SBOannotator: a Python tool for the automated assignment of systems biology ontology terms

Abstract

Motivation: The number and size of computational models in biology have drastically increased over the past years and continue to grow. Modeled networks are becoming more complex, and reconstructing them from the beginning in an exchangeable and reproducible manner is challenging. Using precisely defined ontologies enables the encoding of field-specific knowledge and the association of disparate data types. In computational modeling, the medium for representing domain knowledge is the set of orthogonal structured controlled vocabularies named Systems Biology Ontology (SBO). The SBO terms enable modelers to explicitly define and describe model entities, including their roles and characteristics.

Results: Here, we present the first standalone tool that automatically assigns SBO terms to multiple entities of a given SBML model, named the SBOannotator. The main focus lies on the reactions, as the correct assignment of precise SBO annotations requires their extensive classification. Our implementation does not consider only top-level terms but examines the functionality of the underlying enzymes to allocate precise and highly specific ontology terms to biochemical reactions. Transport reactions are examined separately and are classified based on the mechanism of molecule transport. Pseudo-reactions that serve modeling purposes are given reasonable terms to distinguish between biomass production and the import or export of metabolites. Finally, other model entities, such as metabolites and genes, are annotated with appropriate terms. Including SBO annotations in the models will enhance the reproducibility, usability, and analysis of biochemical networks.

Availability and implementation: SBOannotator is freely available from https://github.com/draeger-lab/SBOannotator/.

Figure 1.

Overview of SBOannotator’s pipeline. The software enables the automated assignment of SBO terms to multiple model entities in a given SBML file. First, the pseudo- and transport reactions are characterized and classified. Then, the biochemical reactions are subdivided into 18 classes based on the underlying enzymatic function. Finally, further model elements are annotated by their respective SBO annotations, and the final model is stored in SBML format. The main advantage is the detailed categorization of biochemical reactions and the allocation of specialized terms that precisely capture related and exchangeable information.