iEssLnc: quantitative estimation of lncRNA gene essentialities with meta-path-guided random walks on the lncRNA-protein interaction network

• Published in: Briefings in bioinformatics Vol. 24; no. 3
• Main Authors: Zhang, Ying-Ying, Liang, De-Min, Du, Pu-Feng
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 19.05.2023; Oxford Publishing Limited (England)
• Subjects: Animals; Breeding success; Cytoskeleton; Functional analysis; Genes; Genomes; Graph neural networks; Humans; Mice; MicroRNAs - metabolism; miRNA; Neoplasms; Neural networks; Neural Networks, Computer; Non-coding RNA; Protein Interaction Maps; Proteins; Random walk; Reproduction; RNA, Long Noncoding - genetics; RNA, Long Noncoding - metabolism; Screening; mete-path-guided random walk; optimal negative samples; lncRNA-protein interaction network; essential genes
• ISSN: 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbad097

Abstract Gene essentiality is defined as the extent to which a gene is required for the survival and reproductive success of a living system. It can vary between genetic backgrounds and environments. Essential protein coding genes have been well studied. However, the essentiality of non-coding regions is rarely reported. Most regions of human genome do not encode proteins. Determining essentialities of non-coding genes is demanded. We developed iEssLnc models, which can assign essentiality scores to lncRNA genes. As far as we know, this is the first direct quantitative estimation to the essentiality of lncRNA genes. By taking the advantage of graph neural network with meta-path-guided random walks on the lncRNA-protein interaction network, iEssLnc models can perform genome-wide screenings for essential lncRNA genes in a quantitative manner. We carried out validations and whole genome screening in the context of human cancer cell-lines and mouse genome. In comparisons to other methods, which are transferred from protein-coding genes, iEssLnc achieved better performances. Enrichment analysis indicated that iEssLnc essentiality scores clustered essential lncRNA genes with high ranks. With the screening results of iEssLnc models, we estimated the number of essential lncRNA genes in human and mouse. We performed functional analysis to find that essential lncRNA genes interact with microRNAs and cytoskeletal proteins significantly, which may be of interest in experimental life sciences. All datasets and codes of iEssLnc models have been deposited in GitHub (https://github.com/yyZhang14/iEssLnc).