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Purpose: A gene expression signature, predictive for local recurrence after breast-conserving treatment, has previously been identified from a series of 165 young patients with breast cancer. We evaluated this signature on both another platform and an independent series, compared its performance with other published gene-sets, and investigated the gene expression profile of a larger data set.Experimental Design: Gene expression tumor profiles were obtained on 148 of the initial 165 Dutch patients and on an independent validation series of 195 French patients. Both unsupervised and supervised classifications were used to study the gene expression profile of the 343 breast cancers and to identify subgroups that differ for their risk of local recurrence.Results: The previous local recurrence signature was validated across platforms. However, when applied to the French patients, the signature did not reproduce its reported performance and did not better classify the patients than other published gene sets. Hierarchical clustering of all 343 breast cancers did not show any grouping reflecting local recurrence status. Genes related to proliferation were found differentially expressed between patients with or without local recurrence only in triple-negative tumors. Supervised classification revealed no significant gene set predictive for local recurrence or able to outperform classification based on clinical variables.Conclusions: Although the previously identified local recurrence signature was robust on another platform, we were neither able to validate it on an independent data set, nor able to define a strong gene expression classifier for local recurrence using a larger data set. We conclude that there are no significant differences in gene expression pattern in tumors from patients with and without local recurrence after breast-conserving treatment. Clin Cancer Res; 18(6); 1704–15. ©2012 AACR.

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Background Encephalitis is a worldwide public health issue, with a substantially high burden among children in southeast Asia. We aimed to determine the causes of encephalitis in children admitted to hospitals across the Greater Mekong region by implementing a comprehensive state-of-the-art diagnostic procedure harmonised across all centres, and identifying clinical characteristics related to patients’ conditions. Methods In this multicentre, observational, prospective study of childhood encephalitis, four referral hospitals in Cambodia, Vietnam, Laos, and Myanmar recruited children (aged 28 days to 16 years) who presented with altered mental status lasting more than 24 h and two of the following minor criteria: fever (within the 72 h before or after presentation), one or more generalised or partial seizures (excluding febrile seizures), a new-onset focal neurological deficit, cerebrospinal fluid (CSF) white blood cell count of 5 per mL or higher, or brain imaging (CT or MRI) suggestive of lesions of encephalitis. Comprehensive diagnostic procedures were harmonised across all centres, with first-line testing was done on samples taken at inclusion and results delivered within 24 h of inclusion for main treatable causes of disease and second-line testing was done thereafter for mostly non-treatable causes. An independent expert medical panel reviewed the charts and attribution of causes of all the included children. Using multivariate analyses, we assessed risk factors associated with unfavourable outcomes (ie, severe neurological sequelae and death) at discharge using data from baseline and day 2 after inclusion. This study is registered with ClinicalTrials.gov, NCT04089436, and is now complete. Findings Between July 28, 2014, and Dec 31, 2017, 664 children with encephalitis were enrolled. Median age was 4·3 years (1·8–8·8), 295 (44%) children were female, and 369 (56%) were male. A confirmed or probable cause of encephalitis was identified in 425 (64%) patients: 216 (33%) of 664 cases were due to Japanese encephalitis virus, 27 (4%) were due to dengue virus, 26 (4%) were due to influenza virus, 24 (4%) were due to herpes simplex virus 1, 18 (3%) were due to Mycobacterium tuberculosis, 17 (3%) were due to Streptococcus pneumoniae, 17 (3%) were due to enterovirus A71, 74 (9%) were due to other pathogens, and six (1%) were due to autoimmune encephalitis. Diagnosis was made within 24 h of admission to hospital for 83 (13%) of 664 children. 119 (18%) children had treatable conditions and 276 (42%) had conditions that could have been preventable by vaccination. At time of discharge, 153 (23%) of 664 children had severe neurological sequelae and 83 (13%) had died. In multivariate analyses, risk factors for unfavourable outcome were diagnosis of M tuberculosis infection upon admission (odds ratio 3·23 [95% CI 1·04–10·03]), coma on day 2 (2·90 [1·78–4·72]), supplementary oxygen requirement (1·89 [1·25–2·86]), and more than 1 week duration between symptom onset and admission to hospital (3·03 [1·68–5·48]). At 1 year after inclusion, of 432 children who were discharged alive from hospital with follow-up data, 24 (5%) had died, 129 (30%) had neurological sequelae, and 279 (65%) had completely recovered. Interpretation In southeast Asia, most causes of childhood encephalitis are either preventable or treatable, with Japanese encephalitis virus being the most common cause. We provide crucial information that could guide public health policy to improve diagnostic, vaccination, and early therapeutic guidelines on childhood encephalitis in the Greater Mekong region. Funding Institut Pasteur, Institut Pasteur International Network, Fondation Merieux, Aviesan Sud, INSERM, Wellcome Trust, Institut de Recherche pour le Développement (IRD), and Fondation Total. Translations For the Khmer, Lao, Vietnamese and Burmese translation of the abstract see Supplementary Materials section.

