Annotation of SBML Models Through Rule-Based Semantic Integration

*Motivation:* The creation of accurate quantitative Systems Biology Markup Language (SBML) models is a time-intensive, manual process often complicated by the many data sources and formats required to annotate even a small and well-scoped model. Ideally, the retrieval and integration of biological knowledge for model annotation should be performed quickly, precisely, and with a minimum of manual effort. Here, we present a method using off-the-shelf semantic web technology which enables this process: the heterogeneous data sources are first syntactically converted into ontologies; these are then aligned to a small domain ontology by applying a rule base. Integrating resources in this way can accommodate multiple formats with different semantics; it provides richly modelled biological knowledge suitable for annotation of SBML models.
*Results:* We demonstrate proof-of-principle for this rule-based mediation with two use cases for SBML model annotation. This was implemented with existing tools, decreasing development time and increasing reusability. This initial work establishes the feasibility of this approach as part of an automated SBML model annotation system.
*Availability:* Detailed information including download and mapping of the ontologies as well as integration results is available from "http://www.cisban.ac.uk/RBM":http://www.cisban.ac.uk/RB

Analysis and visualisation of RDF resources in Ondex

An increasing number of biomedical resources provide their information on the Semantic Web and this creates the basis for a distributed knowledge base which has the potential to advance biomedical research [1]. This potential, however, cannot be realized until researchers from the life sciences can interact with information in the Semantic Web. In particular, there is a need for tools that provide data reduction, visualization and interactive analysis capabilities.
Ondex is a data integration and visualization platform developed to support Systems Biology Research [2]. At its core is a data model based on two main principles: first, all information can be represented as a graph and, second, all elements of the graph can be annotated with ontologies. This data model conforms to the Semantic Web framework, in particular to RDF, and therefore Ondex is ideally positioned as a platform that can exploit the semantic web. 
The Ondex system offers a range of features and analysis methods of potential value to semantic web users, including:
-	An interactive graph visualization interface (Ondex user client), which provides data reduction and representation methods that leverage the ontological annotation.
-	A suite of importers from a variety of data sources to Ondex (http://ondex.org/formats.html)
-	A collection of plug-ins which implement graph analysis, graph transformation and graph-matching functions.
-	An integration toolkit (Ondex Integrator) which allows users to compose workflows from these modular components
-	In addition, all importers and plug-ins are available as web-services which can be integrated in other tools, as for instance Taverna [3].
The developments that will be presented in this demo have made this functionality interoperable with the Semantic Web framework. In particular we have developed an interactive importer, based on SPARQL that allows the query-driven construction of datasets which brings together information from different RDF data resources into Ondex.
These datasets can then be further refined, analysed and annotated both interactively using the Ondex user client and via user-defined workflows. The results of these analyses can be exported in RDF, which can be used to enrich existent knowledge bases, or to provide application-specific views of the data. Both importer and exporter only focus on a subset of the Ondex and RDF data models, which are shared between these two data representations [4].
In this demo we will show how Ondex can be used to query, analyse and visualize Semantic Web knowledge bases. In particular we will present real use cases focused, but not limited to, resources relevant to plant biology. 
We believe that Ondex can be a valid contribution to the adoption of the Semantic Web in Systems Biology research and in biomedical investigation more generally. We welcome feedback on our current import/export prototype and suggestions for the advancement of Ondex for the Semantic Web.

References

1.	Ruttenberg, A. et. al.: Advancing translational research with the Semantic Web, BMC Bioinformatics, 8 (Suppl. 3): S2 (2007).
2.	Köhler, J., Baumbach, J., Taubert, J., Specht, M., Skusa, A., Ruegg, A., Rawlings, C., Verrier, P., Philippi, S.: Graph-based analysis and visualization of experimental results with Ondex. Bioinformatics 22 (11):1383-1390 (2006).
3.	Rawlings, C.: Semantic Data Integration for Systems Biology Research, Technology Track at ISMB’09, http://www.iscb.org/uploaded/css/36/11846.pdf (2009).
4.	Splendiani, A. et. al.: Ondex semantic definition, (Web document) http://ondex.svn.sourceforge.net/viewvc/ondex/trunk/doc/semantics/ (2009).
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BacillOndex: An Integrated Data Resource for Systems and Synthetic Biology

BacillOndex is an extension of the Ondex data integration system, providing a semantically annotated, integrated knowledge base for the model Gram-positive bacterium Bacillus subtilis. This application allows a user to mine a variety of B. subtilis data sources, and analyse the resulting integrated dataset, which contains data about genes, gene products and their interactions. The data can be analysed either manually, by browsing using Ondex, or computationally via a Web services interface. We describe the process of creating a BacillOndex instance, and describe the use of the system for the analysis of single nucleotide polymorphisms in B. subtilis Marburg. The Marburg strain is the progenitor of the widely-used laboratory strain B. subtilis 168. We identified 27 SNPs with predictable phenotypic effects, including genetic traits for known phenotypes. We conclude that BacillOndex is a valuable tool for the systems-level investigation of, and hypothesis generation about, this important biotechnology workhorse. Such understanding contributes to our ability to construct synthetic genetic circuits in this organism

BBF RFC 108: Synthetic Biology Open Language (SBOL) Version 2.0.0

The Synthetic Biology Open Language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, filling a need not satisfied by other pre-existing standards

Performing statistical analyses on quantitative data in Taverna workflows: an example using R and maxdBrowse to identify differentially-expressed genes from microarray data.

