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The structural investigation of intrinsically disordered proteins (IDPs) requires ensemble models describing the diversity of the conformational states of the molecule. Due to their probabilistic nature, there is a need for new paradigms that understand and treat IDPs from a purely statistical point of view, considering their conformational ensembles as well-defined probability distributions. In this work, we define a conformational ensemble as an ordered set of probability distributions and provide a suitable metric to detect differences between two given ensembles at the residue level, both locally and globally. The underlying geometry of the conformational space is properly integrated, one ensemble being characterized by a set of probability distributions supported on the three-dimensional Euclidean space (for global-scale comparisons) and on the two-dimensional flat torus (for local-scale comparisons). The inherent uncertainty of the data is also taken into account to provide finer estimations of the differences between ensembles. Additionally, an overall distance between ensembles is defined from the differences at the residue level. We illustrate the potential of the approach with several examples of applications for the comparison of conformational ensembles: (i) produced from molecular dynamics (MD) simulations using different force fields, and (ii) before and after refinement with experimental data. We also show the usefulness of the method to assess the convergence of MD simulations, and discuss other potential applications such as in machine-learning-based approaches. The numerical tool has been implemented in Python through easy-to-use Jupyter Notebooks available at https://gitlab.laas.fr/moma/WASCO. The structural investigation of intrinsically disordered proteins (IDPs) requires ensemble models describing the diversity of the conformational states of the molecule. Due to their probabilistic nature, there is a need for new paradigms that understand and treat IDPs from a purely statistical point of view, considering their conformational ensembles as well-defined probability distributions. In this work, we define a conformational ensemble as an ordered set of probability distributions and provide a suitable metric to detect differences between two given ensembles at the residue level, both locally and globally. The underlying geometry of the conformational space is properly integrated, one ensemble being characterized by a set of probability distributions supported on the three-dimensional Euclidean space (for global-scale comparisons) and on the two-dimensional flat torus (for local-scale comparisons). The inherent uncertainty of the data is also taken into account to provide finer estimations of the differences between ensembles. Additionally, an overall distance between ensembles is defined from the differences at the residue level. We illustrate the potential of the approach with several examples of applications for the comparison of conformational ensembles: (i) produced from molecular dynamics (MD) simulations using different force fields, and (ii) before and after refinement with experimental data. We also show the usefulness of the method to assess the convergence of MD simulations, and discuss other potential applications such as in machine-learning-based approaches. The numerical tool has been implemented in Python through easy-to-use Jupyter Notebooks available at https://gitlab.laas.fr/moma/WASCO.

In their recent work, Rosenberget al. [1] studied the dependence between the identity of synonymous codons and the distribution of the backbone dihedral angles of the translated amino acids. It has been shown that the use of synonymous codons is highly relevant in multiple biological processes including, among others, mRNA splicing, translational rates and protein folding [2, 3]. While the correlation between synonymous codons and secondary structure in translated proteins has been widely studied [4,5], Rosenberg et al. evaluated the effect of codon identity on a finer scale, analyzing whether the distribution of (ϕ, ψ) dihedral angles within secondary structure elements is significantly altered when synonymous codons are used. However, their statistical methodology is formally incorrect, casting doubt on the obtained results. The origin of the incorrectness is described in the following section. Then, using an appropriate methodology, we reanalyzed the data presented in [1]. Our results confirm the influence of the codon on the distribution of the dihedral angles, but differ from those of Rosenberget al. in the strength of significance of the differences depending on the secondary structure type. Finally, we assessed whether these findings may be affected by the structural classification or the local sequence context. These additional analyses show that codon-specific effects have similar significance in different areas of Ramachandran space, although the effect may be stronger for a particular type of secondary structure, such asβ-strands compared toα-helices. They also indicate that synonymous codon effects are stronger when considered in the context of the local sequence.

International audience; Backbone dihedral angles ϕ and ψ are the main structural descriptors of proteins and peptides. The distribution of these angles has been investigated over decades as they are essential for the validation and refinement of experimental measurements, as well as for structure prediction and design methods. The dependence of these distributions, not only on the nature of each amino acid but also on that of the closest neighbors, has been the subject of numerous studies. Although neighbor-dependent distributions are nowadays generally accepted as a good model, there is still some controversy about the combined effects of left and right neighbors. We have investigated this question using rigorous methods based on recently-developed statistical techniques. Our results unambiguously demonstrate that the influence of left and right neighbors cannot be considered independently. Consequently, three-residue fragments should be considered as the minimal building blocks to investigate polypeptide sequence-structure relationships.

