Subpathway-GMir: identifying miRNA-mediated metabolic subpathways by integrating condition-specific genes, microRNAs, and pathway topologies
Author(s) -
Feng Li,
Yanjun Xu,
Yunpeng Zhang,
Zeguo Sun,
Junwei Han,
Chunlong Zhang,
Haixiu Yang,
Desi Shang,
Fei Su,
Xinrui Shi,
Shang Li,
Chunquan Li,
Xia Li
Publication year - 2015
Publication title -
oncotarget
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.373
H-Index - 127
ISSN - 1949-2553
DOI - 10.18632/oncotarget.5341
Subject(s) - microrna , computational biology , bioinformatics , gene , medicine , biology , genetics
MicroRNAs (miRNAs) regulate disease-relevant metabolic pathways. However, most current pathway identification methods fail to consider miRNAs in addition to genes when analyzing pathways. We developed a powerful method called Subpathway-GMir to construct miRNA-regulated metabolic pathways and to identify miRNA-mediated subpathways by considering condition-specific genes, miRNAs, and pathway topologies. We used Subpathway-GMir to analyze two liver hepatocellular carcinomas (LIHC), one stomach adenocarcinoma (STAD), and one type 2 diabetes (T2D) data sets. Results indicate that Subpathway-GMir is more effective in identifying phenotype-associated metabolic pathways than other methods and our results are reproducible and robust. Subpathway-GMir provides a flexible platform for identifying abnormal metabolic subpathways mediated by miRNAs, and may help to clarify the roles that miRNAs play in a variety of diseases. The Subpathway-GMir method has been implemented as a freely available R package.
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