SWIFTCORE: a tool for the context-specific reconstruction of genome-scale metabolic networks

High-throughput omics technologies have enabled the comprehensive reconstructions of genome-scale metabolic networks for many organisms. However, only a subset of reactions is active in each cell which differs from tissue to tissue or from patient to patient. Reconstructing a subnetwork of the gener...

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Bibliographic Details
Published in:BMC bioinformatics 2020-04, Vol.21 (1), p.140-140, Article 140
Main Authors: Tefagh, Mojtaba, Boyd, Stephen P
Format: Article
Language:English
Subjects:
Algorithms
Computer applications
Context
Context-specific models
Gene expression
Genomes
Genomics
Greedy algorithms
Metabolic network analysis
Metabolic network reconstruction
Metabolic networks
Metabolism
Metabolites
Networks
Reconstruction
Software
Systems biology
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Summary:High-throughput omics technologies have enabled the comprehensive reconstructions of genome-scale metabolic networks for many organisms. However, only a subset of reactions is active in each cell which differs from tissue to tissue or from patient to patient. Reconstructing a subnetwork of the generic metabolic network from a provided set of context-specific active reactions is a demanding computational task. We propose SWIFTCC and SWIFTCORE as effective methods for flux consistency checking and the context-specific reconstruction of genome-scale metabolic networks which consistently outperform the previous approaches. We have derived an approximate greedy algorithm which efficiently scales to increasingly large metabolic networks. SWIFTCORE is freely available for non-commercial use in the GitHub repository at https://mtefagh.github.io/swiftcore/.
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-020-3440-y