Speaker
Description
Impact
The system can be used by biomedical researchers to reach different results: validate potential biomarkers or multi-gene classifiers, perform statistical analyses, multi-chip quality assessment, cross-platform dataset integration, etc. The design of services, the parallelization of pre-processing tasks and the implementation on both grid and high performance environment make this system useful for validation on several independent datasets. The collaboration between molecular oncologists and software engineers allowed for the optimization of the system without loosing flexibility. Results from technical and validation tests show that our approach can scale up in the size of processed data well above current available Desktop computer limitations (both in term of computational time and reduced user overhead on manual procedures).
Following the Software as a Service paradigm users can focus their budgets on experimental design rather than infrastructure.
Keywords
integration, genomics, large-scale analysis, web interfaces
Conclusions and Future Work
Future developments are planned to enhance service usability by using a standard WSDL interface. In fact, the adopted framework is able to provide services both through a Web interface and through a Web Service, exploitable from any Web Services enabled client application.
Orchestrating multiple processing steps in a workflow strategy opens a new scenario where users can design complex experiments, tailored to their needs, just by using Portal services as workflow building bocks.
URL for further information
http://www.biomedcentral.com/1471-2105/9/480
http://www.springerlink.com/content/nk7217671w3m330x/
Detailed analysis
The challenge is: how to use this complex amount of data and tools in a easy and efficient way?
The platform proposed tries to answer to these challenges.
The general objective is to offer automated methodologies for large volumes of bioinformatics data analysis and storage, dealing with complex issues as different formats, distributed locations, time-consuming tasks, computational needs.
Exploiting the backend gLite infrastructure and a HPC environment, several data analysis algorithms and tools (R/Bioconductor, Dchip, APT) have been integrated into the platform providing high-level services for bioinformatics community.
The front-end, based on Enginframe Grid Portal from Nice, simplifies user interaction through customized web interfaces, increasing usability by users of all levels.
A case study for correlation between microarray gene expression data and clinico-pathological data through a combination of available and newly developed processing tools will be presented.