Information integration of protein–protein interactions as essential tools for immunomics

After a brief introduction to point out the necessity to advance for a global understanding of the macromolecular interactions occurring during the immune system development and responses, Section 2 will be devoted to analyse the current tools for an automatic location of information on these protein–protein interactions in the web. In the next section (Section 3), we will point out different action lines to improve these tools and, consequently, to increase the efficiency to establish (to understand) the “protein network skeleton” that controls our immune responses. Finally, we will briefly present our current strategy and work to advance towards this goal.     

Transparent mediation-based access to multiple yeast data sources using an ontology driven interface

BACKGROUND: Saccharomyces cerevisiae is recognized as a model system representing a simple eukaryote whose genome can be easily manipulated. Information solicited by scientists on its biological entities (Proteins, Genes, RNAs...) is scattered within several data sources like SGD, Yeastract, CYGD-MIPS, BioGrid, PhosphoGrid, etc. Because of the heterogeneity of these sources, querying them separately and then manually combining the returned results is a complex and time-consuming task for biologists most of whom are not bioinformatics expert. It also reduces and limits the use that can be made on the available data. RESULTS: To provide transparent and simultaneous access to yeast sources, we have developed YeastMed: an XML and mediator-based system. In this paper, we present our approach in developing this system which takes advantage of SB-KOM to perform the query transformation needed and a set of Data Services to reach the integrated data sources. The system is composed of a set of modules that depend heavily on XML and Semantic Web technologies. User queries are expressed in terms of a domain ontology through a simple form-based web interface. CONCLUSIONS: YeastMed is the first mediation-based system specific for integrating yeast data sources. It was conceived mainly to help biologists to find simultaneously relevant data from multiple data sources. It has a biologist-friendly interface easy to use. The system is available at http://www.khaos.uma.es/yeastmed/.     

Transparent mediation-based access to multiple yeast data sources using an ontology driven interface

Background

Over the past decade the workflow system paradigm has evolved as an efficient and user-friendly approach for developing complex bioinformatics applications. Two popular workflow systems that have gained acceptance by the bioinformatics community are Taverna and Galaxy. Each system has a large user-base and supports an ever-growing repository of application workflows. However, workflows developed for one system cannot be imported and executed easily on the other. The lack of interoperability is due to differences in the models of computation, workflow languages, and architectures of both systems. This lack of interoperability limits sharing of workflows between the user communities and leads to duplication of development efforts.

Results

In this paper, we present Tavaxy, a stand-alone system for creating and executing workflows based on using an extensible set of re-usable workflow patterns. Tavaxy offers a set of new features that simplify and enhance the development of sequence analysis applications: It allows the integration of existing Taverna and Galaxy workflows in a single environment, and supports the use of cloud computing capabilities. The integration of existing Taverna and Galaxy workflows is supported seamlessly at both run-time and design-time levels, based on the concepts of hierarchical workflows and workflow patterns. The use of cloud computing in Tavaxy is flexible, where the users can either instantiate the whole system on the cloud, or delegate the execution of certain sub-workflows to the cloud infrastructure.

Conclusions

Tavaxy reduces the workflow development cycle by introducing the use of workflow patterns to simplify workflow creation. It enables the re-use and integration of existing (sub-) workflows from Taverna and Galaxy, and allows the creation of hybrid workflows. Its additional features exploit recent advances in high performance cloud computing to cope with the increasing data size and complexity of analysis. The system can be accessed either through a cloud-enabled web-interface or downloaded and installed to run within the user's local environment. All resources related to Tavaxy are available at http://www.tavaxy.org.     

Information integration of protein-protein interactions as essential tools for immunomics

After a brief introduction to point out the necessity to advance for a global understanding of the macromolecular interactions occurring during the immune system development and responses, Section 2 will be devoted to analyse the current tools for an automatic location of information on these protein-protein interactions in the web. In the next section (Section 3), we will point out different action lines to improve these tools and, consequently, to increase the efficiency to establish (to understand) the "protein network skeleton" that controls our immune responses. Finally, we will briefly present our current strategy and work to advance towards this goal.     

Social pathway annotation: extensions of the systems biology metabolic modelling assistant

High-throughput experiments have produced large amounts of heterogeneous data in the life sciences. These data are usually represented in different formats (and sometimes in technical documents) on the Web. Inevitably, life science researchers have to deal with all these data and different formats to perform their daily research, but it is simply not possible for a single human mind to analyse all these data. The integration of data in the life sciences is a key component in the analysis of biological processes. These data may contain errors, but the curation of the vast amount of data generated in the 'omic' era cannot be done by individual researchers. To address this problem, community-driven tools could be used to assist with data analysis. In this article, we focus on a tool with social networking capabilities built on top of the SBMM (Systems Biology Metabolic Modelling) Assistant to enable the collaborative improvement of metabolic pathway models (the application is freely available at http://sbmm.uma.es/SPA).