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

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  • Additional Information
    • Source:
      Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print Cited Medium: Internet ISSN: 1367-4811 (Electronic) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE
    • Publication Information:
      Original Publication: Oxford : Oxford University Press, c1998-
    • Subject Terms:
      Systems Biology* ; Programming Languages*; Software ; Models, Biological ; Language
    • Abstract:
      Motivation: Annotations of biochemical models provide details of chemical species, documentation of chemical reactions, and other essential information. Unfortunately, the vast majority of biochemical models have few, if any, annotations, or the annotations provide insufficient detail to understand the limitations of the model. The quality and quantity of annotations can be improved by developing tools that recommend annotations. For example, recommender tools have been developed for annotations of genes. Although annotating genes is conceptually similar to annotating biochemical models, there are important technical differences that make it difficult to directly apply this prior work.
      Results: We present AMAS, a system that predicts annotations for elements of models represented in the Systems Biology Markup Language (SBML) community standard. We provide a general framework for predicting model annotations for a query element based on a database of annotated reference elements and a match score function that calculates the similarity between the query element and reference elements. The framework is instantiated to specific element types (e.g. species, reactions) by specifying the reference database (e.g. ChEBI for species) and the match score function (e.g. string similarity). We analyze the computational efficiency and prediction quality of AMAS for species and reactions in BiGG and BioModels and find that it has subsecond response times and accuracy between 80% and 95% depending on specifics of what is predicted. We have incorporated AMAS into an open-source, pip-installable Python package that can run as a command-line tool that predicts and adds annotations to species and reactions to an SBML model.
      Availability and Implementation: Our project is hosted at https://github.com/sys-bio/AMAS, where we provide examples, documentation, and source code files. Our source code is licensed under the MIT open-source license.
      (© The Author(s) 2023. Published by Oxford University Press.)
    • Comments:
      Update of: bioRxiv. 2023 Jul 21;:. (PMID: 37503075)
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    • Grant Information:
      P41 EB023912 United States EB NIBIB NIH HHS
    • Publication Date:
      Date Created: 20231026 Date Completed: 20231107 Latest Revision: 20240210
    • Publication Date:
      20240210
    • Accession Number:
      PMC10628433
    • Accession Number:
      10.1093/bioinformatics/btad658
    • Accession Number:
      37882737