False Annotations of Proteins: Automatic Detection via Keyword-Based Clustering

Computational protein annotation methods occasionally introduce errors. False-positive (FP) errors are annotations that are mistakenly associated with a protein. Such false annotations introduce errors that may spread into databases through similarity with other proteins. We present a protein-clustering method that enables automatic separation of FP from true-positive hits. The method is based on the combination of each protein's annotations. Using a test set of all PROSITE signatures that are marked as FPs, we show that the method successfully separates FPs in 70% of the cases. Automatic detection of FPs may greatly facilitate the manual validation process and increase annotation sensitivity.