International audience; In a high‐dimensional multiple testing framework, we present new confidence bounds on the false positives contained in subsets S of selected null hypotheses. These bounds are post hoc in the sense that the coverage probability holds simultaneously over all S, possibly chosen depending on the data. This article focuses on the common case of structured null hypotheses, for example, along a tree, a hierarchy, or geometrically (spatially or temporally). Following recent advances in post hoc inference, we build confidence bounds for some pre-specified forest‐structured subsets and deduce a bound for any subset S by interpolation. The proposed bounds are shown to improve substantially previous ones when the signal is locally structured. Our findings are supported both by theoretical results and numerical experiments. Moreover, our bounds can be obtained by an algorithm (with complexity bilinear in the sizes of the reference hierarchy and of the selected subset) that is implemented in the open‐source R package sansSouci available from https://github.com/pneuvial/sanssouci, making our approach operational.

In many cancers, mechanisms of gene regulation can be severely altered. Identification of deregulated genes, which do not follow the regulation processes that exist between transcription factors and their target genes, is of importance to better understand the development of the disease. We propose a methodology to detect deregulation mechanisms with a particular focus on cancer subtypes. This strategy is based on the comparison between tumoral and healthy cells. First, we use gene expression data from healthy cells to infer a reference gene regulatory network. Then, we compare it with gene expression levels in tumor samples to detect deregulated target genes. We finally measure the ability of each transcription factor to explain these deregulations. We apply our method on a public bladder cancer data set derived from The Cancer Genome Atlas project and confirm that it captures hallmarks of cancer subtypes. We also show that it enables the discovery of new potential biomarkers.

International audience; Hierarchical Agglomerative Classification (HAC) with Ward’s linkage has been widely used since its introduction in Ward (1963). The present article reviews the different extensions of the method to various input data and the constrained framework, while providing applicability conditions. In addition, various versions of the graphical representation of the results as a dendrogram are also presented and their properties are clarified. While some of these results can sometimes be found in an heteroclite literature, we clarify and complete them all using a uniform background. In particular, this study reveals an important distinction between a consistency property of the dendrogram and the absence of crossover within it. Finally, a simulation study shows that the constrained version of HAC can sometimes provide more relevant results than its unconstrained version despite the fact that the latter optimizes the objective criterion on a reduced set of solutions at each step. Overall, the article provides comprehensive recommandations for the use of HAC and constrained HAC depending on the input data as well as for the representation of the results.

We follow a post hoc, “user-agnostic” approach to false discovery control in a large-scale multiple testing framework, as introduced by Genovese and Wasserman [J. Amer. Statist. Assoc. 101 (2006) 1408–1417], Goeman and Solari [Statist. Sci. 26 (2011) 584–597]: the statistical guarantee on the number of correct rejections must hold for any set of candidate items, possibly selected by the user after having seen the data. To this end, we introduce a novel point of view based on a family of reference rejection sets and a suitable criterion, namely the joint familywise error rate over that family (JER for short). First, we establish how to derive post hoc bounds from a given JER control and analyze some general properties of this approach. We then develop procedures for controlling the JER in the case where reference regions are $p$-value level sets. These procedures adapt to dependencies and to the unknown quantity of signal (via a step-down principle). We also show interesting connections to confidence envelopes of Meinshausen [Scand. J. Stat. 33 (2006) 227–237]; Genovese and Wasserman [J. Amer. Statist. Assoc. 101 (2006) 1408–1417], the closed testing based approach of Goeman and Solari [Statist. Sci. 26 (2011) 584–597] and to the higher criticism of Donoho and Jin [Ann. Statist. 32 (2004) 962–994]. Our theoretical statements are supported by numerical experiments.

