Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7424
Title: Quantification of non-coding RNA target localization diversity and its application in cancers Open Access
Authors: Cheng, Lixin 
Prof. LEUNG Kwong Sak 
Issue Date: Apr-2018
Source: Journal of Molecular Cell Biology, April 2018, vol. 10 (2), pp. 130–138
Journal: Journal of Molecular Cell Biology 
Abstract: Subcellular localization is pivotal for RNAs and proteins to implement biological functions. The localization diversity of protein interactions has been studied as a crucial feature of proteins, considering that the protein–protein interactions take place in various subcellular locations. Nevertheless, the localization diversity of non-coding RNA (ncRNA) target proteins has not been systematically studied, especially its characteristics in cancers. In this study, we provide a new algorithm, non-coding RNA target localization coefficient (ncTALENT), to quantify the target localization diversity of ncRNAs based on the ncRNA–protein interaction and protein subcellular localization data. ncTALENT can be used to calculate the target localization coefficient of ncRNAs and measure how diversely their targets are distributed among the subcellular locations in various scenarios. We focus our study on long non-coding RNAs (lncRNAs), and our observations reveal that the target localization diversity is a primary characteristic of lncRNAs in different biotypes. Moreover, we found that lncRNAs in multiple cancers, differentially expressed cancer lncRNAs, and lncRNAs with multiple cancer target proteins are prone to have high target localization diversity. Furthermore, the analysis of gastric cancer helps us to obtain a better understanding that the target localization diversity of lncRNAs is an important feature closely related to clinical prognosis. Overall, we systematically studied the target localization diversity of the lncRNAs and uncovered its association with cancer.
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/7424
ISSN: 1759-4685
DOI: 10.1093/jmcb/mjy006
Appears in Collections:Applied Data Science - Publication

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