Algorithms for effective querying of compound graph-based pathway databases.

BMC Bioinformatics
Ugur DogrusozOzgun Babur

Abstract

Graph-based pathway ontologies and databases are widely used to represent data about cellular processes. This representation makes it possible to programmatically integrate cellular networks and to investigate them using the well-understood concepts of graph theory in order to predict their structural and dynamic properties. An extension of this graph representation, namely hierarchically structured or compound graphs, in which a member of a biological network may recursively contain a sub-network of a somehow logically similar group of biological objects, provides many additional benefits for analysis of biological pathways, including reduction of complexity by decomposition into distinct components or modules. In this regard, it is essential to effectively query such integrated large compound networks to extract the sub-networks of interest with the help of efficient algorithms and software tools. Towards this goal, we developed a querying framework, along with a number of graph-theoretic algorithms from simple neighborhood queries to shortest paths to feedback loops, that is applicable to all sorts of graph-based pathway databases, from PPIs (protein-protein interactions) to metabolic and signaling pathways. The framework is...Continue Reading

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Citations

Nov 13, 2010·Nucleic Acids Research·Ethan G CeramiChris Sander
May 15, 2012·BMC Bioinformatics·Sunisa ChatsurachaiHiroshi Shimizu
Sep 27, 2013·PLoS Computational Biology·Emek DemirChris Sander
Apr 5, 2012·Autoimmune Diseases·Ricardo A CifuentesJuan-Manuel Anaya
Aug 17, 2014·BMC Bioinformatics·Dominik SchweigerStephan Pabinger
Dec 7, 2016·BMC Bioinformatics·Vasundra TouréCharles Auffray
Oct 11, 2017·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Anna RitzT M Murali
Apr 25, 2018·Briefings in Bioinformatics·Marek OstaszewskiReinhard Schneider
Oct 28, 2019·Nucleic Acids Research·Igor RodchenkovChris Sander
Oct 28, 2019·PLoS Computational Biology·Nicholas FranzeseAnna Ritz

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