AMBIENT: Active Modules for Bipartite Networks - using high-throughput transcriptomic data to dissect metabolic response View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2013-12

AUTHORS

William A Bryant, Michael JE Sternberg, John W Pinney

ABSTRACT

BACKGROUND: With the continued proliferation of high-throughput biological experiments, there is a pressing need for tools to integrate the data produced in ways that produce biologically meaningful conclusions. Many microarray studies have analysed transcriptomic data from a pathway perspective, for instance by testing for KEGG pathway enrichment in sets of upregulated genes. However, the increasing availability of species-specific metabolic models provides the opportunity to analyse these data in a more objective, system-wide manner. RESULTS: Here we introduce ambient (Active Modules for Bipartite Networks), a simulated annealing approach to the discovery of metabolic subnetworks (modules) that are significantly affected by a given genetic or environmental change. The metabolic modules returned by ambient are connected parts of the bipartite network that change coherently between conditions, providing a more detailed view of metabolic changes than standard approaches based on pathway enrichment. CONCLUSIONS: ambient is an effective and flexible tool for the analysis of high-throughput data in a metabolic context. The same approach can be applied to any system in which reactions (or metabolites) can be assigned a score based on some biological observation, without the limitation of predefined pathways. A Python implementation of ambient is available at http://www.theosysbio.bio.ic.ac.uk/ambient. More... »

PAGES

26

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1752-0509-7-26

DOI

http://dx.doi.org/10.1186/1752-0509-7-26

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1051466576

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/23531303


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