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A Web Tool for Generating High Quality Machine-readable Biological Pathways

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Published on: February 8, 2017

An automated model annotation system (AMAS) for SBML models.

Woosub Shin¹, John H Gennari², Joseph L Hellerstein^3,4

¹Auckland Bioengineering Institute, University of Auckland, 1010 Auckland, New Zealand.

Bioinformatics (Oxford, England)

|October 26, 2023

Summary

A new system, AMAS, automatically predicts and adds annotations to biochemical models, improving their clarity and usability. This tool enhances Systems Biology Markup Language (SBML) models with high accuracy and speed.

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Area of Science:

Biochemistry
Systems Biology
Bioinformatics

Background:

Biochemical models require detailed annotations for chemical species and reactions to understand their limitations.
Current biochemical models often lack sufficient annotations, hindering usability and reproducibility.
Existing gene annotation tools are not directly applicable to biochemical models due to technical differences.

Purpose of the Study:

To develop an automated system for predicting and adding annotations to biochemical models.
To improve the quality and quantity of annotations in Systems Biology Markup Language (SBML) models.
To provide a general framework for annotating various elements within biochemical models.

Main Methods:

Developed AMAS, a system predicting annotations for SBML model elements.
Utilized a framework based on annotated reference databases and a similarity-based match score function.
Instantiated the framework for species and reactions using databases like ChEBI and BiGG.
Analyzed computational efficiency and prediction accuracy.

Main Results:

AMAS achieves subsecond response times for annotation prediction.
Prediction accuracy ranges from 80% to 95% for species and reactions.
AMAS is available as an open-source Python package and command-line tool.
The system can predict and add annotations to SBML models.

Conclusions:

AMAS effectively enhances biochemical models with accurate and timely annotations.
The developed framework offers a flexible approach for annotating diverse model elements.
The open-source availability promotes wider adoption and improvement of biochemical model annotations.