Filtering high-throughput protein-protein interaction data using a combination of genomic features.
BACKGROUND
Protein-protein interaction data used in the creation or prediction of molecular networks is usually obtained from large scale or high-throughput experiments. This experimental data is liable to contain a large number of spurious interactions. Hence, there is a need to validate the interactions and filter out the incorrect data before using them in prediction studies.


RESULTS
In this study, we use a combination of 3 genomic features -- structurally known interacting Pfam domains, Gene Ontology annotations and sequence homology -- as a means to assign reliability to the protein-protein interactions in Saccharomyces cerevisiae determined by high-throughput experiments. Using Bayesian network approaches, we show that protein-protein interactions from high-throughput data supported by one or more genomic features have a higher likelihood ratio and hence are more likely to be real interactions. Our method has a high sensitivity (90%) and good specificity (63%). We show that 56% of the interactions from high-throughput experiments in Saccharomyces cerevisiae have high reliability. We use the method to estimate the number of true interactions in the high-throughput protein-protein interaction data sets in Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens to be 27%, 18% and 68% respectively. Our results are available for searching and downloading at http://helix.protein.osaka-u.ac.jp/htp/.


CONCLUSIONS
A combination of genomic features that include sequence, structure and annotation information is a good predictor of true interactions in large and noisy high-throughput data sets. The method has a very high sensitivity and good specificity and can be used to assign a likelihood ratio, corresponding to the reliability, to each interaction.
Patil
Ashwini
A
Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan. ashwini@protein.osaka-u.ac.jp
Nakamura
Haruki
H
D000818:Animals; D001331:Automation; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D030562:Databases, Protein; D004331:Drosophila melanogaster; D020869:Gene Expression Profiling; D015966:Gene Expression Regulation, Fungal; D005800:Genes, Fungal / Q000235:genetics*; D016678:Genome; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D011485:Protein Binding; D017510:Protein Folding; D025941:Protein Interaction Mapping; D040901:Proteomics; D012372:ROC Curve; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000235:genetics*; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics*; D012680:Sensitivity and Specificity; D012984:Software; D012985:Software Design; D014157:Transcription Factors
D029701:Saccharomyces cerevisiae Proteins; D014157:Transcription Factors