Introduction Guadeloupe, a French West Indies island, has been fiercely affected by two large waves of COVID.Therapeutic approach was different between the two waves in the intensive care unit (ICU). We aimed to compare the two different periods in terms of characteristics and outcomes and to evaluate risk factors associated with 60-day mortality in our overall cohort. Methods All consecutive patients with laboratory confirmed COVID-19 pneumonia and requiring oxygen support admitted in our ICU unit of University Hospital of Guadeloupe were prospectively included. Patients were treated during the first wave with a combination of Hydroxychloroquine and Azithromycin and during the second wave with dexamethasone and reinforced anticoagulation. Results In our cohort, 187 patients were included, 31 during the first one and 156 during the second. Patients were mostly male (69%) with a median age of 64years old. Patients tend to be younger during the second wave and body mass index was higher (respectively 31 vs 27kg/m2, p=0.01). Overall mortality at Day 60 was high (45%) and not different between the two waves. Among patients under mechanical ventilation risk factors associated with death in a multivariate analysis were a high number of comorbidities, a high level of SOFA score and the delay of invasive mechanical ventilation (IMV) onset after admission in ICU (OR=1.6 (95% CI 1.2 – 2.4). Conclusion Although therapeutics approach evolve, COVID-19 severe pneumonia is still associated with a high mortality rate in ICU.

Live imaging of the pHluorin tagged Soluble N-ethylmaleimide-sensitive-factor Attachment protein REceptor (v-SNARE) Vesicle-associated membrane protein 7 (VAMP7) by total internal reflection fluorescence microscopy (TIRFM) is a straightforward way to explore secretion from the lysosomal compartment. Taking advantage of cell culture on micropatterned surfaces to normalize cell shape, a variety of statistical tools were employed to perform a spatial analysis of secretory patterns. Using Ripley's K function and a statistical test based on the nearest neighbor distance (NND), we confirmed that secretion from lysosomes is not a random process but shows significant clustering. Of note, our analysis revealed that exocytosis events are also clustered in nonadhesion areas, indicating that adhesion molecules are not the only structures that can induce secretory hot spots at the plasma membrane. Still, we found that cell adhesion enhances clustering. In addition to precisely defined adhesive and nonadhesive areas, the circular geometry of these micropatterns allows the use of polar coordinates, simplifying analyses. We used Kernel Density Estimation (KDE) and the cumulative distribution function on polar coordinates of exocytosis events to identify enriched areas of exocytosis. In ring-shaped micropattern cells, clustering occurred at the border between the adhesive and nonadhesive areas. Our analysis illustrates how statistical tools can be employed to investigate spatial distributions of diverse biological processes.

