Many protein function databases are built on automated or semi-automated curations and can contain various annotation errors. The correction of such misannotations is critical to improving the accuracy and reliability of the databases. We proposed a new approach to detect potentially incorrect Gene Ontology (GO) annotations by comparing the ratio of annotation rates (RAR) for the same GO term across different taxonomic groups, where those with a relatively low RAR usually correspond to incorrect annotations. As an illustration, we applied the approach to 20 commonly studied species in two recent UniProt-GOA releases and identified 250 potential misannotations in the 2018-11-6 release, where only 25% of them were corrected in the 2019-6-3 release. Importantly, 56% of the misannotations are “Inferred from Biological aspect of Ancestor (IBA)”, i.e. reviewed computational annotations based on phylogenetic analysis. This is in contrast to previous observations that attributed misannotations mainly to “Inferred from Sequence or structural Similarity (ISS)”, probably reflecting an error source shift due to the new developments of function annotation databases. The results demonstrated a simple but efficient misannotation detection approach that is useful for developing taxonomic constraints in large-scale comparative protein function studies.

Tool Link: https://zhanglab.ccmb.med.umich.edu/RAR

URL for remote viewing: https://umich-health.zoom.us/j/94886745590?pwd=LzhLU243K2ZhbXNzU1BJRHQ5V25BZz09