BACKGROUND: There has been a dramatic increase in the amount of quantitative data derived from the measurement of changes at different levels of biological complexity during the post-genomic era. However, there are a number of issues associated with the use of computational tools employed for the analysis of such data. For example, computational tools such as R and MATLAB require prior knowledge of their programming languages in order to implement statistical analyses on data. Combining two or more tools in an analysis may also be problematic since data may have to be manually copied and pasted between separate user interfaces for each tool. Furthermore, this transfer of data may require a reconciliation step in order for there to be interoperability between computational tools. RESULTS: Developments in the Taverna workflow system have enabled pipelines to be constructed and enacted for generic and ad hoc analyses of quantitative data. Here, we present an example of such a workflow involving the statistical identification of differentially-expressed genes from microarray data followed by the annotation of their relationships to cellular processes. This workflow makes use of customised maxdBrowse web services, a system that allows Taverna to query and retrieve gene expression data from the maxdLoad2 microarray database. These data are then analysed by R to identify differentially-expressed genes using the Taverna RShell processor which has been developed for invoking this tool when it has been deployed as a service using the RServe library. In addition, the workflow uses Beanshell scripts to reconcile mismatches of data between services as well as to implement a form of user interaction for selecting subsets of microarray data for analysis as part of the workflow execution. A new plugin system in the Taverna software architecture is demonstrated by the use of renderers for displaying PDF files and CSV formatted data within the Taverna workbench. CONCLUSION: Taverna can be used by data analysis experts as a generic tool for composing ad hoc analyses of quantitative data by combining the use of scripts written in the R programming language with tools exposed as services in workflows. When these workflows are shared with colleagues and the wider scientific community, they provide an approach for other scientists wanting to use tools such as R without having to learn the corresponding programming language to analyse their own data.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Developing and enhancing biodiversity monitoring programmes: a collaborative assessment of priorities

1.Biodiversity is changing at unprecedented rates, and it is increasingly important that these changes are quantified through monitoring programmes. Previous recommendations for developing or enhancing these programmes focus either on the end goals, that is the intended use of the data, or on how these goals are achieved, for example through volunteer involvement in citizen science, but not both. These recommendations are rarely prioritized. 2.We used a collaborative approach, involving 52 experts in biodiversity monitoring in the UK, to develop a list of attributes of relevance to any biodiversity monitoring programme and to order these attributes by their priority. We also ranked the attributes according to their importance in monitoring biodiversity in the UK. Experts involved included data users, funders, programme organizers and participants in data collection. They covered expertise in a wide range of taxa. 3.We developed a final list of 25 attributes of biodiversity monitoring schemes, ordered from the most elemental (those essential for monitoring schemes; e.g. articulate the objectives and gain sufficient participants) to the most aspirational (e.g. electronic data capture in the field, reporting change annually). This ordered list is a practical framework which can be used to support the development of monitoring programmes. 4.People's ranking of attributes revealed a difference between those who considered attributes with benefits to end users to be most important (e.g. people from governmental organizations) and those who considered attributes with greatest benefit to participants to be most important (e.g. people involved with volunteer biological recording schemes). This reveals a distinction between focussing on aims and the pragmatism in achieving those aims. 5.Synthesis and applications. The ordered list of attributes developed in this study will assist in prioritizing resources to develop biodiversity monitoring programmes (including citizen science). The potential conflict between end users of data and participants in data collection that we discovered should be addressed by involving the diversity of stakeholders at all stages of programme development. This will maximize the chance of successfully achieving the goals of biodiversity monitoring programmes

BBF RFC 112: Synthetic Biology Open Language (SBOL) Version 2.1.0

BBF RFC 112 (the SBOL 2.1.0 standard) replaces BBF RFC 108 (the SBOL 2.0 standard), as well as the minor update SBOL 2.0.1.The Synthetic Biology Open Language (SBOL) has been developed as a standard to support the specification and exchange of biological design information

Synthetic Biology Open Language (SBOL) Version 1.1.0

In this BioBricks Foundation Request for Comments (BBF RFC), we specify the Synthetic Biology Open Language (SBOL) Version 1.1.0 to enable the electronic exchange of information describing DNA components used in synthetic biology. We define: 1. the vocabulary, a set of preferred terms and 2. the core data model, a common computational representation

Sharing Data from Cardiovascular Clinical Trials--A Proposal.

status: publishe