title: Computational methods to predict long noncoding RNA functions based on co-expression network.
abstract: Although long noncoding RNAs (lncRNAs) have been recognized in recent years to constitute a significant portion of mammalian transcriptome, and the functional impact of several lncRNAs has been characterized by a few studies, yet it is still difficult to large-scale ascertain their functions from lab experiment or structure prediction. To address this deficit, we have developed a computational pipeline to large-scale annotate the functions of lncRNA based on coding-noncoding gene co-expression network. In this network, a node (circle) represents the protein-coding gene or lncRNA, and an edge (connecting line between nodes) indicates that the two genes are co-expressed (the correlation coefficients of connected genes reached the defined cutoff). In this chapter, we show how to use an lncRNA functional annotation pipeline to construct a co-expression network based on gene expression profiles in prostate cancer and how to further predict lncRNA functions using model-based and hub-based sub-networks.
authors: Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, No. 6 Kexueyuan South Road, Haidian District, Beijing, 100190, People's Republic of China.
authors: Yi
authors: Y
authors: Zhao
authors: Haitao
authors: H
authors: Luo
authors: Xiaowei
authors: X
authors: Chen
authors: Yi
authors: Y
authors: Xiao
authors: Runsheng
authors: R
authors: Chen
mesh_terms: D019295:Computational Biology / Q000379:methods*; D006801:Humans; D008297:Male; D011471:Prostatic Neoplasms / Q000235:genetics; D062085:RNA, Long Noncoding / Q000235:genetics*
chemical_list: D062085:RNA, Long Noncoding