piRNAPred – computational Identification of piRNAs Using Features Based on RNA Sequence, Structure, Thermodynamic and Physicochemical Properties

PIWI-interacting RNAs (piRNAs) are a recently-discovered class of small non-coding 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.

To identify novel piRNAs, researchers from the Indian Institute of Science Education and Research have 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 D^1684p+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.

Schematic illustration of the overall workflow adopted to develop piRNAPred

Left and right arm demonstrates the processing of piRNA and non-piRNA sequence from piRBase and NONCODE, respectively to generate a dataset (D^1684p+1665n) followed by their downstream conversion into sequence, structure, thermodynamic, physiochemical and BINARY¹⁺¹⁰ feature space and predictive model development. 

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, the researchers achieved a dimensionality reduction of feature space using an attribute selected classifier.

The researchers 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.