NN-RNALoc: neural network-based model for prediction of mRNA sub-cellular localization using distance-based sub-sequence profiles

Abstract

AbstractLocalization of messenger RNAs (mRNA) as a widely observed phenomenon is considered as an efficient way to target proteins to a specific region of a cell and is also known as a strategy for gene regulation. The importance of correct intracellular RNA placement in the development of embryonic and neural dendrites has long been demonstrated in former studies. Improper localization of RNA in the cell, which has been shown to occur due to a variety of reasons, including mutations in trans-regulatory elements, is also associated with the occurrence of some neuromuscular diseases as well as cancer. We propose NN-RNALoc, a neural network-based model to predict the cellular location of mRNAs. The features extracted from mRNA sequences along with the information gathered from their proteins are fed to this prediction model. We introduce a novel distance-based sub-sequence profile for representation of RNA sequences which is more memory and time efficient and comparying to the k-mer frequencies, can possibly better encode sequences when the distance k increases. The performance of NN-RNALoc on the following benchmark datsets CeFra-seq and RNALocate, is compared to the results achieved by two powerful prediction models that were proposed in former studies named as mRNALoc and RNATracker The results reveal that the employment of protein-protein interaction information, which plays a crucial role in many biological functions, together with the novel distance-based sub-sequence profiles of mRNA sequences, leads to a more accurate prediction model. Besides, NN-RNALoc significantly reduces the required computing time compared to previous studies. Source code and data used in this study are available at: https://github.com/NeginBabaiha/NN-RNALoc