LncLSTA: A versatile predictor unveiling subcellular localization of lncRNAs through long-short term attention
Kai Wang, Yueming Hu, Sida Li, Ming Chen, Zhong LiAbstract
Motivation
Many evidences suggest that the subcellular localization of long-stranded non-coding RNAs (LncRNAs) provides key insights for the study of their biological function.
Results
This study proposes a novel deep learning framework, LncLSTA, designed for predicting the subcellular localization of LncRNAs. It firstly exploits LncRNA sequence, electron-ion interaction potential (EIIP), and nucleotide chemical property (NCP) as feature inputs. Departing from conventional k-mer approaches, this model employs a set of one-dimensional convolutional and maxpooling operations for dynamical feature aggregation. Furthermore, LncLSTA integrates a long-short term attention module with a Bi-LSTM network to comprehensively extract sequence information. In addition, it incorporates a TextCNN module to enhance accuracy and robustness in subcellular localization tasks. Experimental results demonstrate the efficacy of LncLSTA, showcasing its superior performance compared to other state-of-the-art methods. Notably, LncLSTA exhibits the transfer learning capability, extending its utility to predict the subcellular localization prediction of mRNAs, while maintaining consistently satisfactory prediction results. This research contributes valuable insights into understanding the biological functions of LncRNAs through subcellular localization, emphasizing the potential of deep learning approaches in advancing RNA-related studies.
Availability
The source code is publicly available at https://bis.zju.edu.cn/LncLSTA.
Supplementary information
Supplementary data are available at Bioinformatics online.