Concevoir et partagerdes workflowsd’analyse de données.Application auxtraitements intensifsen bioinformatique

摘要

Design and share data analysis workflows. Application to bioinformatics intensivetreatmentsAs part of an Open Science initiative, we are particularly interested in the scientificWorkflow Management Systems (WfMS) and their applications for intensive data analysisin bioinformatics. We start from the assumption that WfMS can evolve to becomeefficient hubs able to speed up the development and the dissemination of innovativeanalysis methods. These software platforms could rally and unite not only the currentstakeholders, who are service consumers, but also the service producers, around a disciplinarytheme. We therefore consider that these environments must be both adapted tothe practices of the scientists who are method designers and also enhanced with increasedproductivity during design and treatment. These constraints lead us to study the rapidcapture of workflows, the simplification of technical tasks integration, like parallelisationand the deployment customization. First, we define an expressive graphic worfklowlanguage, adapted to the quick capture of workflows. This is interpreted by a workflowengine based on a new model of computation with high performances obtained by theuse of multiple levels of parallelism. Then, we present a Model-Driven design approachthat facilitates the data parallelism generation and the production of suitable implementationsfor different execution contexts. We describe in particular the integration of acomponents and platforms meta-model used to automate the configuration of workflows’dependencies. Finally, in the case of the cloud model Container as a Service (CaaS), wedevelop a workflow specification intrinsically re-executable and readily disseminatable.The adoption of this kind of model could lead to an acceleration of exchanges and abetter availability of data analysis workflows.