Limits...
PathFinder: mining signal transduction pathway segments from protein-protein interaction networks.

Bebek G, Yang J - BMC Bioinformatics (2007)

Bottom Line: Our goal is to find biologically significant pathway segments in a given interaction network.Given a pair of starting and ending proteins, our methodology returns candidate pathway segments between these two proteins with possible missing links (recovered false negatives).In our study, S. cerevisiae (yeast) data is used to demonstrate the effectiveness of our method.

View Article: PubMed Central - HTML - PubMed

Affiliation: EECS Department, Case Western Reserve University, Cleveland, OH 44106, USA. gurkan.bebek@case.edu

ABSTRACT

Background: A Signal transduction pathway is the chain of processes by which a cell converts an extracellular signal into a response. In most unicellular organisms, the number of signal transduction pathways influences the number of ways the cell can react and respond to the environment. Discovering signal transduction pathways is an arduous problem, even with the use of systematic genomic, proteomic and metabolomic technologies. These techniques lead to an enormous amount of data and how to interpret and process this data becomes a challenging computational problem.

Results: In this study we present a new framework for identifying signaling pathways in protein-protein interaction networks. Our goal is to find biologically significant pathway segments in a given interaction network. Currently, protein-protein interaction data has excessive amount of noise, e.g., false positive and false negative interactions. First, we eliminate false positives in the protein-protein interaction network by integrating the network with microarray expression profiles, protein subcellular localization and sequence information. In addition, protein families are used to repair false negative interactions. Then the characteristics of known signal transduction pathways and their functional annotations are extracted in the form of association rules.

Conclusion: Given a pair of starting and ending proteins, our methodology returns candidate pathway segments between these two proteins with possible missing links (recovered false negatives). In our study, S. cerevisiae (yeast) data is used to demonstrate the effectiveness of our method.

Show MeSH
The filamentation signaling pathway. (A) The main chain of the filamentation pathway (KEGG Database), (B) PathFinder output for the Sho1-Tec1 pair (75% recall, 17% precision). For (A), the dashed interactions indicate that these interactions do not exists in the database. For (B), the proteins that were not on the main chain of the pathway were not colored, whereas the proteins on the main chain are colored grey. Proteins that are part of other pathways are colored with different colors.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2100073&req=5

Figure 5: The filamentation signaling pathway. (A) The main chain of the filamentation pathway (KEGG Database), (B) PathFinder output for the Sho1-Tec1 pair (75% recall, 17% precision). For (A), the dashed interactions indicate that these interactions do not exists in the database. For (B), the proteins that were not on the main chain of the pathway were not colored, whereas the proteins on the main chain are colored grey. Proteins that are part of other pathways are colored with different colors.

Mentions: In this second example, the filamentation MAPK pathway is searched. The filamentation MAPK pathway (Figure 5A) is activated by glucose or nitrogen starvation and results in filamentous growth. The Sho1-Tec1 protein pair is picked as the starting and ending protein pair. However, this pathway has a missing interaction in the yeast PPIN. Previously, such missing links were noted to prevent attempts to recover signaling pathways segments [18]. After searching for the pathway with our methodology (without inferred links), we acquired the pathway segment shown in Figure 5B. When the results are compared, all known interactions among the interacting proteins but Cdc42 were recovered. Additional proteins from the pheromone response and high osmolarity glycerol (HOG) MAPK pathways were also recovered. This is most likely due to shared proteins on these pathways.


PathFinder: mining signal transduction pathway segments from protein-protein interaction networks.

Bebek G, Yang J - BMC Bioinformatics (2007)

The filamentation signaling pathway. (A) The main chain of the filamentation pathway (KEGG Database), (B) PathFinder output for the Sho1-Tec1 pair (75% recall, 17% precision). For (A), the dashed interactions indicate that these interactions do not exists in the database. For (B), the proteins that were not on the main chain of the pathway were not colored, whereas the proteins on the main chain are colored grey. Proteins that are part of other pathways are colored with different colors.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2100073&req=5

Figure 5: The filamentation signaling pathway. (A) The main chain of the filamentation pathway (KEGG Database), (B) PathFinder output for the Sho1-Tec1 pair (75% recall, 17% precision). For (A), the dashed interactions indicate that these interactions do not exists in the database. For (B), the proteins that were not on the main chain of the pathway were not colored, whereas the proteins on the main chain are colored grey. Proteins that are part of other pathways are colored with different colors.
Mentions: In this second example, the filamentation MAPK pathway is searched. The filamentation MAPK pathway (Figure 5A) is activated by glucose or nitrogen starvation and results in filamentous growth. The Sho1-Tec1 protein pair is picked as the starting and ending protein pair. However, this pathway has a missing interaction in the yeast PPIN. Previously, such missing links were noted to prevent attempts to recover signaling pathways segments [18]. After searching for the pathway with our methodology (without inferred links), we acquired the pathway segment shown in Figure 5B. When the results are compared, all known interactions among the interacting proteins but Cdc42 were recovered. Additional proteins from the pheromone response and high osmolarity glycerol (HOG) MAPK pathways were also recovered. This is most likely due to shared proteins on these pathways.

Bottom Line: Our goal is to find biologically significant pathway segments in a given interaction network.Given a pair of starting and ending proteins, our methodology returns candidate pathway segments between these two proteins with possible missing links (recovered false negatives).In our study, S. cerevisiae (yeast) data is used to demonstrate the effectiveness of our method.

View Article: PubMed Central - HTML - PubMed

Affiliation: EECS Department, Case Western Reserve University, Cleveland, OH 44106, USA. gurkan.bebek@case.edu

ABSTRACT

Background: A Signal transduction pathway is the chain of processes by which a cell converts an extracellular signal into a response. In most unicellular organisms, the number of signal transduction pathways influences the number of ways the cell can react and respond to the environment. Discovering signal transduction pathways is an arduous problem, even with the use of systematic genomic, proteomic and metabolomic technologies. These techniques lead to an enormous amount of data and how to interpret and process this data becomes a challenging computational problem.

Results: In this study we present a new framework for identifying signaling pathways in protein-protein interaction networks. Our goal is to find biologically significant pathway segments in a given interaction network. Currently, protein-protein interaction data has excessive amount of noise, e.g., false positive and false negative interactions. First, we eliminate false positives in the protein-protein interaction network by integrating the network with microarray expression profiles, protein subcellular localization and sequence information. In addition, protein families are used to repair false negative interactions. Then the characteristics of known signal transduction pathways and their functional annotations are extracted in the form of association rules.

Conclusion: Given a pair of starting and ending proteins, our methodology returns candidate pathway segments between these two proteins with possible missing links (recovered false negatives). In our study, S. cerevisiae (yeast) data is used to demonstrate the effectiveness of our method.

Show MeSH