DeepSplicer: An Improved Method of Splice Sites Prediction using Deep Learning

Abstract

Post-transcriptional splicing of ribonucleic acid (mRNA) entails removing regions of RNA sequences (Introns) that do not include information for protein synthesis. Thus, accurate splicing site detection is integral for understanding gene structure and, as a result, protein synthesis for biological and medicinal applications. However, the necessity to develop an advanced computational algorithm arises because existing splice site (SS) prediction methods are either computationally inefficient or expensive. Considering this, we present DeepSplicer-a deep learning-based Convolutional Neural Network (CNN) model for locating splice sites. In this work, we compared the ability of the existing SS prediction algorithms model to identify SS in organisms-Homo sapiens, Oryza sativa japonica, Arabidopsis thaliana, DrosophUa melanogaster, and Caenorhabditis elegans-to ours. Using a 5-fold cross-validation test, DeepSplicer achieves an accuracy of 96.65% for acceptor homo sapiens dataset and 94.75% for donor homo sapiens dataset. The datasets used and models generated are available at our GitHub repository here: https://github.com/OluwadareLab/DeeoSolicer.