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Sci Rep. 2016 Sep 14;6:33262. doi: 10.1038/srep33262.

Subpathway-CorSP: Identification of metabolic subpathways via integrating expression correlations and topological features between metabolites and genes of interest within pathways.

Author information

1
School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China.
2
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081,China.
3
School of Nursing, Daqing Campus, Harbin Medical University, Daqing 163319, China.
4
School of Pharmacy, Daqing Campus, Harbin Medical University, Daqing 163319, China.
5
The fifth Affiliated Hospital of Harbin Medical University, Daqing 163319, China.
6
Department of Pharmacology, Daqing Campus, Harbin Medical University, Daqing 163319, China.

Abstract

Metabolic pathway analysis is a popular strategy for comprehensively researching metabolites and genes of interest associated with specific diseases. However, the traditional pathway identification methods do not accurately consider the combined effect of these interesting molecules and neglects expression correlations or topological features embedded in the pathways. In this study, we propose a powerful method, Subpathway-CorSP, for identifying metabolic subpathway regions. This method improved on original pathway identification methods by using a subpathway identification strategy and emphasizing expression correlations between metabolites and genes of interest based on topological features within the metabolic pathways. We analyzed a prostate cancer data set and its metastatic sub-group data set with detailed comparison of Subpathway-CorSP with four traditional pathway identification methods. Subpathway-CorSP was able to identify multiple subpathway regions whose entire corresponding pathways were not detected by traditional pathway identification methods. Further evidences indicated that Subpathway-CorSP provided a robust and efficient way of reliably recalling cancer-related subpathways and locating novel subpathways by the combined effect of metabolites and genes. This was a novel subpathway strategy based on systematically considering expression correlations and topological features between metabolites and genes of interest within given pathways.

PMID:
27625019
PMCID:
PMC5021946
DOI:
10.1038/srep33262
[Indexed for MEDLINE]
Free PMC Article

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