show Abstracthide AbstractThe dual functional lncRNAs have been intensively studied and identified to be involved in various fundamental cellular processes recently. It is essential to understand in which context when a dual functional lncRNA serves as a non-coding RNA or a template for coding peptide, particularly in some pathological conditions. 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 multi-head self-attention mechanism, LncReader, to identify dual functional lncRNAs based on their sequence, physicochemical and secondary structural features. Our data demonstrated that LncReader showed multiple advantage compared to various classical machine learning methods. Moreover, to obtain independent in-house datasets for robust testing, mass spectrometry proteomics combined with RNA-seq were applied in four leukemia cell lines. Remarkably, LncReader achieved the best performance among all these datasets. Therefore, LncReader provides a sophisticated and practical tool that enables fast dual functional lncRNAs identification. Overall design: Ribosome-seq of four leukemia cell lines (HL60, MOLM13, K562, THP_1), one replicate of each cell line.