Abstract:
:More and more evidences demonstrate that the long non-coding RNAs (lncRNAs) play many key roles in diverse biological processes. There is a critical need to annotate the functions of increasing available lncRNAs. In this article, we try to apply a global network-based strategy to tackle this issue for the first time. We develop a bi-colored network based global function predictor, long non-coding RNA global function predictor ('lnc-GFP'), to predict probable functions for lncRNAs at large scale by integrating gene expression data and protein interaction data. The performance of lnc-GFP is evaluated on protein-coding and lncRNA genes. Cross-validation tests on protein-coding genes with known function annotations indicate that our method can achieve a precision up to 95%, with a suitable parameter setting. Among the 1713 lncRNAs in the bi-colored network, the 1625 (94.9%) lncRNAs in the maximum connected component are all functionally characterized. For the lncRNAs expressed in mouse embryo stem cells and neuronal cells, the inferred putative functions by our method highly match those in the known literature.
journal_name
Nucleic Acids Resjournal_title
Nucleic acids researchauthors
Guo X,Gao L,Liao Q,Xiao H,Ma X,Yang X,Luo H,Zhao G,Bu D,Jiao F,Shao Q,Chen R,Zhao Ydoi
10.1093/nar/gks967subject
Has Abstractpub_date
2013-01-01 00:00:00pages
e35issue
2eissn
0305-1048issn
1362-4962pii
gks967journal_volume
41pub_type
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