An Automated Model Annotation System (AMAS) for SBML Models.
An Automated Model Annotation System (AMAS) for SBML Models.
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DOI：
10.1101/2023.07.19.549722
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发表时间：
2023-07-21
期刊：
影响因子：
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通讯作者：
Sauro HM
中科院分区：
文献类型：
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作者：
Shin W;Gennari JH;Hellerstein JL;Sauro HM
生化模型的注释提供了化学物种的详细信息、化学反应的记录和其他基本信息。不幸的是，绝大多数生化模型几乎没有注释，或者注释没有提供足够的细节来理解模型的局限性。通过开发推荐注释的工具，可以提高注释的质量和数量。例如，已经开发了用于基因注释的推荐工具。尽管注释基因在概念上类似于注释生化模型，但存在重要的技术差异，使得直接应用这项先前的工作变得困难。我们提出了AMAS，这是一个预测系统生物学标记语言(SBML)社区标准中表示的模型元素的注释的系统。我们提供了一个通用框架，用于基于带注释的参考元素的数据库和计算查询元素与参考元素之间的相似度的匹配得分函数来预测查询元素的模型注释。通过指定参考数据库(例如，物种的Chebi)和匹配得分函数(例如，字符串相似性)，框架被实例化为特定元素类型(例如，物种、反应)。我们分析了AMAs对Bigg和Biomodels中的物种和反应的计算效率和预测质量，发现它具有亚秒级的响应时间和准确率在80%到95%之间，具体取决于预测的细节。我们将AMAS整合到一个开源的、可通过pip安装的Python包中，该包可以作为命令行工具运行，可以预测物种和SBML模型的反应，并为其添加注释。我们的项目托管在https://github.com/sys-bio/AMAS，上，我们在那里提供示例、文档和源代码文件。我们的源代码是按照麻省理工学院的开放源码许可证授权的。
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. 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 sub-second 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. 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.