Exploration of the omics evidence landscape: adding qualitative labels to predicted protein-protein interactions
- Equal contributors
1 Centre for Molecular and Biomolecular Informatics, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, Toernooiveld, 6525 ED Nijmegen, The Netherlands
2 European Molecular Biology Laboratory, Meyerhofstraße 1, 69117 Heidelberg, Germany
3 Bioinformatics Group, Department of Biology, Science Faculty, Utrecht University, Padualaan, 3584 CH Utrecht, The Netherlands
4 Academic Biomedical Centre, Utrecht University, Yalelaan, 3584 CL Utrecht, The Netherlands
Genome Biology 2007, 8:R197 doi:10.1186/gb-2007-8-9-r197Published: 19 September 2007
In the post-genomic era various functional genomics, proteomics and computational techniques have been developed to elucidate the protein interaction network. While some of these techniques are specific for a certain type of interaction, most predict a mixture of interactions. Qualitative labels are essential for the molecular biologist to experimentally verify predicted interactions.
Of the individual protein-protein interaction prediction methods, some can predict physical interactions without producing other types of interactions. None of the methods can specifically predict metabolic interactions. We have constructed an 'omics evidence landscape' that combines all sources of evidence for protein interactions from various types of omics data for Saccharomyces cerevisiae. We explore this evidence landscape to identify areas with either only metabolic or only physical interactions, allowing us to specifically predict the nature of new interactions in these areas. We combine the datasets in ways that examine the whole evidence landscape and not only the highest scoring protein pairs in both datasets and find specific predictions.
The combination of evidence types in the form of the evidence landscape allows for qualitative labels to be inferred and placed on the predicted protein interaction network of S. cerevisiae. These qualitative labels will help in the biological interpretation of gene networks and will direct experimental verification of the predicted interactions.