Effective computational detection of piRNAs using n-gram models and support vector machine

BACKGROUND: Piwi-interacting RNAs (piRNAs) are a new class of small non-coding RNAs that are known to be associated with RNA silencing. The piRNAs play an important role in protecting the genome from invasive transposons in the germline. Recent studies have shown that piRNAs are linked to the genome stability and a variety of human cancers. Due to their clinical importance, there is a pressing need for effective computational methods that can be used for computational identification of piRNAs. However, piRNAs lack conserved structural motifs and show relatively low sequence similarity across different species, which makes accurate computational prediction of piRNAs challenging. RESULTS: In this paper, we propose a novel method, piRNAdetect, for reliable computational prediction of piRNAs in genome sequences. In the proposed method, we first classify piRNA sequences in the training dataset that share similar sequence motifs and extract effective predictive features through the use of n-gram models (NGMs). The extracted NGM-based features are then used to construct a support vector machine that can be used for accurate prediction of novel piRNAs. CONCLUSIONS: We demonstrate the effectiveness of the proposed piRNAdetect algorithm through extensive performance evaluation based on piRNAs in three different species – H. sapiens, R. norvegicus, and M. musculus – obtained from the piRBase and show that piRNAdetect outperforms the current state-of-the-art methods in terms of efficiency and accuracy.