CaGrid Workflow Toolkit: a Taverna based workflow tool for cancer grid.
BACKGROUND
In biological and medical domain, the use of web services made the data and computation functionality accessible in a unified manner, which helped automate the data pipeline that was previously performed manually. Workflow technology is widely used in the orchestration of multiple services to facilitate in-silico research. Cancer Biomedical Informatics Grid (caBIG) is an information network enabling the sharing of cancer research related resources and caGrid is its underlying service-based computation infrastructure. CaBIG requires that services are composed and orchestrated in a given sequence to realize data pipelines, which are often called scientific workflows.


RESULTS
CaGrid selected Taverna as its workflow execution system of choice due to its integration with web service technology and support for a wide range of web services, plug-in architecture to cater for easy integration of third party extensions, etc. The caGrid Workflow Toolkit (or the toolkit for short), an extension to the Taverna workflow system, is designed and implemented to ease building and running caGrid workflows. It provides users with support for various phases in using workflows: service discovery, composition and orchestration, data access, and secure service invocation, which have been identified by the caGrid community as challenging in a multi-institutional and cross-discipline domain.


CONCLUSIONS
By extending the Taverna Workbench, caGrid Workflow Toolkit provided a comprehensive solution to compose and coordinate services in caGrid, which would otherwise remain isolated and disconnected from each other. Using it users can access more than 140 services and are offered with a rich set of features including discovery of data and analytical services, query and transfer of data, security protections for service invocations, state management in service interactions, and sharing of workflows, experiences and best practices. The proposed solution is general enough to be applicable and reusable within other service-computing infrastructures that leverage similar technology stack.
Computation Institute, University of Chicago and Argonne National Laboratory, Chicago, IL, USA. wtan@mcs.anl.gov
Wei
W
Tan
Ravi
R
Madduri
Alexandra
A
Nenadic
Stian
S
Soiland-Reyes
Dinanath
D
Sulakhe
Ian
I
Foster
Carole A
CA
Goble
D019295:Computational Biology / Q000379:methods*; D003628:Database Management Systems; D016247:Information Storage and Retrieval / Q000379:methods*; D020407:Internet; D009369:Neoplasms / Q000145:classification / Q000175:diagnosis / Q000235:genetics*; D012984:Software