Open AccessLncRNAs2Pathways: Identifying the pathways influenced by a set of lncRNAs of interest based on a global network propagation method
Author(s) -
Junwei Han,
Siyao Liu,
Zeguo Sun,
Yunpeng Zhang,
Fan Zhang,
Chunlong Zhang,
Desi Shang,
Haixiu Yang,
Fei Su,
Yanjun Xu,
Chunquan Li,
Huan Ren,
Xia Li
Publication year - 2017
Publication title -
scientific reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 213
ISSN - 2045-2322
DOI - 10.1038/srep46566
Subject(s) - computer science , biological network , robustness (evolution) , biological pathway , computational biology , set (abstract data type) , systems biology , bioinformatics , biology , gene , gene expression , genetics , programming language
Long non-coding RNAs (lncRNAs) have been demonstrated to play essential roles in diverse cellular processes and biological functions. Exploring the functions associated with lncRNAs may help provide insight into their underlying biological mechanisms. The current methods primarily focus on investigating the functions of individual lncRNAs; however, essential biological functions may be affected by the combinatorial effects of multiple lncRNAs. Here, we have developed a novel computational method, LncRNAs2Pathways, to identify the functional pathways influenced by the combinatorial effects of a set of lncRNAs of interest based on a global network propagation algorithm. A new Kolmogorov–Smirnov-like statistical measure weighted by the network propagation score, which considers the expression correlation among lncRNAs and coding genes, was used to evaluate the biological pathways influenced by the lncRNAs of interest. We have described the LncRNAs2Pathways methodology and illustrated its effectiveness by analyzing three lncRNA sets associated with glioma, prostate and pancreatic cancers. We further analyzed the reproducibility and robustness and compared our results with those of two other methods. Based on these analyses, we showed that LncRNAs2Pathways can effectively identify the functional pathways associated with lncRNA sets. Finally, we implemented this method as a freely available R-based tool.