Web services and workflow management for biological resources

被引:0
|
作者
Romano P. [1 ]
Marra D. [1 ]
Milanesi L. [2 ,3 ]
机构
[1] Bioinformatics and Structural Proteomics, National Cancer Research Institute (IST), Genova
[2] Biomedical Technologies Institute (ITB), National Research Council, Segrate
[3] CILEA, Segrate
关键词
European Bioinformatics Institute; Query Form; Biomedical Domain; Network Service Provider; Biomedical Interest;
D O I
10.1186/1471-2105-6-S4-S24
中图分类号
学科分类号
摘要
Background: The completion of the Human Genome Project has resulted in large quantities of biological data which are proving difficult to manage and integrate effectively. There is a need for a system that is able to automate accesses to remote sites and to "understand" the information that it is managing in order to link data properly. Workflow management systems combined with Web Services are promising Information and Communication Technologies (ICT) tools. Some have already been proposed and are being increasingly applied to the biomedical domain, especially as many biology-related Web Services are now becoming available. Information on biological resources and on genomic sequences mutations are two examples of very specialized datasets that are useful for specific research domains. Results: The architecture of a system that is able to access and execute predefined workflows is presented in this paper. Web Services allowing access to the IARC TP53 Mutation Database and CABRI catalogues of biological resources have been implemented and are available on-line. Example workflows which retrieve data from these Web Services have also been created and are available on-line. Conclusion: We present a general architecture and some building blocks for the implementation of a system that is able to remotely execute workflows of biomedical interest and show how this approach can effectively produce useful outputs. The further development and implementation of Web Services allowing access to an exhaustive set of biomedical databases and the creation of effective and useful workflows will improve the automation of in-silico analysis.
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