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bioRxiv · 10.1101/2023.01.17.524365

A hybrid approach for predicting multi-label subcellular localization of mRNA at genome scale

Choudhury, S.·Bajiya, N.·Patiyal, S.·Raghava, G. P. S.
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Abstract

In the past, number of methods have been developed for predicting single label subcellular localization of mRNA in a cell. Only limited methods had been built to predict multi-label subcellular localization of mRNA. Most of the existing methods are slow and cannot be implemented at transcriptome scale. In this study, a fast and reliable method had been developed for predicting multi-label subcellular localization of mRNA that can be implemented at genome scale. Firstly, deep learning method based on convolutional neural network method have been developed using one-hot encoding and attained an average AUROC - 0.584 (0.543 - 0.605). Secondly, machine learning based methods have been developed using mRNA sequence composition, our XGBoost classifier achieved an average AUROC - 0.709 (0.668 - 0.732). In addition to alignment free methods, we also developed alignment-based methods using similarity and motif search techniques. Finally, a hybrid technique has been developed that combine XGBoost models and motif-based searching and achieved an average AUROC 0.742 (0.708 - 0.816). Our method - MRSLpred, developed in this study is complementary to the existing method. One of the major advantages of our method over existing methods is its speed, it can scan all mRNA of a transcriptome in few hours. A publicly accessible webserver and a standalone tool has been developed to facilitate researchers (Webserver: https://webs.iiitd.edu.in/raghava/mrslpred/). Key PointsO_LIPrediction of Subcellular localization of mRNA C_LIO_LIClassification of mRNA based on Motif and BLAST search C_LIO_LICombination of alignment based and alignment free techniques C_LIO_LIA fast method for subcellular localization of mRNA C_LIO_LIA web server and standalone software C_LI

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Choudhury, S., Bajiya, N., Patiyal, S., Raghava, G. P. S.. 2023-01-19. A hybrid approach for predicting multi-label subcellular localization of mRNA at genome scale. https://doi.org/10.1101/2023.01.17.524365

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