Liu et al. 2022 (PRJNA831863)

Title	LncReader: identification of dual functional long non-coding RNAs using multi-head self-attention mechanism [Ribo-seq]
Organism
Number of Samples	4
Release Date	2022/04/25 00:00
Sequencing Types
Protocol Details

GWIPS-viz	Trips-Viz

Title
Authors	Liu T,Zou B,He M,Hu Y,Dou Y,Cui T,Tan P,Li S,Rao S,Huang Y,Liu S,Cai K,Wang D
Journal	Briefings in bioinformatics
Publication Date	2023 Jan 19
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. © The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
PMC
PMID	36575567
DOI

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