Automatic consistency assurance for literature-based gene ontology annotation

Jiyu Chen, Nicholas Geard, Justin Zobel, Karin Verspoor
2021 BMC Bioinformatics  
Background Literature-based gene ontology (GO) annotation is a process where expert curators use uniform expressions to describe gene functions reported in research papers, creating computable representations of information about biological systems. Manual assurance of consistency between GO annotations and the associated evidence texts identified by expert curators is reliable but time-consuming, and is infeasible in the context of rapidly growing biological literature. A key challenge is
more » ... aining consistency of existing GO annotations as new studies are published and the GO vocabulary is updated. Results In this work, we introduce a formalisation of biological database annotation inconsistencies, identifying four distinct types of inconsistency. We propose a novel and efficient method using state-of-the-art text mining models to automatically distinguish between consistent GO annotation and the different types of inconsistent GO annotation. We evaluate this method using a synthetic dataset generated by directed manipulation of instances in an existing corpus, BC4GO. We provide detailed error analysis for demonstrating that the method achieves high precision on more confident predictions. Conclusions Two models built using our method for distinct annotation consistency identification tasks achieved high precision and were robust to updates in the GO vocabulary. Our approach demonstrates clear value for human-in-the-loop curation scenarios.
doi:10.1186/s12859-021-04479-9 pmid:34823464 pmcid:PMC8620237 fatcat:u4z4maa2ajbltcalwp3vyy2m4q