Optimising orbit counting of arbitrary order by equation selection

• Published in: BMC Bioinformatics
• Main Authors: Melckenbeeck, Ine, Audenaert, Pieter, Van Parys, Thomas, Van de Peer, Yves, Colle, Didier, Pickavet, Mario
• Format: Article in Journal/Newspaper
• Language: English
• Published: BioMed Central 2019
• Subjects: Graph theory; Graphlets; Orbits; Equations; Optimisation; Cytoscape app; Orca
• Online Access: http://hdl.handle.net/2263/75137; https://doi.org/10.1186/s12859-018-2483-9

BACKGROUND : Graphlets are useful for bioinformatics network analysis. Based on the structure of Hoˇcevar and Demšar’s ORCA algorithm, we have created an orbit counting algorithm, named Jesse. This algorithm, like ORCA, uses equations to count the orbits, but unlike ORCA it can count graphlets of any order. To do so, it generates the required internal structures and equations automatically. Many more redundant equations are generated, however, and Jesse’s running time is highly dependent on which of these equations are used. Therefore, this paper aims to investigate which equations are most efficient, and which factors have an effect on this efficiency. RESULTS : With appropriate equation selection, Jesse’s running time may be reduced by a factor of up to 2 in the best case, compared to using randomly selected equations. Which equations are most efficient depends on the density of the graph, but barely on the graph type. At low graph density, equations with terms in their right-hand side with few arguments are more efficient, whereas at high density, equations with terms with many arguments in the right-hand side are most efficient. At a density between 0.6 and 0.7, both types of equations are about equally efficient. CONCLUSION : Our Jesse algorithm became up to a factor 2 more efficient, by automatically selecting the best equations based on graph density. It was adapted into a Cytoscape App that is freely available from the Cytoscape App Store to ease application by bioinformaticians. Ghent University – imec and the European Union Seventh Framework Programme (FP7/2007-2013) – European Research Council Advanced Grant Agreement 322739-DOUBLEUP. https://bmcbioinformatics.biomedcentral.com am2020 Biochemistry Genetics Microbiology and Plant Pathology