LncSTPred: a predictive model of lncRNA subcellular localization and decipherment of the biological determinants influencing localization

Si-Le Hu; Ying-Li Chen; Lu-Qiang Zhang; Hui Bai; Jia-Hong Yang; Qian-Zhong Li

doi:10.3389/fmolb.2024.1452142

LncSTPred: a predictive model of lncRNA subcellular localization and decipherment of the biological determinants influencing localization

Front Mol Biosci. 2024 Sep 5:11:1452142. doi: 10.3389/fmolb.2024.1452142. eCollection 2024.

Authors

Si-Le Hu¹, Ying-Li Chen¹, Lu-Qiang Zhang¹, Hui Bai¹, Jia-Hong Yang¹, Qian-Zhong Li^{1

2}

Affiliations

¹ School of Physical Science and Technology, Inner Mongolia University, Hohhot, China.
² The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Inner Mongolia University, Hohhot, China.

Abstract

Introduction: Long non-coding RNAs (lncRNAs) play crucial roles in genetic markers, genome rearrangement, chromatin modifications, and other biological processes. Increasing evidence suggests that lncRNA functions are closely related to their subcellular localization. However, the distribution of lncRNAs in different subcellular localizations is imbalanced. The number of lncRNAs located in the nucleus is more than ten times that in the exosome.

Methods: In this study, we propose a new oversampling method to construct a predictive dataset and develop a predictive model called LncSTPred. This model improves the Adaboost algorithm for subcellular localization prediction using 3-mer, 3-RF sequence, and minimum free energy structure features.

Results and discussion: By using our improved Adaboost algorithm, better prediction accuracy for lncRNA subcellular localization was obtained. In addition, we evaluated feature importance by using the F-score and analyzed the influence of highly relevant features on lncRNAs. Our study shows that the ANA features may be a key factor for predicting lncRNA subcellular localization, which correlates with the composition of stems and loops in the secondary structure of lncRNAs.

Keywords: algorithm improvements; lncRNA; model biological interpretation; oversampling method; subcellular localization.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Natural Science Foundation of China (Nos. 32160216, 62361047, 62161033).