname: Lei
authors.affiliation: Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.
authors.forename: Yanni
authors.initials: Y
authors.lastname: Sun
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D019985:Benchmarking; D019295:Computational Biology / Q000379:methods*; D005189:False Positive Reactions; D016678:Genome; D035683:MicroRNAs / Q000378:metabolism*; D010944:Plants / Q000235:genetics; D015203:Reproducibility of Results; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software
chemical_list: D035683:MicroRNAs