Journal
BMC BIOINFORMATICS
Volume 15, Issue -, Pages -Publisher
BMC
DOI: 10.1186/s12859-014-0419-6
Keywords
piRNAs; piRNA prediction; Support vector machine (SVM); Chilo suppressalis; Drosophila melanogaster; Homo sapiens; Mus musculus; Rattus norvegicus
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Background: Piwi-interacting RNAs (piRNAs) are a class of small non-coding RNA primarily expressed in germ cells that can silence transposons at the post-transcriptional level. Accurate prediction of piRNAs remains a significant challenge. Results: We developed a program for piRNA annotation (Piano) using piRNA-transposon interaction information. We downloaded 13,848 Drosophila piRNAs and 261,500 Drosophila transposons. The piRNAs were aligned to transposons with a maximum of three mismatches. Then, piRNA-transposon interactions were predicted by RNAplex. Triplet elements combining structure and sequence information were extracted from piRNA-transposon matching/pairing duplexes. A support vector machine (SVM) was used on these triplet elements to classify real and pseudo piRNAs, achieving 95.3 +/- 0.33% accuracy and 96.0 +/- 0.5% sensitivity. The SVM classifier can be used to correctly predict human, mouse and rat piRNAs, with overall accuracy of 90.6%. We used Piano to predict piRNAs for the rice stem borer, Chilo suppressalis, an important rice insect pest that causes huge yield loss. As a result, 82,639 piRNAs were predicted in C. suppressalis. Conclusions: Piano demonstrates excellent piRNA prediction performance by using both structure and sequence features of transposon-piRNAs interactions.
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