Cluster-based assessment of protein-protein interaction confidence
<p>Abstract</p> <p>Background</p> <p>Protein-protein interaction networks are key to a systems-level understanding of cellular biology. However, interaction data can contain a considerable fraction of false positives. Several methods have been proposed to assess the con...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2012-10-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/13/262 |
| _version_ | 1811329292400852992 |
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| author | Kamburov Atanas Grossmann Arndt Herwig Ralf Stelzl Ulrich |
| author_facet | Kamburov Atanas Grossmann Arndt Herwig Ralf Stelzl Ulrich |
| author_sort | Kamburov Atanas |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Protein-protein interaction networks are key to a systems-level understanding of cellular biology. However, interaction data can contain a considerable fraction of false positives. Several methods have been proposed to assess the confidence of individual interactions. Most of them require the integration of additional data like protein expression and interaction homology information. While being certainly useful, such additional data are not always available and may introduce additional bias and ambiguity.</p> <p>Results</p> <p>We propose a novel, network topology based interaction confidence assessment method called CAPPIC (cluster-based assessment of protein-protein interaction confidence). It exploits the network’s inherent modular architecture for assessing the confidence of individual interactions. Our method determines algorithmic parameters intrinsically and does not require any parameter input or reference sets for confidence scoring.</p> <p>Conclusions</p> <p>On the basis of five yeast and two human physical interactome maps inferred using different techniques, we show that CAPPIC reliably assesses interaction confidence and its performance compares well to other approaches that are also based on network topology. The confidence score correlates with the agreement in localization and biological process annotations of interacting proteins. Moreover, it corroborates experimental evidence of physical interactions. Our method is not limited to physical interactome maps as we exemplify with a large yeast genetic interaction network. An implementation of CAPPIC is available at <url>http://intscore.molgen.mpg.de.</url></p> |
| first_indexed | 2024-04-13T15:41:14Z |
| format | Article |
| id | doaj.art-d0c769f54d3741e6ace9dc4ef8ca55e8 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-13T15:41:14Z |
| publishDate | 2012-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-d0c769f54d3741e6ace9dc4ef8ca55e82022-12-22T02:41:08ZengBMCBMC Bioinformatics1471-21052012-10-0113126210.1186/1471-2105-13-262Cluster-based assessment of protein-protein interaction confidenceKamburov AtanasGrossmann ArndtHerwig RalfStelzl Ulrich<p>Abstract</p> <p>Background</p> <p>Protein-protein interaction networks are key to a systems-level understanding of cellular biology. However, interaction data can contain a considerable fraction of false positives. Several methods have been proposed to assess the confidence of individual interactions. Most of them require the integration of additional data like protein expression and interaction homology information. While being certainly useful, such additional data are not always available and may introduce additional bias and ambiguity.</p> <p>Results</p> <p>We propose a novel, network topology based interaction confidence assessment method called CAPPIC (cluster-based assessment of protein-protein interaction confidence). It exploits the network’s inherent modular architecture for assessing the confidence of individual interactions. Our method determines algorithmic parameters intrinsically and does not require any parameter input or reference sets for confidence scoring.</p> <p>Conclusions</p> <p>On the basis of five yeast and two human physical interactome maps inferred using different techniques, we show that CAPPIC reliably assesses interaction confidence and its performance compares well to other approaches that are also based on network topology. The confidence score correlates with the agreement in localization and biological process annotations of interacting proteins. Moreover, it corroborates experimental evidence of physical interactions. Our method is not limited to physical interactome maps as we exemplify with a large yeast genetic interaction network. An implementation of CAPPIC is available at <url>http://intscore.molgen.mpg.de.</url></p>http://www.biomedcentral.com/1471-2105/13/262 |
| spellingShingle | Kamburov Atanas Grossmann Arndt Herwig Ralf Stelzl Ulrich Cluster-based assessment of protein-protein interaction confidence BMC Bioinformatics |
| title | Cluster-based assessment of protein-protein interaction confidence |
| title_full | Cluster-based assessment of protein-protein interaction confidence |
| title_fullStr | Cluster-based assessment of protein-protein interaction confidence |
| title_full_unstemmed | Cluster-based assessment of protein-protein interaction confidence |
| title_short | Cluster-based assessment of protein-protein interaction confidence |
| title_sort | cluster based assessment of protein protein interaction confidence |
| url | http://www.biomedcentral.com/1471-2105/13/262 |
| work_keys_str_mv | AT kamburovatanas clusterbasedassessmentofproteinproteininteractionconfidence AT grossmannarndt clusterbasedassessmentofproteinproteininteractionconfidence AT herwigralf clusterbasedassessmentofproteinproteininteractionconfidence AT stelzlulrich clusterbasedassessmentofproteinproteininteractionconfidence |