The SBOannotator is the first standalone tool that automatically assigns SBO terms to multiple entities of a
given SBML model, 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.
The performance of SBOannotator in assigning descriptive and more precise terms
to biochemical reactions was evaluated using 108 metabolic models from the
BiGG
database. All downloaded models contained initially only five
types of SBO annotation representing only top-level terms and over 30 distinct terms
after using SBOannotator.
The biochemical reactions made up the largest group in both cases.
However, their coverage was reduced from 57.9% to 18.9%, meaning a
large percentage of the initial reactions received a more specific term.
The source code of the SBOannotator can be downloaded from the following GitHub repository:
https://github.com/draeger-lab/SBOannotator
To install SBOannotator in your computer, run this command in your terminal:
$ pip install SBOannotator
This is the preferred method to install SBOannotator, as it will always install the most recent stable release.
Please use the following publication to cite:
Leonidou, N., Fritze, E., Renz, A., & Dräger, A. (2023)
SBOannotator: a Python Tool for the Automated Assignment
of Systems Biology Ontology Terms.
DOI:
https://doi.org/10.1093/bioinformatics/btad437
