Statistically rigorous automated protein annotation

atics.oxfordjournals.org/ D ow nloaded from-2- Krebs and Bourne Motivation: Assignment of putative protein functional annotation by comparative analysis using pre-defined experimental annotations is performed routinely by molecular biologists. The number and statistical significance of these assignments remains a challenge in this era of high-throughput proteomics. A combined statistical method that enables robust, automated protein annotation by reliably expanding existing annotation sets is described. An existing clustering scheme, based on relevant experimental information (e.g., sequence identity, keywords, or gene expression data) is required. The method assigns new proteins to these clusters with a measure of reliability. It can also provide human reviewers with a reliability score for both new and previously classified proteins. Results: A dataset of 27,000 annotated PDB polypeptide chains (of 36,000 chains currently in the PDB) was generated from 23,000 chains classified a priori