Seamless Coarse Grained Parallelism Integration in Intensive Bioinformatics Workflows

To be easily constructed, shared and maintained, complex in silico bioinformatics analysis are structured as workflows. Furthermore, the growth of computational power and storage demand from this domain, requires workflows to be efficiently executed. However, workflow performances usually rely on the ability of the designer to extract potential parallelism. But atomic bioinformatics tasks do not often exhibit direct parallelism which may appears later in the workflow design process. In this paper, we propose a Model-Driven Architecture approach for capturing the complete design process of bioinformatics workflows. More precisely, two workflow models are specified: the first one, called design model, graphically captures a low throughput prototype. The second one, called execution model, specifies multiple levels of coarse grained parallelism. The execution model is automatically generated from the design model using annotation derived from the EDAM ontology. These annotations describe the data types connecting differents elementary tasks. The execution model can then be interpreted by a workflow engine and executed on hardware having intensive computation facility

BioQuali Cytoscape plugin: analysing the global consistency of regulatory networks

International audienceBackground: The method most commonly used to analyse regulatory networks is the in silico simulation of fluctuations in network components when a network is perturbed. Nevertheless, confronting experimental data with a regulatory network entails many difficulties, such as the incomplete state-of-art of regulatory knowledge, the large-scale of regulatory models, heterogeneity in the available data and the sometimes violated assumption that mRNA expression is correlated to protein activity. Results: We have developed a plugin for the Cytoscape environment, designed to facilitate automatic reasoning on regulatory networks. The BioQuali plugin enhances user-friendly conversions of regulatory networks (including reference databases) into signed directed graphs. BioQuali performs automatic global reasoning in order to decide which products in the network need to be up or down regulated (active or inactive) to globally explain experimental data. It highlights incomplete regions in the network, meaning that gene expression levels do not globally correlate with existing knowledge on regulation carried by the topology of the network. Conclusion: The BioQuali plugin facilitates in silico exploration of large-scale regulatory networks by combining the user-friendly tools of the Cytoscape environment with high-performance automatic reasoning algorithms. As a main feature, the plugin guides further investigation regarding a system by highlighting regions in the network that are not accurately described and merit specific study

The BioMart community portal: an innovative alternative to large, centralized data repositories.

The BioMart Community Portal (www.biomart.org) is a community-driven effort to provide a unified interface to biomedical databases that are distributed worldwide. The portal provides access to numerous database projects supported by 30 scientific organizations. It includes over 800 different biological datasets spanning genomics, proteomics, model organisms, cancer data, ontology information and more. All resources available through the portal are independently administered and funded by their host organizations. The BioMart data federation technology provides a unified interface to all the available data. The latest version of the portal comes with many new databases that have been created by our ever-growing community. It also comes with better support and extensibility for data analysis and visualization tools. A new addition to our toolbox, the enrichment analysis tool is now accessible through graphical and web service interface. The BioMart community portal averages over one million requests per day. Building on this level of service and the wealth of information that has become available, the BioMart Community Portal has introduced a new, more scalable and cheaper alternative to the large data stores maintained by specialized organizations

Oocyte-Somatic Cells Interactions, Lessons from Evolution

National audienceLa qualité de l'ovocyte des vertébrés (capacité à être fécondé et permettre ensuite un développement embryonnaire normal) peut être variable, que ce soit en élevage ou dans le milieu naturel. Même s'il est établi que les relations entre l'ovocyte et les cellules folliculaires qui l'entourent jouent un rôle clé dans les capacités de développement ultérieur de l'ovocyte, les mécanismes moléculaires impliqués dans cette acquisition de la compétence ovocytaire au développement lors de l'ovogenèse restent mal connus. Ce projet vise a identifier des mécanismes moléculaires impliqués dans l'acquisition de la compétence ovocytaire au développement et qui soient communs aux vertébrés, aux vertébrés non mammaliens ou aux vertébrés mammaliens

Accurate prediction of BRCA1 and BRCA2 heterozygous genotype using expression profiling after induced DNA damage

International audiencePURPOSE: In this study, the differential gene expression changes following radiation-induced DNA damage in healthy cells from BRCA1/BRCA1 mutation carriers have been compared with controls using high-density microarray technology. We aimed to establish if BRCA1/BRCA2 mutation carriers could be distinguished from noncarriers based on expression profiling of normal cells. EXPERIMENTAL DESIGN: Short-term primary fibroblast cultures were established from skin biopsies from 10 BRCA1 and 10 BRCA2 mutation carriers and 10 controls, all of whom had previously had breast cancer. The cells were subjected to 15 Gy ionizing irradiation to induce DNA damage. RNA was extracted from all cell cultures, preirradiation and at 1 hour postirradiation. For expression profiling, 15 K spotted cDNA microarrays manufactured by the Cancer Research UK DNA Microarray Facility were used. Statistical feature selection was used with a support vector machine (SVM) classifier to determine the best feature set for predicting BRCA1 or BRCA2 heterozygous genotype. To investigate prediction accuracy, a nonprobabilistic classifier (SVM) and a probabilistic Gaussian process classifier were used. RESULTS: In the task of distinguishing BRCA1 and BRCA2 mutation carriers from noncarriers and from each other following radiation-induced DNA damage, the SVM achieved 90%, and the Gaussian process classifier achieved 100% accuracy. This effect could not be achieved without irradiation. In addition, the SVM identified a set of BRCA genotype predictor genes. CONCLUSIONS: We conclude that after irradiation-induced DNA damage, BRCA1 and BRCA2 mutation carrier cells have a distinctive expression phenotype, and this may have a future role in predicting genotypes, with application to clinical detection and classification of mutations

Accurate prediction of BRCA1 and BRCA2 heterozygous genotype using expression profiling after induced DNA damage

Purpose: In this study, the differential gene expression changes following radiation-induced DNA damage in healthy cells from BRCA1/BRCA1 mutation carriers have been compared with controls using high-density microarray technology. We aimed to establish if BRCA1/BRCA2 mutation carriers could be distinguished from noncarriers based on expression profiling of normal cells. Experimental Design: Short-term primary fibroblast cultures were established from skin biopsies from 10 BRCA1 and 10 BRCA2 mutation carriers and 10 controls, all of whom had previously had breast cancer. The cells were subjected to 15 Gy ionizing irradiation to induce DNA damage. RNA was extracted from all cell cultures, preirradiation and at 1 hour postirradiation. For expression profiling, 15 K spotted cDNA microarrays manufactured by the Cancer Research UK DNA Microarray Facility were used. Statistical feature selection was-used with a support vector machine (SVM) classifier to determine the best feature set for predicting BRCA1 or BRCA2 heterozygous genotype. To investigate prediction accuracy, a nonprobabilistic classifier (SVM) and a probabilistic Gaussian process classifier were used. Results: In the task of distinguishing BRCA1 and BRCA2 mutation carriers from noncarriers and from each other following radiation-induced DNA damage, the SVM achieved 90%, and the Gaussian process classifier achieved 100% accuracy. This effect could not be achieved without irradiation. In addition, the SVM identified a set of BRCA genotype predictor genes, Conclusions: We conclude that after irradiation-induced DNA damage, BRCA1 and BRCA2 mutation carrier cells have a distinctive expression phenotype, and this may have a future role in predicting genotypes, with application to clinical detection and classification of mutation

Early-onset autoimmunity associated with SOCS1 haploinsufficiency

International audienc