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DORMAN: Database of Reconstructed MetAbolic Networks.
IEEE/ACM Trans Comput Biol Bioinform ; 18(4): 1474-1480, 2021.
Article en En | MEDLINE | ID: mdl-31581093
ABSTRACT
Genome-scale reconstructed metabolic networks have provided an organism specific understanding of cellular processes and their relations to phenotype. As they are deemed essential to study metabolism, the number of organisms with reconstructed metabolic networks continues to increase. This everlasting research interest lead to the development of online systems/repositories that store existing reconstructions and enable new model generation, integration, and constraint-based analyses. While features that support model reconstruction are widely available, current systems lack the means to help users who are interested in analyzing the topology of the reconstructed networks. Here, we present the Database of Reconstructed Metabolic Networks - DORMAN. DORMAN is a centralized online database that stores SBML-based reconstructed metabolic networks published in the literature, and provides web-based computational tools for visualizing and analyzing the model topology. Novel features of DORMAN are (i) interactive visualization interface that allows rendering of the complete network as well as editing and exporting the model, (ii) hierarchical navigation that provides efficient access to connected entities in the model, (iii) built-in query interface that allow posing topological queries, and finally, and (iv) model comparison tool that enables comparing models with different nomenclatures, using approximate string matching. DORMAN is online and freely accessible at http//ciceklab.cs.bilkent.edu.tr/dorman.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Bases de Datos Genéticas / Redes y Vías Metabólicas / Metabolómica Idioma: En Revista: ACM Trans Comput Biol Bioinform Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2021 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Bases de Datos Genéticas / Redes y Vías Metabólicas / Metabolómica Idioma: En Revista: ACM Trans Comput Biol Bioinform Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2021 Tipo del documento: Article
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