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DEGAS: de novo discovery of dysregulated pathways in human diseases.

Ulitsky I, Krishnamurthy A, Karp RM, Shamir R - PLoS ONE (2010)

Bottom Line: Recent studies have found that only a small number of the genes in human disease-related pathways show consistent dysregulation in sick individuals.However, those studies found that some pathway genes are affected in most sick individuals, but genes can differ among individuals.We applied DEGAS to seven human diseases and obtained statistically significant results that appear to home in on compact pathways enriched with hallmarks of the diseases.

View Article: PubMed Central - PubMed

Affiliation: Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel. ulitsky@wi.mit.edu

ABSTRACT

Background: Molecular studies of the human disease transcriptome typically involve a search for genes whose expression is significantly dysregulated in sick individuals compared to healthy controls. Recent studies have found that only a small number of the genes in human disease-related pathways show consistent dysregulation in sick individuals. However, those studies found that some pathway genes are affected in most sick individuals, but genes can differ among individuals. While a pathway is usually defined as a set of genes known to share a specific function, pathway boundaries are frequently difficult to assign, and methods that rely on such definition cannot discover novel pathways. Protein interaction networks can potentially be used to overcome these problems.

Methodology/principal findings: We present DEGAS (DysrEgulated Gene set Analysis via Subnetworks), a method for identifying connected gene subnetworks significantly enriched for genes that are dysregulated in specimens of a disease. We applied DEGAS to seven human diseases and obtained statistically significant results that appear to home in on compact pathways enriched with hallmarks of the diseases. In Parkinson's disease, we provide novel evidence for involvement of mRNA splicing, cell proliferation, and the 14-3-3 complex in the disease progression. DEGAS is available as part of the MATISSE software package (http://acgt.cs.tau.ac.il/matisse).

Conclusions/significance: The subnetworks identified by DEGAS can provide a signature of the disease potentially useful for diagnosis, pinpoint possible pathways affected by the disease, and suggest targets for drug intervention.

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Related in: MedlinePlus

DEGAS outline.All the nodes in the network are tested as potential root nodes for a minimal radius DP. For each node, we efficiently compute the smallest radius for which some DP exists in the r-neighborhood of the node. All the nodes for which this radius is minimal are used to construct DP using the ExpandingGreedy heuristic (see Methods). The smallest DPs identified over all the tested roots are reported.
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pone-0013367-g002: DEGAS outline.All the nodes in the network are tested as potential root nodes for a minimal radius DP. For each node, we efficiently compute the smallest radius for which some DP exists in the r-neighborhood of the node. All the nodes for which this radius is minimal are used to construct DP using the ExpandingGreedy heuristic (see Methods). The smallest DPs identified over all the tested roots are reported.

Mentions: Our initial results have shown that, in this basic formulation, small DPs frequently correspond to sparse subnetworks that were frequently not biologically relevant (results not shown). We adjusted our problem formulation accordingly, and focused on identifying DPs that are not only small, but also have the smallest possible radius – i.e., all the nodes in a DP are within a short distance from some root node (Figure 2, see Methods).


DEGAS: de novo discovery of dysregulated pathways in human diseases.

Ulitsky I, Krishnamurthy A, Karp RM, Shamir R - PLoS ONE (2010)

DEGAS outline.All the nodes in the network are tested as potential root nodes for a minimal radius DP. For each node, we efficiently compute the smallest radius for which some DP exists in the r-neighborhood of the node. All the nodes for which this radius is minimal are used to construct DP using the ExpandingGreedy heuristic (see Methods). The smallest DPs identified over all the tested roots are reported.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2957424&req=5

pone-0013367-g002: DEGAS outline.All the nodes in the network are tested as potential root nodes for a minimal radius DP. For each node, we efficiently compute the smallest radius for which some DP exists in the r-neighborhood of the node. All the nodes for which this radius is minimal are used to construct DP using the ExpandingGreedy heuristic (see Methods). The smallest DPs identified over all the tested roots are reported.
Mentions: Our initial results have shown that, in this basic formulation, small DPs frequently correspond to sparse subnetworks that were frequently not biologically relevant (results not shown). We adjusted our problem formulation accordingly, and focused on identifying DPs that are not only small, but also have the smallest possible radius – i.e., all the nodes in a DP are within a short distance from some root node (Figure 2, see Methods).

Bottom Line: Recent studies have found that only a small number of the genes in human disease-related pathways show consistent dysregulation in sick individuals.However, those studies found that some pathway genes are affected in most sick individuals, but genes can differ among individuals.We applied DEGAS to seven human diseases and obtained statistically significant results that appear to home in on compact pathways enriched with hallmarks of the diseases.

View Article: PubMed Central - PubMed

Affiliation: Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel. ulitsky@wi.mit.edu

ABSTRACT

Background: Molecular studies of the human disease transcriptome typically involve a search for genes whose expression is significantly dysregulated in sick individuals compared to healthy controls. Recent studies have found that only a small number of the genes in human disease-related pathways show consistent dysregulation in sick individuals. However, those studies found that some pathway genes are affected in most sick individuals, but genes can differ among individuals. While a pathway is usually defined as a set of genes known to share a specific function, pathway boundaries are frequently difficult to assign, and methods that rely on such definition cannot discover novel pathways. Protein interaction networks can potentially be used to overcome these problems.

Methodology/principal findings: We present DEGAS (DysrEgulated Gene set Analysis via Subnetworks), a method for identifying connected gene subnetworks significantly enriched for genes that are dysregulated in specimens of a disease. We applied DEGAS to seven human diseases and obtained statistically significant results that appear to home in on compact pathways enriched with hallmarks of the diseases. In Parkinson's disease, we provide novel evidence for involvement of mRNA splicing, cell proliferation, and the 14-3-3 complex in the disease progression. DEGAS is available as part of the MATISSE software package (http://acgt.cs.tau.ac.il/matisse).

Conclusions/significance: The subnetworks identified by DEGAS can provide a signature of the disease potentially useful for diagnosis, pinpoint possible pathways affected by the disease, and suggest targets for drug intervention.

Show MeSH
Related in: MedlinePlus