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OpenAlea: scientific workflows combining data analysis and simulation

Pradal C., Fournier C., Valduriez P., Cohen-Boulakia S.. 2015. In : Gupta Amarnath (ed.), Rathbun Susan (ed.). Proceedings of the 27th International Conference on Scientific and Statistical Database Management. New York : ACM, 6 p.. International Conference on Scientific and Statistical Database Management. 27, 2015-06-29/2015-07-01, San Diego (Etats-Unis).

DOI: 10.1145/2791347.2791365

Analyzing biological data (e.g., annotating genomes, assembling NGS data...) may involve very complex and interlinked steps where several tools are combined together. Scientific workflow systems have reached a level of maturity that makes them able to support the design and execution of such in-silico experiments, and thus making them increasingly popular in the bioinformatics community. However, in some emerging application domains such as system biology, developmental biology or ecology, the need for data analysis is combined with the need to model complex multi-scale biological systems, possibly involving multiple simulation steps. This requires the scientific workflow to deal with retro-action to understand and predict the relationships between structure and function of these complex systems. OpenAlea ( is the only scientific workflow system able to uniformly address the problem, which made it successful in the scientific community. One of its main originality is to introduce higher-order dataflows as a means to uniformly combine classical data analysis with modeling and simulation. In this demonstration paper, we provide for the first time the description of the OpenAlea system involving an original combination of features. We illustrate the demonstration on a high-throughput workflow in phenotyping, phenomics, and environmental control designed to study the interplay between plant architecture and climatic change.

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