The ModelSEED Database for the integration of metabolic annotations and the reconstruction, comparison, and analysis of metabolic models for plants, fungi, and microbes
| Autor: | Samuel M. D. Seaver, Filipe Liu, Qizhi Zhang, James Jeffryes, José P. Faria, Janaka N. Edirisinghe, Michael Mundy, Nicholas Chia, Elad Noor, Moritz E. Beber, Aaron A. Best, Matthew DeJongh, Jeffrey A. Kimbrel, Patrik D’haeseleer, Erik Pearson, Shane Canon, Elisha M. Wood-Charlson, Robert W. Cottingham, Adam P. Arkin, Christopher S. Henry |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0303 health sciences
Database Construct (python library) computer.software_genre Plant genomes Flux balance analysis Biochemical network Set (abstract data type) 03 medical and health sciences 0302 clinical medicine Resource (project management) Metabolic modeling computer 030217 neurology & neurosurgery 030304 developmental biology Data integration |
| DOI: | 10.1101/2020.03.31.018663 |
| Popis: | Introduction For over ten years, the ModelSEED has been a primary resource for researchers endeavoring to construct draft genome-scale metabolic models based on annotated microbial or plant genomes. As described here, and now being released, the ModelSEED biochemistry database serves as the foundation of biochemical data underlying the ModelSEED and KBase. Objectives The ModelSEED biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by being: (i) a database to serve metabolic modeling by including compartmentalization, transport reactions, charged molecules, proton balancing on reactions, and templates for model species; (ii) extensible by the user community, with all data stored in GitHub; and (iii) designed as a biochemical “Rosetta Stone” to facilitate comparison and integration of annotations from many different tools and databases. Methods The ModelSEED was constructed by combining chemistry from many resources, applying standard transformations to data, identifying overlapping compounds and reactions, 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. We also develop ontologies designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. Results The current ModelSEED includes 33,978 compounds and 36,645 reactions, made available in an extensible set of files on GitHub, and visualized via the web from the ModelSEED and KBase. Conclusion This database serves as a transparent source of biochemistry data to broadly support mechanistic modeling and data integration. |
| Databáze: | OpenAIRE |
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