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Annotation and merging of SBML models with semanticSBML.
Krause, Falko; Uhlendorf, Jannis; Lubitz, Timo; Schulz, Marvin; Klipp, Edda; Liebermeister, Wolfram.
Afiliação
  • Krause F; Theoretische Biophysik, Humboldt-Universität zu Berlin, Invalidenstrasse 42, D-10115 Berlin, Germany.
Bioinformatics ; 26(3): 421-2, 2010 Feb 01.
Article em En | MEDLINE | ID: mdl-19933161
SUMMARY: Systems Biology Markup Language (SBML) is the leading exchange format for mathematical models in Systems Biology. Semantic annotations link model elements with external knowledge via unique database identifiers and ontology terms, enabling software to check and process models by their biochemical meaning. Such information is essential for model merging, one of the key steps towards the construction of large kinetic models. SemanticSBML is a tool that helps users to check and edit MIRIAM annotations and SBO terms in SBML models. Using a large collection of biochemical names and database identifiers, it supports modellers in finding the right annotations and in merging existing models. Initially, an element matching is derived from the MIRIAM annotations and conflicting element attributes are categorized and highlighted. Conflicts can then be resolved automatically or manually, allowing the user to control the merging process in detail. AVAILABILITY: SemanticSBML comes as a free software written in Python and released under the GPL 3. A Debian package, a source package for other Linux distributions, a Windows installer and an online version of semanticSBML with limited functionality are available at http://www.semanticsbml.org. A preinstalled version can be found on the Linux live DVD SB.OS, available at http://www.sbos.eu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Biologia Computacional / Biologia de Sistemas Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2010 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Biologia Computacional / Biologia de Sistemas Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2010 Tipo de documento: Article