Uniformly valid confidence intervals post model selection in regression can be constructed based on Post-Selection Inference (PoSI) constants. PoSI constants are minimal for orthogonal design matrices, and can be upper bounded in function of the sparsity of the set of models under consideration, for generic design matrices. In order to improve on these generic sparse upper bounds, we consider design matrices satisfying a Restricted Isometry Property (RIP) condition. We provide a new upper bound on the PoSI constant in this setting. This upper bound is an explicit function of the RIP constant of the design matrix, thereby giving an interpolation between the orthogonal setting and the generic sparse setting. We show that this upper bound is asymptotically optimal in many settings by constructing a matching lower bound., Electronic journal of statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2018

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Summary: We describe the implementation of the method introduced by Chambaz et al. in 2012. We also demonstrate its genome-wide application to the integrative search of new regions with strong association between DNA copy number and gene expression accounting for DNA methylation in breast cancers. Availability and implementation: An open-source R package tmle.npvi is available from CRAN (http://cran.r-project.org/). Contact: pierre.neuvial@genopole.cnrs.fr

International audience; In tumoral cells, gene regulation mechanisms are severely altered, and these modifications in the regulations may be characteristic of different subtypes of cancer. However, these alterations do not necessarily induce differential expressions between the subtypes. To answer this question, we propose a statistical methodology to identify the misregulated genes given a reference network and gene expression data. Our model is based on a regulatory process in which all genes are allowed to be deregulated. We derive an EM algorithm where the hidden variables correspond to the status (under/over/normally expressed) of the genes and where the E-step is solved thanks to a message passing algorithm. Our procedure provides posterior probabilities of deregulation in a given sample for each gene. We assess the performance of our method by numerical experiments on simulations and on a bladder cancer data set.

International audience; We have profiled promoter DNA methylation alterations in 272 glioblastoma tumors in the context of The Cancer Genome Atlas (TCGA). We found that a distinct subset of samples displays concerted hypermethylation at a large number of loci, indicating the existence of a glioma-CpG island methylator phenotype (G-CIMP). We validated G-CIMP in a set of non-TCGA glioblastomas and low-grade gliomas. G-CIMP tumors belong to the proneural subgroup, are more prevalent among lower-grade gliomas, display distinct copy-number alterations, and are tightly associated with IDH1 somatic mutations. Patients with G-CIMP tumors are younger at the time of diagnosis and experience significantly improved outcome. These findings identify G-CIMP as a distinct subset of human gliomas on molecular and clinical grounds.

MotivationArray-CGH can be used to determine DNA copy number, imbalances in which are a fundamental factor in the genesis and progression of tumors. The discovery of classes with similar patterns of array-CGH profiles therefore adds to our understanding of cancer and the treatment of patients. Various input data representations for array-CGH, dissimilarity measures between tumor samples and clustering algorithms may be used for this purpose. The choice between procedures is often difficult. An evaluation procedure is therefore required to select the best class discovery method (combination of one input data representation, one dissimilarity measure and one clustering algorithm) for array-CGH. Robustness of the resulting classes is a common requirement, but no stability-based comparison of class discovery methods for array-CGH profiles has ever been reported.ResultsWe applied several class discovery methods and evaluated the stability of their solutions, with a modified version of Bertoni's [Formula: see text]-based test [1]. Our version relaxes the assumption of independency required by original Bertoni's [Formula: see text]-based test. We conclude that Minimal Regions of alteration (a concept introduced by [2]) for input data representation, sim [3] or agree [4] for dissimilarity measure and the use of average group distance in the clustering algorithm produce the most robust classes of array-CGH profiles.AvailabilityThe software is available from http://bioinfo.curie.fr/projects/cgh-clustering. It has also been partly integrated into "Visualization and analysis of array-CGH"(VAMP)[5]. The data sets used are publicly available from ACTuDB [6].

