Rationale: PIWI-interacting RNAs (piRNAs) are a recently-discovered class of small noncoding
RNAs (ncRNAs) with a length of 21-35 nucleotides. They play a role in gene expression regulation,
transposon silencing, and viral infection inhibition. Once considered as “dark matter” of
ncRNAs, piRNAs emerged as important players in multiple cellular functions in different organisms.
However, our knowledge of piRNAs is still very limited as many piRNAs have not been yet identified
due to lack of robust computational predictive tools.
Methods: To identify novel piRNAs, we developed piRNAPred, an integrated framework for piRNA
prediction employing hybrid features like k-mer nucleotide composition, secondary structure, thermodynamic
and physicochemical properties. A non-redundant dataset (D3349 or D1684p+1665n) comprising
1684 experimentally verified piRNAs and 1665 non-piRNA sequences was obtained from piRBase
and NONCODE, respectively. These sequences were subjected to the computation of various sequence-
structure based features in binary format and trained using different machine learning techniques,
of which support vector machine (SVM) performed the best.
Results: During the ten-fold cross-validation approach (10-CV), piRNAPred achieved an overall accuracy
of 98.60% with Mathews correlation coefficient (MCC) of 0.97 and receiver operating characteristic
(ROC) of 0.99. Furthermore, we achieved a dimensionality reduction of feature space using an
attribute selected classifier.
Conclusion: We obtained the highest performance in accurately predicting piRNAs as compared to
the current state-of-the-art piRNA predictors. In conclusion, piRNAPred would be helpful to expand
the piRNA repertoire, and provide new insights on piRNA functions.