Grid'5000: a large scale and highly reconfigurable grid experimental testbed

Large scale distributed systems such as Grids are difficult to study from theoretical models and simulators only. Most Grids deployed at large scale are production plat-forms that are inappropriate research tools because of their limited reconfiguration, control and monitoring capa-bilities. In this paper, we present Grid’5000, a 5000 CPU nation-wide infrastructure for research in Grid computing. Grid’5000 is designed to provide a scientific tool for com-puter scientists similar to the large-scale instruments used by physicists, astronomers, and biologists. We describe the motivations, design considerations, architec-ture, control, and monitoring infrastructure of this experi-mental platform. We present configuration examples and performance results for the reconfiguration subsystem

Les nouveaux défis de la biologie moléculaire

Les nouveaux défis de la biologie moléculair

A robust approach for estimating change-points in the mean of an AR(1) process

International audienceWe consider the problem of multiple change-point estimation in the mean of an AR(1) process. Taking into account the dependence structure does not allow us to use the dynamic programming algorithm, which is the only algorithm giving the optimal solution in the independent case. We propose a robust estimator of the autocorrelation parameter, which is consistent and satisfies a central limit theorem in the Gaussian case. Then, we propose to follow the classical inference approach, by plugging this estimator in the criteria used for change-points estimation. We show that the asymptotic properties of these estimators are the same as those of the classical estimators in the independent framework. The same plug-in approach is then used to approximate the modified BIC and choose the number of segments. This method is implemented in the R package AR1 seg and is available from the Comprehensive R Archive Network (CRAN). This package is used in the simulation section in which we show that for finite sample sizes taking into account the dependence structure improves the statistical performance of the change-point estimators and of the selection criterion

L’eau et la ville du Moyen Age à nos jours

International audienc

A robust approach for estimating change-points in the mean of an AR(1) process

International audienceWe consider the problem of multiple change-point estimation in the mean of an AR(1) process. Taking into account the dependence structure does not allow us to use the dynamic programming algorithm, which is the only algorithm giving the optimal solution in the independent case. We propose a robust estimator of the autocorrelation parameter, which is consistent and satisfies a central limit theorem in the Gaussian case. Then, we propose to follow the classical inference approach, by plugging this estimator in the criteria used for change-points estimation. We show that the asymptotic properties of these estimators are the same as those of the classical estimators in the independent framework. The same plug-in approach is then used to approximate the modified BIC and choose the number of segments. This method is implemented in the R package AR1 seg and is available from the Comprehensive R Archive Network (CRAN). This package is used in the simulation section in which we show that for finite sample sizes taking into account the dependence structure improves the statistical performance of the change-point estimators and of the selection criterion

Two-dimensional segmentation for analyzing HiC data

Motivation: The spatial conformation of the chromosome has a deep influence on gene regulation and expression. Hi-C technology allows the evaluation of the spatial proximity between any pair of loci along the genome. It results in a data matrix where blocks corresponding to (self-)interacting regions appear. The delimitation of such blocks is critical to better understand the spatial organization of the chromatin. From a computational point of view, it results in a 2D segmentation problem. Results: We focus on the detection of cis-interacting regions, which appear to be prominent in observed data. We define a block-wise segmentation model for the detection of such regions. We prove that the maximization of the likelihood with respect to the block boundaries can be rephrased in terms of a 1D segmentation problem, for which the standard dynamic programming applies. The performance of the proposed methods is assessed by a simulation study on both synthetic and resampled data. A comparative study on public data shows good concordance with biologically confirmed regions