We consider multivariate two-sample tests of means, where the location shift between the two populations is expected to be related to a known graph structure. An important application of such tests is the detection of differentially expressed genes between two patient populations, as shifts in expression levels are expected to be coherent with the structure of graphs reflecting gene properties such as biological process, molecular function, regulation or metabolism. For a fixed graph of interest, we demonstrate that accounting for graph structure can yield more powerful tests under the assumption of smooth distribution shift on the graph. We also investigate the identification of nonhomogeneous subgraphs of a given large graph, which poses both computational and multiple hypothesis testing problems. The relevance and benefits of the proposed approach are illustrated on synthetic data and on breast and bladder cancer gene expression data analyzed in the context of KEGG and NCI pathways., Comment: Published in at http://dx.doi.org/10.1214/11-AOAS528 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org). arXiv admin note: substantial text overlap with arXiv:1009.5173

International audience; Breast cancers are comprised of molecularly distinct subtypes that may respond differently to pathway-targeted therapies now under development. Collections of breast cancer cell lines mirror many of the molecular subtypes and pathways found in tumors, suggesting that treatment of cell lines with candidate therapeutic compounds can guide identification of associations between molecular subtypes, pathways, and drug response. In a test of 77 therapeutic compounds, nearly all drugs showed differential responses across these cell lines, and approximately one third showed subtype-, pathway-, and/or genomic aberration-specific responses. These observations suggest mechanisms of response and resistance and may inform efforts to develop molecular assays that predict clinical response.

International audience; We define a new measure of variable importance of an exposure on a continuous outcome, accounting for potential confounders. The exposure features a reference level x0 with positive mass and a continuum of other levels. For the purpose of estimating it, we fully develop the semi-parametric estimation methodology called targeted minimum loss estimation methodology (TMLE) [vanderLaan & Rubin 2006, van der Laan & Rose 2011]. We cover the whole spectrum of its theoretical study (convergence of the iterative procedure which is at the core of the TMLE methodology; consistency and asymptotic normality of the estimator), practical implementation, simulation study and application to a genomic example that originally motivated this article. In the latter, the exposure X and response Y are, respectively, the DNA copy number and expression level of a given gene in a cancer cell. Here, the reference level is x0=2, that is the expected DNA copy number in a normal cell. The confounder is a measure of the methylation of the gene. The fact that there is no clear biological indication that X and Y can be interpreted as an exposure and a response, respectively, is not problematic.

Summary: CalMaTe calibrates preprocessed allele-specific copy number estimates (ASCNs) from DNA microarrays by controlling for single-nucleotide polymorphism-specific allelic crosstalk. The resulting ASCNs are on average more accurate, which increases the power of segmentation methods for detecting changes between copy number states in tumor studies including copy neutral loss of heterozygosity. CalMaTe applies to any ASCNs regardless of preprocessing method and microarray technology, e.g. Affymetrix and Illumina. Availability: The method is available on CRAN (http://cran.r-project.org/) in the open-source R package calmate, which also includes an add-on to the Aroma Project framework (http://www.aroma-project.org/). Contact: arubio@ceit.es Supplementary information: Supplementary data are available at Bioinformatics online.

International audience; We study the properties of false discovery rate (FDR) thresholding, viewed as a classification procedure. The ''0''-class (null) is assumed to have a known density while the ''1''-class (alternative) is obtained from the ''0''-class either by translation or by scaling. Furthermore, the ''1''-class is assumed to have a small number of elements w.r.t. the ''0''-class (sparsity). We focus on densities of the Subbotin family, including Gaussian and Laplace models. Nonasymptotic oracle inequalities are derived for the excess risk of FDR thresholding. These inequalities lead to explicit rates of convergence of the excess risk to zero, as the number m of items to be classified tends to infinity and in a regime where the power of the Bayes rule is away from 0 and 1. Moreover, these theoretical investigations suggest an explicit choice for the target level $\alpha_m$ of FDR thresholding, as a function of m. Our oracle inequalities show theoretically that the resulting FDR thresholding adapts to the unknown sparsity regime contained in the data. This property is illustrated with numerical experiments.

L'émergence récente de problèmes de grande dimension, c'est-à-dire pour lesquels le nombre p de variables excède le nombre n d'observations - et ce parfois de plusieurs ordres de grandeur, nécessite des développements majeurs en statistique. En effet, les outils mathématiques classiques pour traiter des questions usuelles comme les problèmes de tests d'hypothèses ou de classification (supervisée ou non supervisée) ont été développés dans des contextes où p < n, et ne sont donc pas applicables tels quels en grande dimension.Nous nous intéressons dans ce chapitre à la question des tests multiples en génomique, que nous introduisons grâce à l'exemple de la recherche de gènes dont le niveau d'expression (c'est-à-dire le niveau d'activité) diffère entre deux groupes de patients à partir de données de puces à ADN. On parle de gènes différentiellement exprimés, que l'on appelle analyse différentielle. Notons qu'il existe des applications de la théorie des tests multiples dans tous les domaines faisant intervenir des données de grande dimension, non seulement en génomique mais aussi par exemple en imagerie médicale ou en astronomie.

