TY - JOUR
T1 - The ModelSEED Database for the integration of metabolic annotations and the reconstruction, comparison, and analysis of metabolic models for plants, fungi, and microbes
AU - Seaver, Samuel M.D.
AU - Liu, Filipe
AU - Zhang, Qizhi
AU - Jeffryes, James
AU - Faria, José P.
AU - Edirisinghe, Janaka N.
AU - Mundy, Michael
AU - Chia, Nicholas
AU - Noor, Elad
AU - Beber, Moritz E.
AU - Best, Aaron A.
AU - DeJongh, Matthew
AU - Kimbrel, Jeffrey A.
AU - D'haeseleer, Patrik
AU - Pearson, Erik
AU - Canon, Shane
AU - Wood-Charlson, Elisha M.
AU - Cottingham, Robert W.
AU - Arkin, Adam P.
AU - Henry, Christopher S.
N1 - Publisher Copyright:
The copyright holder for this preprint (which was not certified by peer review) is the author/funder. It is made available under a CC-BY-ND 4.0 International license
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/4/1
Y1 - 2020/4/1
N2 - For over ten 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 and KBase.
AB - For over ten 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 and KBase.
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U2 - 10.1101/2020.03.31.018663
DO - 10.1101/2020.03.31.018663
M3 - Article
AN - SCOPUS:85098909139
JO - American Journal of Physiology - Renal Fluid and Electrolyte Physiology
JF - American Journal of Physiology - Renal Fluid and Electrolyte Physiology
SN - 1931-857X
ER -