The authors present a pattern-based approach, mirPD, which uses a two-stage filtration to identify miRNAs from deep sequencing data. In the first filtration stage, patterns capturing conserved knowledge of real miRNAs are extracted from real (published) miRNAs to filter reads. The reads passing the pattern filtration are then mapped to the genome to get candidate precursors which are further filtered according to miRNA biological features in the second stage. Compared with the classic miRNA identification method miRDeep (v1 and v2) on a typical dataset, the experimental result indicates that the mirPD provides higher sensitivity and similar precision, accuracy and specificity.
Full text available on ScienceDirect: http://dx.doi.org/10.1016/j.dsp.2013….