35 pages; International audience; In this chapter, we focus on statistical questions raised by the identification of copy number alterations in tumor samples using genotyping microarrays, also known as Single Nucleotide Polymorphism (SNP) arrays. We define the copy number states formally, and show how they are assessed by SNP arrays. We identify and discuss general and cancer-specific challenges for SNP array data preprocessing, and how they are addressed by existing methods. We review existing statistical methods for the detection of copy number changes along the genome. We describe the influence of two biological parameters -the proportion of normal cells in the sample and the ploidy of the tumor- on observed data. Finally, we discuss existing approaches for the detection and calling of copy number aberrations in the particular context of cancer studies, and identify statistical challenges that remain to be addressed.

Background High-throughput genotyping microarrays assess both total DNA copy number and allelic composition, which makes them a tool of choice for copy number studies in cancer, including total copy number and loss of heterozygosity (LOH) analyses. Even after state of the art preprocessing methods, allelic signal estimates from genotyping arrays still suffer from systematic effects that make them difficult to use effectively for such downstream analyses. Results We propose a method, TumorBoost, for normalizing allelic estimates of one tumor sample based on estimates from a single matched normal. The method applies to any paired tumor-normal estimates from any microarray-based technology, combined with any preprocessing method. We demonstrate that it increases the signal-to-noise ratio of allelic signals, making it significantly easier to detect allelic imbalances. Conclusions TumorBoost increases the power to detect somatic copy-number events (including copy-neutral LOH) in the tumor from allelic signals of Affymetrix or Illumina origin. We also conclude that high-precision allelic estimates can be obtained from a single pair of tumor-normal hybridizations, if TumorBoost is combined with single-array preprocessing methods such as (allele-specific) CRMA v2 for Affymetrix or BeadStudio's (proprietary) XY-normalization method for Illumina. A bounded-memory implementation is available in the open-source and cross-platform R package aroma.cn, which is part of the Aroma Project (http://www.aroma-project.org/).

This thesis deals with statistical questions raised by the analysis of high-dimensional genomic data for cancer research. In the first part, we study asymptotic properties of multiple testing procedures that aim at controlling the False Discovery Rate (FDR), that is, the expected False Discovery Proportion (FDP) among rejected hypotheses. We develop a versatile formalism to calculate the asymptotic distribution of the FDP an the associated regularity conditions, for a wide range of multiple testing procedures, and compare their asymptotic power. We then study in terms of FDR control connections between intrinsic bounds between three multiple testing problems: detection, estimation and selection. In particular, we connect convergence rates in the estimation problem to the regularity of the p-value distribution near 1. In the second part, we develop statistical methods to study DNA microarrays for cancer research. We propose a microarray normalization method that removes spatial biases while preserving the true biological signal; it combines robust regression with a mixture model with spatial constraints. Then we develop a method to infer gene regulations from gene expression data, which is based on learning and multiple testing theories. Finally, we build a genomic score to predict, for a patient treated for a breast tumor, whether or not a second cancer is a true recurrence of the first cancer.; Cette thèse traite de questions statistiques soulevées par l'analyse de données génomiques de grande dimension, dans le cadre de la recherche contre le cancer. La première partie est consacrée à l'étude des propriétés asymptotiques de procédures de tests multiples visant à contrôler l'espérance (FDR) du taux de fausses découvertes (FDP) parmi les hypothèses rejetées. On introduit un formalisme flexible qui permet de calculer la loi asymptotique du FDP et les conditions de régularité associées pour une vaste famille de procédures de tests multiples, et de comparer la puissance de ces procédures. On s'intéresse ensuite aux liens en termes de contrôle du FDR entre les bornes intrinsèques à trois problèmes de tests multiples: la détection, l'estimation, et la sélection. On relie en particulier la vitesse de convergence dans le problème d'estimation à la régularité de la loi des probabilités critiques au voisinage de 1. La seconde partie est dédiée au développement de méthodes d'analyse des données de puces à ADN en cancérologie. On propose une méthode de pré-traitement des données de puces à ADN combinant une régression robuste et un modèle de mélange avec contrainte spatiale, qui permet d'éliminer les biais spatiaux en préservant le signal biologique. On développe ensuite une méthode d'inférence de régulations entre gènes à partir de données d'expression de gènes, qui repose sur des techniques d'apprentissage informatique et de tests multiples. Enfin, on construit un test génomique permettant de déterminer, pour une patiente traitée pour un cancer du sein, si un second cancer survenant sur le même sein est ou non une récidive du premier.

