Semantic Data and Models Sharing in Systems Biology: The Just Enough Results Model and the SEEK Platform

Abstract (Expand)

Research in Systems Biology involves integrating data and knowledge about the dynamic processes in biological systems in order to understand and model them. Semantic web technologies should be ideal … for exploring the complex networks of genes, proteins and metabolites that interact, but much of this data is not natively available to the semantic web. Data is typically collected and stored with free-text annotations in spreadsheets, many of which do not conform to existing metadata standards and are often not publically released. Along with initiatives to promote more data sharing, one of the main challenges is therefore to semantically annotate and extract this data so that it is available to the research community. Data annotation and curation are expensive and undervalued tasks that have enormous benefits to the discipline as a whole, but fewer benefits to the individual data producers. By embedding semantic annotation into spreadsheets, however, and automatically extracting this data into RDF at the time of repository submission, the process of producing standards-compliant data, that is available for semantic web querying, can be achieved without adding additional overheads to laboratory data management. This paper describes these strategies in the context of semantic data management in the SEEK. The SEEK is a web-based resource for sharing and exchanging Systems Biology data and models that is underpinned by the JERM ontology (Just Enough Results Model), which describes the relationships between data, models, protocols and experiments. The SEEK was originally developed for SysMO, a large European Systems Biology consortium studying micro-organisms, but it has since had widespread adoption across European Systems Biology.

Editor: David Hutchison and Takeo Kanade and Josef Kittler and Jon M. Kleinberg and Friedemann Mattern and John C. Mitchell and Moni Naor and Oscar Nierstrasz and C. Pandu Rangan and Bernhard Steffen and Madhu Sudan and Demetri Terzopoulos and Doug Tygar and Moshe Y. Vardi and Gerhard Weikum and Camille Salinesi and Moira C. Norrie and Óscar Pastor

Date Published: 2013

Publication Type: Journal

Annotation and merging of SBML models with semanticSBML

Abstract (Expand)

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.

Authors: , Jannis Uhlendorf, Timo Lubitz, Marvin Schulz, , Wolfram Liebermeister

Date Published: 17th Nov 2009

Publication Type: Not specified

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