Dataset Information

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

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@bioinf.uni-sb.de

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Keller A

PROVIDER: S-EPMC2781748 | biostudies-literature | 2009 Nov

REPOSITORIES: biostudies-literature

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Publications

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

Keller Andreas A Backes Christina C Gerasch Andreas A Kaufmann Michael M Kohlbacher Oliver O Meese Eckart E Lenhof Hans-Peter HP

Bioinformatics (Oxford, England) 20090827 21

<h4>Motivation</h4>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.<h4>Results</h4>We present ...[more]

PMID: 19713416

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Dataset Information

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

Motivation

Results

Availability

Contact

Supplementary information

Publications

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

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