We investigate the performance of a family of multiple comparison procedures for strong control of the False Discovery Rate ($\mathsf{FDR}$). The $\mathsf{FDR}$ is the expected False Discovery Proportion ($\mathsf{FDP}$), that is, the expected fraction of false rejections among all rejected hypotheses. A number of refinements to the original Benjamini-Hochberg procedure [1] have been proposed, to increase power by estimating the proportion of true null hypotheses, either implicitly, leading to one-stage adaptive procedures [4, 7] or explicitly, leading to two-stage adaptive (or plug-in) procedures [2, 21]. We use a variant of the stochastic process approach proposed by Genovese and Wasserman [11] to study the fluctuations of the $\mathsf{FDP}$ achieved with each of these procedures around its expectation, for independent tested hypotheses. We introduce a framework for the derivation of generic Central Limit Theorems for the $\mathsf{FDP}$ of these procedures, characterizing the associated regularity conditions, and comparing the asymptotic power of the various procedures. We interpret recently proposed one-stage adaptive procedures [4, 7] as fixed points in the iteration of well known two-stage adaptive procedures [2, 21]., Published in at http://dx.doi.org/10.1214/08-EJS207 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Background To distinguish new primary breast cancers from true recurrences, pangenomic analyses of DNA copy number alterations (CNAs) using single-nucleotide polymorphism arrays have proven useful. Methods The pangenomic profiles of 22 pairs of primary breast carcinoma (ductal or lobular) and ipsilateral breast cancers from the same patients were analyzed. Hierarchical clustering was performed using CNAs and DNA breakpoint information. A partial identity score developed using DNA breakpoint information was used to quantify partial identities between two tumors. The nature of ipsilateral breast cancers (true recurrence vs new primary tumor) as defined using the clustering methods and the partial identity score was compared with that based on clinical characteristics. Metastasis-free survival was compared among patients with primary tumors and true recurrences as defined using the partial identity score and by clinical characteristics. All statistical tests were two-sided. Results All methods agreed on the nature of ipsilateral breast cancers for 14 pairs of samples. For five pairs, the clinical definition disagreed with both clustering methods. For three pairs, the two clustering methods were discordant and the one using DNA breakpoints agreed with the clinical definition. The partial identity score confirmed the nature of ipsilateral breast cancers as defined by clustering of DNA breakpoints in 21 of 22 pairs. The difference in metastasis-free survival of patients with new primary tumors and those with true recurrences was not statistically significant when tumors were defined based on clinical and histologic characteristics (5-year metastasis-free survival: 76%, 95% confidence interval [CI] = 52% to 100% for new primary tumors and 38%, 95% CI = 17% to 83% for true recurrences; P = .18; new primary tumor vs true recurrence, hazard ratio = 2.8, 95% CI = 0.6 to 13.7), but the difference was statistically significant when tumors were defined using the partial identity score (5-year metastasis-free survival: 100% for new primary tumors and 29%, 95% CI = 11% to 78% for true recurrences; P = .01). Conclusions DNA breakpoint information more often agreed with the clinical determination than CNAs in this population. The partial identity score, which was calculated based on DNA breakpoints, allows statistical discrimination between new primary tumors and true recurrences that could outperform the clinical determination in terms of prognosis.