Background The World Health Organization (WHO) proposed guidelines on dengue clinical classification in 1997 and more recently in 2009 for the clinical management of patients. The WHO 1997 classification defines three categories of dengue infection according to severity: dengue fever (DF), dengue hemorrhagic fever (DHF), and dengue shock syndrome (DSS). Alternative WHO 2009 guidelines provide a cross-sectional classification aiming to discriminate dengue fever from dengue with warning signs (DWWS) and severe dengue (SD). The primary objective of this study was to perform a comparison of two dengue classifications. The secondary objective was to describe the changes of hematological and biochemical parameters occurring in patients presenting with different degrees of severity during the course of the disease, since progression to more severe clinical forms is unpredictable. Methodology/Principal findings We performed a prospective, monocentric, cross-sectional study of hospitalized children in Cambodia, aged from 2 to 15 years old with severe and non-severe dengue. We enrolled 243 patients with acute dengue-like illness: 71.2% were dengue infections confirmed using quantitative reverse transcription PCR or NS1 antigen capture ELISA, of which 87.2% and 9.0% of DF cases were respectively classified DWWS and SD, and 35.9% of DHF were designated SD using an adapted WHO 2009 classification for SD case definition. Systematic use of ultrasound at patient admission was crucial for detecting plasma leakage. No difference was observed in the concentration of secreted NS1 protein between different dengue severity groups. Lipid profiles were different between DWWS and SD at admission, characterized by a decrease in total cholesterol, HDL cholesterol, and LDL cholesterol, in SD. Conclusions/Significance Our results show discrepancies between the two classifications, including misclassification of severe dengue cases as mild cases by the WHO 1997 classification. Using an adapted WHO 2009 classification, SD more precisely defines the group of patients requiring careful clinical care at a given time during hospitalization., Author summary Dengue is a viral disease that results in various health conditions, ranging from strictly asymptomatic infections to life-threatening severe dengue. Here we have compared the two World Health Organization (WHO) dengue classification schemes from 1997 and 2009, which define different categories of dengue fever and have been at the root of much debate in the dengue research community. We enrolled a Cambodian pediatric cohort of hospitalized dengue-confirmed patients, with clinical and biological follow-up. Our findings demonstrate that (i) the WHO 1997 classification is a longitudinal dengue-case classification which uses strict prerequisite clinical and/or biological signs to move from one level of severity to another, and (ii) the WHO 2009 classification, being a transversal dengue-case classifier, is more flexible and when adapted with systematic use of ultrasound is more accurate in identifying severe dengue case for timely and appropriate clinical management of hospitalized patients. Our laboratory results found that lipid profiles exhibited changes and differences between different dengue severity groups at hospital.

Mineralocorticoid receptors (MRs) and glucocorticoid receptors (GRs) are two closely related hormone-activated transcription factors that regulate major pathophysiologic functions. High homology between these receptors accounts for the crossbinding of their corresponding ligands, MR being activated by both aldosterone and cortisol and GR essentially activated by cortisol. Their coexpression and ability to bind similar DNA motifs highlight the need to investigate their respective contributions to overall corticosteroid signaling. Here, we decipher the transcriptional regulatory mechanisms that underlie selective effects of MRs and GRs on shared genomic targets in a human renal cellular model. Kinetic, serial, and sequential chromatin immunoprecipitation approaches were performed on the period circadian protein 1 ( PER1) target gene, providing evidence that both receptors dynamically and cyclically interact at the same target promoter in a specific and distinct transcriptional signature. During this process, both receptors regulate PER1 gene by binding as homo- or heterodimers to the same promoter region. Our results suggest a novel level of MR-GR target gene regulation, which should be considered for a better and integrated understanding of corticosteroid-related pathophysiology.-Le Billan, F., Amazit, L., Bleakley, K., Xue, Q.-Y., Pussard, E., Lhadj, C., Kolkhof, P., Viengchareun, S., Fagart, J., Lombes, M. Corticosteroid receptors adopt distinct cyclical transcriptional signatures.

