Journal Article

Pathway discovery in metabolic networks by subgraph extraction

Karoline Faust, Pierre Dupont, Jérôme Callut and Jacques van Helden

in Bioinformatics

Volume 26, issue 9, pages 1211-1218
Published in print May 2010 | ISSN: 1367-4803
Published online March 2010 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btq105

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Motivation: Subgraph extraction is a powerful technique to predict pathways from biological networks and a set of query items (e.g. genes, proteins, compounds, etc.). It can be applied to a variety of different data types, such as gene expression, protein levels, operons or phylogenetic profiles. In this article, we investigate different approaches to extract relevant pathways from metabolic networks. Although these approaches have been adapted to metabolic networks, they are generic enough to be adjusted to other biological networks as well.

Results: We comparatively evaluated seven sub-network extraction approaches on 71 known metabolic pathways from Saccharomyces cerevisiae and a metabolic network obtained from MetaCyc. The best performing approach is a novel hybrid strategy, which combines a random walk-based reduction of the graph with a shortest paths-based algorithm, and which recovers the reference pathways with an accuracy of ∼77%.

Availability: Most of the presented algorithms are available as part of the network analysis tool set (NeAT). The kWalks method is released under the GPL3 license.

Contact: kfaust@ulb.ac.be

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  6211 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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