Journal Article

Pandora, a PAthway and Network DiscOveRy Approach based on common biological evidence

Kelvin Xi Zhang and B. F. Francis Ouellette

in Bioinformatics

Volume 26, issue 4, pages 529-535
Published in print February 2010 | ISSN: 1367-4803
Published online December 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp701

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Motivation: Many biological phenomena involve extensive interactions between many of the biological pathways present in cells. However, extraction of all the inherent biological pathways remains a major challenge in systems biology. With the advent of high-throughput functional genomic techniques, it is now possible to infer biological pathways and pathway organization in a systematic way by integrating disparate biological information.

Results: Here, we propose a novel integrated approach that uses network topology to predict biological pathways. We integrated four types of biological evidence (protein–protein interaction, genetic interaction, domain–domain interaction and semantic similarity of Gene Ontology terms) to generate a functionally associated network. This network was then used to develop a new pathway finding algorithm to predict biological pathways in yeast. Our approach discovered 195 biological pathways and 31 functionally redundant pathway pairs in yeast. By comparing our identified pathways to three public pathway databases (KEGG, BioCyc and Reactome), we observed that our approach achieves a maximum positive predictive value of 12.8% and improves on other predictive approaches. This study allows us to reconstruct biological pathways and delineates cellular machinery in a systematic view.

Availability: The method has been implemented in Perl and is available for downloading from http://www.oicr.on.ca/research/ouellette/pandora. It is distributed under the terms of GPL (http://opensource.org/licenses/gpl-2.0.php)

Contact: francis@oicr.on.ca

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5719 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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