LncReader: identification of dual functional long noncoding RNAs using a multi-head self-attention mechanism. Issue 1 (27th December 2022)

• Record Type: Journal Article
• Authors: Liu, Tianyuan; Zou, Bohao; He, Manman; Hu, Yongfei; Dou, Yiying; Cui, Tianyu; Tan, Puwen; Li, Shaobin; Rao, Shuan; Huang, Yan; Liu, Sixi; Cai, Kaican; Wang, Dong
• Is Part Of: Briefings in bioinformatics. Volume 24:Issue 1(2023)
• Publication Date: 2022-12-27
• Subjects: noncoding RNA -- system biology -- deep learning; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac579
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Long noncoding ribonucleic acids (RNAs; LncRNAs) endowed with both protein-coding and noncoding functions are referred to as 'dual functional lncRNAs'. Recently, dual functional lncRNAs have been intensively studied and identified as involved in various fundamental cellular processes. However, apart from time-consuming and cell-type-specific experiments, there is virtually no in silico method for predicting the identity of dual functional lncRNAs. Here, we developed a deep-learning model with a multi-head self-attention mechanism, LncReader, to identify dual functional lncRNAs. Our data demonstrated that LncReader showed multiple advantages compared to various classical machine learning methods using benchmark datasets from our previously reported cncRNAdb project. Moreover, to obtain independent in-house datasets for robust testing, mass spectrometry proteomics combined with RNA-seq and Ribo-seq were applied in four leukaemia cell lines, which further confirmed that LncReader achieved the best performance compared to other tools. Therefore, LncReader provides an accurate and practical tool that enables fast dual functional lncRNA identification.