LncLSTA: a versatile predictor unveiling subcellular localization of lncRNAs through long-short term attention.

Motivation: Much evidence suggests that the subcellular localization of long-stranded noncoding 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 pseudopotentials, and nucleotide chemical property as feature inputs. Departing from conventional k-mer approaches, this model uses a set of 1D convolutional and maxpooling operations for dynamical feature aggregation. Furthermore, LncLSTA integrates a long-short term attention module with a bidirectional long and short term memory 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 and implementation: The source code is publicly available at https://bis.zju.edu.cn/LncLSTA.