Long Non-coding RNAs Function Annotation: a Global Prediction Method Based on Bi-colored Networks

Overview

Journal Nucleic Acids Res

Publisher Oxford University Press

Specialty Biochemistry

Date 2012 Nov 8

PMID 23132350

Citations 106

Authors

Xingli Guo

Lin Gao

Qi Liao

Hui Xiao

Xiaoke Ma

Xiaofei Yang

Haitao Luo

Guoguang Zhao

Dechao Bu

Fei Jiao

Qixiang Shao

Runsheng Chen

Yi Zhao

Affiliations

Soon will be listed here.

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.

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