Detecting and characterizing microRNAs of diverse genomic origins via miRvial.

MicroRNAs form an essential class of post-transcriptional gene regulator of eukaryotic species, and play critical parts in development and disease and stress responses. MicroRNAs may originate from various genomic loci, have structural characteristics, and appear in canonical or modified forms, making them subtle to detect and analyze. We present miRvial, a robust computational method and companion software package that supports parameter adjustment and visual inspection of candidate microRNAs. Extensive results comparing miRvial and six existing microRNA finding methods on six model organisms, Mus musculus, Drosophila melanogaste, Arabidopsis thaliana, Oryza sativa, Physcomitrella patens and Chlamydomonas reinhardtii, demonstrated the utility and rigor of miRvial in detecting novel microRNAs and characterizing features of microRNAs. Experimental validation of several novel microRNAs in C. reinhardtii that were predicted by miRvial but missed by the other methods illustrated the superior performance of miRvial over the existing methods. miRvial is open source and available at https://github.com/SystemsBiologyOfJianghanUniversity/miRvial.