Sarah Cohen-Boulakia

OpenAlea: scientific workflows combining data analysis and simulation

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 (openalea.gforge.inria.fr) 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.

InfraPhenoGrid: A scientific workflow infrastructure for Plant Phenomics on the Grid

Plant phenotyping consists in the observation of physical and biochemical traits of plant genotypes in response to environmental conditions. Challenges , in particular in context of climate change and food security, are numerous. High-throughput platforms have been introduced to observe the dynamic growth of a large number of plants in different environmental conditions. Instead of considering a few genotypes at a time (as it is the case when phenomic traits are measured manually), such platforms make it possible to use completely new kinds of approaches. However, the data sets produced by such widely instrumented platforms are huge, constantly augmenting and produced by increasingly complex experiments, reaching a point where distributed computation is mandatory to extract knowledge from data. In this paper, we introduce InfraPhenoGrid, the infrastructure we designed and deploy to efficiently manage data sets produced by the PhenoArch plant phenomics platform in the context of the French Phenome Project. Our solution consists in deploying scientific workflows on a Grid using a middle-ware to pilot workflow executions. Our approach is user-friendly in the sense that despite the intrinsic complexity of the infrastructure, running scientific workflows and understanding results obtained (using provenance information) is kept as simple as possible for end-users.

Rank aggregation with ties: experiments and analysis

The problem of aggregating multiple rankings into one consensus ranking is an active research topic especially in the database community. Various studies have implemented methods for rank aggregation and may have come up with contradicting conclusions upon which algorithms work best. Comparing such results is cumbersome, as the original studies mixed different approaches and used very different evaluation datasets and metrics. Additionally, in real applications, the rankings to be aggregated may not be permutations where elements are strictly ordered, but they may have ties where some elements are placed at the same position. However, most of the studies have not considered ties. This paper introduces the first large scale study of algorithms for rank aggregation with ties. More precisely, (i) we review rank aggregation algorithms and determine whether or not they can handle ties; (ii) we propose the first implementation to compute the exact solution of the Rank Aggregation with ties problem; (iii) we evaluate algorithms for rank aggregation with ties on a very large panel of both real and carefully generated synthetic datasets; (iv) we provide guidance on the algorithms to be favored depending on dataset features.