Xiaoqiong Wei1,2, Chengxin Zhang2, Peter L Freddolino2,3, Yang Zhang2,3. 1. State Key Laboratory of Biotherapy and Cancer Center/Collaborative Innovation Center of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China. 2. Department of Computational Medicine and Bioinformatics. 3. Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA.
Abstract
MOTIVATION: 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. RESULTS: 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)' which is in contradiction with 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 large-scale comparative protein function studies. AVAILABILITY AND IMPLEMENTATION: https://zhanglab.ccmb.med.umich.edu/RAR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: 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. RESULTS: 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)' which is in contradiction with 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 large-scale comparative protein function studies. AVAILABILITY AND IMPLEMENTATION: https://zhanglab.ccmb.med.umich.edu/RAR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Chengxin Zhang; Wei Zheng; Micah Cheng; Gilbert S Omenn; Peter L Freddolino; Yang Zhang Journal: J Proteome Res Date: 2021-01-04 Impact factor: 4.466
Authors: Mitchell W Booth; Martin F Breed; Gary A Kendrick; Philipp E Bayer; Anita A Severn-Ellis; Elizabeth A Sinclair Journal: Biol Open Date: 2022-08-23 Impact factor: 2.643