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Open Access Methodology article

Conceptual-level workflow modeling of scientific experiments using NMR as a case study

Kacy K Verdi1, Heidi JC Ellis2* and Michael R Gryk3

Author Affiliations

1 Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA

2 Department of Computer Science, Trinity College, Hartford, CT, 06106, USA

3 Department of Molecular, Microbial and Structural Biology, University of Connecticut Health Center, Farmington, CT, 06030, USA

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BMC Bioinformatics 2007, 8:31  doi:10.1186/1471-2105-8-31

Published: 30 January 2007

Abstract

Background

Scientific workflows improve the process of scientific experiments by making computations explicit, underscoring data flow, and emphasizing the participation of humans in the process when intuition and human reasoning are required. Workflows for experiments also highlight transitions among experimental phases, allowing intermediate results to be verified and supporting the proper handling of semantic mismatches and different file formats among the various tools used in the scientific process. Thus, scientific workflows are important for the modeling and subsequent capture of bioinformatics-related data. While much research has been conducted on the implementation of scientific workflows, the initial process of actually designing and generating the workflow at the conceptual level has received little consideration.

Results

We propose a structured process to capture scientific workflows at the conceptual level that allows workflows to be documented efficiently, results in concise models of the workflow and more-correct workflow implementations, and provides insight into the scientific process itself. The approach uses three modeling techniques to model the structural, data flow, and control flow aspects of the workflow. The domain of biomolecular structure determination using Nuclear Magnetic Resonance spectroscopy is used to demonstrate the process. Specifically, we show the application of the approach to capture the workflow for the process of conducting biomolecular analysis using Nuclear Magnetic Resonance (NMR) spectroscopy.

Conclusion

Using the approach, we were able to accurately document, in a short amount of time, numerous steps in the process of conducting an experiment using NMR spectroscopy. The resulting models are correct and precise, as outside validation of the models identified only minor omissions in the models. In addition, the models provide an accurate visual description of the control flow for conducting biomolecular analysis using NMR spectroscopy experiment.