4.8 Article

The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes

期刊

NUCLEIC ACIDS RESEARCH
卷 49, 期 D1, 页码 D575-D588

出版社

OXFORD UNIV PRESS
DOI: 10.1093/nar/gkaa746

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资金

  1. U.S. Department of Energy [DE-AC02-06CH11357, DE-AC02-05CH11231, DE-AC05-00OR22725, DE-AC52-07NA27344]
  2. National Cancer Institute [R01CA179243]
  3. National Science Foundation [GEPR-1444202, MCB-1716285]
  4. Horizon 2020 - Research and Innovation Framework Programme [686070]
  5. Center for Individualized Medicine, Mayo Clinic

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ModelSEED has been a primary resource for building draft genome-scale metabolic models based on annotated microbial or plant genomes for over 10 years. The biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase, distinguished by various properties and designed to facilitate comparison and integration of annotations from different tools and databases.
For over 10 years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical 'Rosetta Stone' to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org/ biochem and KBase.

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