MRSLpred—a hybrid approach for predicting multi-label subcellular localization of mRNA at the genome scale - Choudhury, Shubham; Bajiya, Nisha; Patiyal, Sumeet; Raghava, Gajendra P. S. (Frontiers in Bioinformatics, 2024)
In the past, several methods have been developed for predicting single-label subcellular localization of mRNA. However, only limited methods are designed to predict multi-label subcellular localization of mRNA. Also, the existing methods are slow and cannot be implemented at a transcriptome scale. In this study, a fast and reliable method has been developed for predicting multi-label subcellular localization of mRNA that can be implemented at genome scale. Machine learning based methods have been developed using mRNA sequence composition, where the XGBoost-based classifier achieved an average AUROC -0.709 (0.668 -0.732). In addition to alignment free methods, we developed alignment-based methods using motif search techniques. Finally, a hybrid technique that combines the XGBoost model and the motif-based approach has been developed, achieving an average AUROC of 0.742 (0.708 -0.816). Our method -MRSLpred, outperforms the existing state-of-the-art classifier in terms of performance as well as computation efficiency. A publicly accessible webserver and a standalone tool has been developed to facilitate researchers (Webserver: https://webs.iiitd.edu.in/raghava/mrslpred/).