Subpathway-GMir: identifying miRNA-mediated metabolic subpathways by integrating condition-specific genes, microRNAs, and pathway topologies
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Li Feng1,*, Yanjun Xu1,*, Yunpeng Zhang1,*, Zeguo Sun1, Junwei Han1, Chunlong Zhang1, Haixiu Yang1, Desi Shang1, Fei Su1, Xinrui Shi1, Shang Li1, Chunquan Li1,2, Xia Li1
1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
2Department of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China
*These authors have contributed equally to this work
Xia Li, e-mail: firstname.lastname@example.org
Chunquan Li, e-mail: email@example.com
Keywords: microRNA, mRNA, cancer, pathway identification, regulation
Received: June 09, 2015 Accepted: October 02, 2015 Published: October 12, 2015
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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