SBOannotator: A Python tool for the automated assignment of Systems Biology Ontology terms
Project description
SBOannotator
SBOannotator: a Python tool for the automated assignment of Systems Biology Ontology terms
Authors : Elisabeth Fritze & Nantia Leonidou
How to cite the SBOannotator?
The online version of the SBOannotator is described in this article: https://www.preprints.org/manuscript/202302.0317/v1
Overview
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.
Installation
pip install SBOannotator
Prerequisites
This tool has the following dependencies:
python >=3.8.5
Packages:
- sqlite3
- libsbml
- collections
- requests
- json
- time
Input data
doc
: an SBML documentmodel_libsbml
: SBML model of interestmodelType
: type of modelling framework (see below)model_annotated
: True, if model already includes annotations with EC numbersdatabase_name
: name of imported database, without extensionnew_filename
: file name for output model
Types of modelling framework accepted:
- constraint-based
- logical
- continuous
- discrete
- hybrid
- logical
Outputs
model_libsbml
: Annotated libsbml model
Usage
To run SBOannotator use the main.py
script and modify the parameters in the readSBML
and sbo_annotator
functions as wished.
If ERROR occurs, check the current version of Python:
python --version'
conda install python=3.8.5
Exemplary models and Results
The folder models/BiGG_Models
contains all the tested models as they were downloaded from
the BiGG database.
The annotated models after using the SBOannotator are listed in the folder named models/Annotated_Models
.
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