Integrative epigenomic mapping defines four main chromatin states in ArabidopsisThis first comprehensive view of the Arabidopsis epigenome reveals that it is organized into four main chromatin types based on the integrative mapping of a broad set of 11 histone marks and DNA methylation in seedlings.

The Erdös-Rényi model of a network is simple and possesses many explicit expressions for average and asymptotic properties, but it does not fit well to real-world networks. The vertices of those networks are often structured in unknown classes (functionally related proteins or social communities) with different connectivity properties. The stochastic block structures model was proposed for this purpose in the context of social sciences, using a Bayesian approach. We consider the same model in a frequentest statistical framework. We give the degree distribution and the clustering coefficient associated with this model, a variational method to estimate its parameters and a model selection criterion to select the number of classes. This estimation procedure allows us to deal with large networks containing thousands of vertices. The method is used to uncover the modular structure of a network of enzymatic reactions.

The organization of the Escherichia coli chromosome into insulated macrodomains influences the segregation of sister chromatids and the mobility of chromosomal DNA. Here, we report that organization of the Terminus region (Ter) into a macrodomain relies on the presence of a 13 bp motif called matS repeated 23 times in the 800-kb-long domain. matS sites are the main targets in the E. coli chromosome of a newly identified protein designated MatP. MatP accumulates in the cell as a discrete focus that colocalizes with the Ter macrodomain. The effects of MatP inactivation reveal its role as main organizer of the Ter macrodomain: in the absence of MatP, DNA is less compacted, the mobility of markers is increased, and segregation of Ter macrodomain occurs early in the cell cycle. Our results indicate that a specific organizational system is required in the Terminus region for bacterial chromosome management during the cell cycle.

As more and more network-structured data sets are available, the statistical analysis of valued graphs has become common place. Looking for a latent structure is one of the many strategies used to better understand the behavior of a network. Several methods already exist for the binary case. We present a model-based strategy to uncover groups of nodes in valued graphs. This framework can be used for a wide span of parametric random graphs models and allows to include covariates. Variational tools allow us to achieve approximate maximum likelihood estimation of the parameters of these models. We provide a simulation study showing that our estimation method performs well over a broad range of situations. We apply this method to analyze host--parasite interaction networks in forest ecosystems.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS361 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Down syndrome caused by chromosome 21 trisomy is the most common genetic cause of mental retardation in humans. Disruption of the phenotype is thought to be the result of gene-dosage imbalance. Variations in chromosome 21 gene expression in Down syndrome were analyzed in lymphoblastoid cells derived from patients and control individuals. Of the 359 genes and predictions displayed on a specifically designed high-content chromosome 21 microarray, one-third were expressed in lymphoblastoid cells. We performed a mixed-model analysis of variance to find genes that are differentially expressed in Down syndrome independent of sex and interindividual variations. In addition, we identified genes with variations between Down syndrome and control samples that were significantly different from the gene-dosage effect (1.5). Microarray data were validated by quantitative polymerase chain reaction. We found that 29% of the expressed chromosome 21 transcripts are overexpressed in Down syndrome and correspond to either genes or open reading frames. Among these, 22% are increased proportional to the gene-dosage effect, and 7% are amplified. The other 71% of expressed sequences are either compensated (56%, with a large proportion of predicted genes and antisense transcripts) or highly variable among individuals (15%). Thus, most of the chromosome 21 transcripts are compensated for the gene-dosage effect. Overexpressed genes are likely to be involved in the Down syndrome phenotype, in contrast to the compensated genes. Highly variable genes could account for phenotypic variations observed in patients. Finally, we show that alternative transcripts belonging to the same gene are similarly regulated in Down syndrome but sense and antisense transcripts are not.

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.Availability and implementation: The HiCseg R package is available from the Comprehensive R Archive Network and from the Web page of the corresponding author.Contact: celine.levy-leduc@agroparistech.fr

We elicit willingness‐to‐pay information for similar food products that differ only in their content of genetically modified organisms (GMOs). Participants in the experiment are a demographically representative sample of French consumers. 35% of participants are unwilling to purchase products made with GMOs, 23% are indifferent or value the presence of GMOs, and 42% are willing to purchase them if they are sufficiently inexpensive. The results contrast with surveys that indicate overwhelming opposition to GM foods. There is a surplus to be gained from the segregation of the market for food products into a GMO‐free segment and a segment allowing GMOs.

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