An algorithm for extracting subgraph of specific species from metabolic pathway
Jianbang Zhao, Lin Gao
Abstract
Jianbang Zhao, Lin Gao
Abstract
A large number of metabolic pathway databases are currently available, such as KEGG, EcoCys, and BioPath. For a better use of the KEGG databases, studying the KEGG data structure and rebuilding it into a convenient form become crucial tasks to our research, such as functional modularity detection, conserved pathway analysis, phylogenetic analysis. This paper presents an algorithm for extracting the metabolic pathways from KEGG database to the form of enzyme-enzyme interactions (EEI) and compound-compound ones. Using the algorithm EMP (Extract Metabolic Pathway), we can transform a specific species metabolic pathway in KEGG into a subgraph which consists of EEI edges. Additionally, we provide a tool named ExtKEGG to extract compound-based metabolic pathways. Furthermore, the experimental results show that our algorithm causes no information loss.
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A large number of metabolic pathway databases are currently available, such as KEGG, EcoCys, and BioPath. For a better use of the KEGG databases, studying the KEGG data structure and rebuilding it into a convenient form become crucial tasks to our research, such as functional modularity detection, conserved pathway analysis, phylogenetic analysis. This paper presents an algorithm for extracting the metabolic pathways from KEGG database to the form of enzyme-enzyme interactions (EEI) and compound-compound ones. Using the algorithm EMP (Extract Metabolic Pathway), we can transform a specific species metabolic pathway in KEGG into a subgraph which consists of EEI edges. Additionally, we provide a tool named ExtKEGG to extract compound-based metabolic pathways. Furthermore, the experimental results show that our algorithm causes no information loss.
Key concepts: KEGG, Metabolic pathway, Computer science, Modularity (biology), Computational biology, Data mining, Enzyme, Biology