RT Journal Article SR Electronic T1 SBMate: A Framework for Evaluating Quality of Annotations in Systems Biology Models JF bioRxiv FD Cold Spring Harbor Laboratory SP 2021.10.09.463757 DO 10.1101/2021.10.09.463757 A1 Woosub Shin A1 Joseph L. Hellerstein A1 Yuda Munarko A1 Maxwell L. Neal A1 David P. Nickerson A1 Anand K. Rampadarath A1 Herbert M. Sauro A1 John H. Gennari YR 2021 UL http://biorxiv.org/content/early/2021/10/09/2021.10.09.463757.abstract AB The interests in repurposing and reusing systems biology models have been growing in recent years. Semantic annotations play an important role for this, as they provide crucial information on the meanings and functions of models. However, there are a limited number of tools that evaluate the existence or quality of such annotations. In this paper, we introduce SBMate, a python package that would serve as a framework for evaluating the quality of annotations in systems biology models. Three default metrics are provided: coverage, consistency, and specificity. Coverage checks whether annotations exist in a model. Consistency tests if the annotations are appropriate for the given model element. Finally, specificity represents how detailed the annotations are. We analyzed 1,000 curated models from the BioModels repository using the three metrics and discussed the results. Additional metrics can be easily added to extend the current version of SBMate.Competing Interest StatementThe authors have declared no competing interest.