Biopipe: A Flexible Framework for Protocol-Based Bioinformatics Analysis
- Shawn Hoon1,
- Kiran Kumar Ratnapu1,
- Jer-ming Chia2,
- Balamurugan Kumarasamy3,
- Xiao Juguang3,
- Michele Clamp4,
- Arne Stabenau5,
- Simon Potter4,
- Laura Clarke4, and
- Elia Stupka3,6
- 1 Institute of Molecular and Cell Biology, National University of Singapore, Singapore 117609
- 2 Genome Institute of Singapore, National University of Singapore, Singapore 117528
- 3 Temasek Life Sciences Laboratory, National University of Singapore, Singapore 117604
- 4 The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
- 5 European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
Abstract
We identify several challenges facing bioinformatics analysis today. Firstly, to fulfill the promise of comparative studies, bioinformatics analysis will need to accommodate different sources of data residing in a federation of databases that, in turn, come in different formats and modes of accessibility. Secondly, the tsunami of data to be handled will require robust systems that enable bioinformatics analysis to be carried out in a parallel fashion. Thirdly, the ever-evolving state of bioinformatics presents new algorithms and paradigms in conducting analysis. This means that any bioinformatics framework must be flexible and generic enough to accommodate such changes. In addition, we identify the need for introducing an explicit protocol-based approach to bioinformatics analysis that will lend rigorousness to the analysis. This makes it easier for experimentation and replication of results by external parties. Biopipe is designed in an effort to meet these goals. It aims to allow researchers to focus on protocol design. At the same time, it is designed to work over a compute farm and thus provides high-throughput performance. A common exchange format that encapsulates the entire protocol in terms of the analysis modules, parameters, and data versions has been developed to provide a powerful way in which to distribute and reproduce results. This will enable researchers to discuss and interpret the data better as the once implicit assumptions are now explicitly defined within the Biopipe framework.
Footnotes
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[Supplemental material is available online at www.genome.org. The Biopipe software operates under an open source license and is freely available at http://www.biopipe.org.]
Article published online before print in July 2003.
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Article and publication are at http://www.genome.org/cgi/doi/10.1101/gr.1363103.
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↵6 Corresponding author. E-MAIL elia@tll.org.sg; FAX 65 6 8727007.
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- Accepted May 21, 2003.
- Received March 27, 2003.
- Cold Spring Harbor Laboratory Press