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Bioinformatics Advance Access originally published online on August 27, 2009
Bioinformatics 2009 25(21):2787-2794; doi:10.1093/bioinformatics/btp510
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© The Author(s) 2009. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

A novel algorithm for detecting differentially regulated paths based on gene set enrichment analysis

Andreas Keller 1,2,*, Christina Backes 1, Andreas Gerasch 3, Michael Kaufmann 3, Oliver Kohlbacher 3, Eckart Meese 4 and Hans-Peter Lenhof 1

1 Center for Bioinformatics, Saarland University, Building E.1.1, Saarbrücken, 2 febit biomed gmbh, Im Neuenheimer Feld 519, Heidelberg, 3 Wilhelm Schickard Institute for Computer Sciences, Eberhard Karls University Tübingen and 4 Department of Human Genetics, Saarland University, Building 60, Homburg, Germany

* To whom correspondence should be addressed.


  Abstract

Motivation: Deregulated signaling cascades are known to play a crucial role in many pathogenic processes, among them are tumor initiation and progression. In the recent past, modern experimental techniques that allow for measuring the amount of mRNA transcripts of almost all known human genes in a tissue or even in a single cell have opened new avenues for studying the activity of the signaling cascades and for understanding the information flow in the networks.

Results: We present a novel dynamic programming algorithm for detecting deregulated signaling cascades. The so-called FiDePa (Finding Deregulated Paths) algorithm interprets differences in the expression profiles of tumor and normal tissues. It relies on the well-known gene set enrichment analysis (GSEA) and efficiently detects all paths in a given regulatory or signaling network that are significantly enriched with differentially expressed genes or proteins. Since our algorithm allows for comparing a single tumor expression profile with the control group, it facilitates the detection of specific regulatory features of a tumor that may help to optimize tumor therapy. To demonstrate the capabilities of our algorithm, we analyzed a glioma expression dataset with respect to a directed graph that combined the regulatory networks of the KEGG and TRANSPATH database. The resulting glioma consensus network that encompasses all detected deregulated paths contained many genes and pathways that are known to be key players in glioma or cancer-related pathogenic processes. Moreover, we were able to correlate clinically relevant features like necrosis or metastasis with the detected paths.

Availability: C++ source code is freely available, BiNA can be downloaded from http://www.bnplusplus.org/.

Contact: ack{at}bioinf.uni-sb.de

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Olga Troyanskaya

Received on April 8, 2009; revised on August 3, 2009; accepted on August 17, 2009

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