Motivation: One of the most challenging tasks in the post-genomic era is the reconstruction of transcriptional regulation networks. The goal is to identify, for each gene expressed in a particular cellular context, the regulators affecting its transcription, and the co-ordination of several regulators in specific types of regulation. DNA microarrays can be used to investigate relationships between regulators and their target genes, through simultaneous observations of their RNA levels.Results: We propose a data mining system for inferring transcriptional regulation relationships from RNA expression values. This system is particularly suitable for the detection of cooperative transcriptional regulation. We model regulatory relationships as labelled two-layer gene regulatory networks, and describe a method for the efficient learning of these bipartite networks from discretized expression data sets. We also evaluate the statistical significance of such inferred networks and validate our methods on two public yeast expression data sets.Availability: http://www.lri.fr/~elati/licorn.htmlContact: mohamed.elati@curie.frSupplementary information: Supplementary data are available at Bioinformatics online.

International audience; Assessing variations in DNA copy number is crucial for understanding constitutional or somatic diseases, particularly cancers. The recently developed array-CGH (comparative genomic hybridization) technology allows this to be investigated at the genomic level. We report the availability of a web tool for analysing array-CGH data. CAPweb (CGH array Analysis Platform on the Web) is intended as a user-friendly tool enabling biologists to completely analyse CGH arrays from the raw data to the visualiza-tion and biological interpretation. The user typically performs the following bioinformatics steps of a CGH array project within CAPweb: the secure upload of the results of CGH array image analysis and of the array annotation (genomic position of the probes); first level analysis of each array, including automatic nor-malization of the data (for correcting experimental biases), breakpoint detection and status assignment (gain, loss or normal); validation or deletion of the analysis based on a summary report and quality criteria ; visualization and biological analysis of the genomic profiles and results through a user-friendly interface. CAPweb is accessible at http://bioinfo. curie.fr/CAPweb.

Motivation: Microarray-based CGH (Comparative Genomic Hybridization), transcriptome arrays and other large-scale genomic technologies are now routinely used to generate a vast amount of genomic profiles. Exploratory analysis of this data is crucial in helping to understand the data and to help form biological hypotheses. This step requires visualization of the data in a meaningful way to visualize the results and to perform first level analyses. Results: We have developed a graphical user interface for visualization and first level analysis of molecular profiles. It is currently in use at the Institut Curie for cancer research projects involving CGH arrays, transcriptome arrays, SNP (single nucleotide polymorphism) arrays, loss of heterozygosity results (LOH), and Chromatin ImmunoPrecipitation arrays (ChIP chips). The interface offers the possibility of studying these different types of information in a consistent way. Several views are proposed, such as the classical CGH karyotype view or genome-wide multi-tumor comparison. Many functionalities for analyzing CGH data are provided by the interface, including looking for recurrent regions of alterations, confrontation to transcriptome data or clinical information, and clustering. Our tool consists of PHP scripts and of an applet written in Java. It can be run on public datasets at Availability: The VAMP software (Visualization and Analysis of array-CGH,transcriptome and other Molecular Profiles) is available upon request. It can be tested on public datasets at . The documentation is available at Contact: vamp@curie.fr

Background Array-based comparative genomic hybridization (array-CGH) is a recently developed technique for analyzing changes in DNA copy number. As in all microarray analyses, normalization is required to correct for experimental artifacts while preserving the true biological signal. We investigated various sources of systematic variation in array-CGH data and identified two distinct types of spatial effect of no biological relevance as the predominant experimental artifacts: continuous spatial gradients and local spatial bias. Local spatial bias affects a large proportion of arrays, and has not previously been considered in array-CGH experiments. Results We show that existing normalization techniques do not correct these spatial effects properly. We therefore developed an automatic method for the spatial normalization of array-CGH data. This method makes it possible to delineate and to eliminate and/or correct areas affected by spatial bias. It is based on the combination of a spatial segmentation algorithm called NEM (Neighborhood Expectation Maximization) and spatial trend estimation. We defined quality criteria for array-CGH data, demonstrating significant improvements in data quality with our method for three data sets coming from two different platforms (198, 175 and 26 BAC-arrays). Conclusion We have designed an automatic algorithm for the spatial normalization of BAC CGH-array data, preventing the misinterpretation of experimental artifacts as biologically relevant outliers in the genomic profile. This algorithm is implemented in the R package MANOR (Micro-Array NORmalization), which is described at http://bioinfo.curie.fr/projects/manor and available from the Bioconductor site http://www.bioconductor.org. It can also be tested on the CAPweb bioinformatics platform at http://bioinfo.curie.fr/CAPweb.