An 18-gene RNA signature detects severe cases among young Cambodian secondary-infected dengue patients and yields insights into the underlying pathogenesis. We present evidence that the detection is robust for peripheral blood mononuclear cells and whole blood and different experimental techniques., Background Early detection of severe dengue can improve patient care and survival. To date, no reliable single-gene biomarker exists. We hypothesized that robust multigene signatures exist. Methods We performed a prospective study on Cambodian dengue patients aged 4 to 22 years. Peripheral blood mononuclear cells (PBMCs) were obtained at hospital admission. We analyzed 42 transcriptomic profiles of patients with secondary dengue infected with dengue serotype 1. Our novel signature discovery approach controls the number of included genes and captures nonlinear relationships between transcript concentrations and severity. We evaluated the signature on secondary cases infected with different serotypes using 2 datasets: 22 PBMC samples from additional patients in our cohort and 32 whole blood samples from an independent cohort. Results We identified an 18-gene signature for detecting severe dengue in patients with secondary infection upon hospital admission with a sensitivity of 0.93 (95% confidence interval [CI], .82–.98), specificity of 0.67 (95% CI, .53–.80), and area under the receiver operating characteristic curve (AUC) of 0.86 (95% CI, .75–.97). At validation, the signature had empirical AUCs of 0.85 (95% CI, .69–1.00) and 0.83 (95% CI, .68–.98) for the PBMCs and whole blood datasets, respectively. Conclusions The signature could detect severe dengue in secondary-infected patients upon hospital admission. Its genes offer new insights into the pathogenesis of severe dengue.

International audience; Clinical symptoms of dengue virus (DENV) infection, the most prevalent arthropod-borne viral disease, range from classical mild dengue fever to severe, life-threatening dengue shock syndrome. However, most DENV infections cause few or no symptoms. Asymptomatic DENV-infected patients provide a unique opportunity to decipher the host immune responses leading to virus elimination without negative impact on an individual's health. We used an integrated approach of transcriptional profiling and immunological analysis to compare a Cambodian population of strictly asymptomatic viremic individuals with clinical dengue patients. Whereas inflammatory pathways and innate immune response pathways were similar between asymptomatic individuals and clinical dengue patients, expression of proteins related to antigen presentation and subsequent T cell and B cell activation pathways was differentially regulated, independent of viral load and previous DENV infection history. Feedback mechanisms controlled the immune response in asymptomatic viremic individuals, as demonstrated by increased activation of T cell apoptosis-related pathways and FcγRIIB (Fcγ receptor IIB) signaling associated with decreased anti-DENV-specific antibody concentrations. Together, our data illustrate that symptom-free DENV infection in children is associated with increased activation of the adaptive immune compartment and proper control mechanisms, leading to elimination of viral infection without excessive immune activation, with implications for novel vaccine development strategies.

International audience; The detection of change-points in a spatially or time ordered data sequence is an important problem in many fields such as genetics and finance. We derive the asymptotic distribution of a statistic recently suggested for detecting change-points. Simulation of its estimated limit distribution leads to a new and computationally efficient change-point detection algorithm, which can be used on very long signals. We assess the algorithm via simulations and on previously benchmarked real-world data sets.

By micropatterning cells in polarized shapes the global distribution of endomembranes can be compared and subtle but statistically significant changes can be detected from only tens of cells. We developed a computational imaging approach that describes the three-dimensional spatial organization of endomembranes from micromanipulation-normalized mammalian cells with probabilistic density maps. Applied to several well-known marker proteins, this approach revealed the average steady-state organization of early endosomes, multivesicular bodies or lysosomes, endoplasmic reticulum exit sites, the Golgi apparatus and Golgi-derived transport carriers in crossbow-shaped cells. The steady-state organization of each tested endomembranous population was well-defined, unique and in some cases depended on the cellular adhesion geometry. Density maps of all endomembrane populations became stable when pooling several tens of cells only and were reproducible in independent experiments, allowing construction of a standardized cell model. We detected subtle changes in steady-state organization induced by disruption of the cellular cytoskeleton, with statistical significance observed for just 20 cells. Thus, combining micropatterning with construction of endomembrane density maps allows the systematic study of intracellular trafficking determinants.

Motivation: In silico prediction of drug–target interactions from heterogeneous biological data is critical in the search for drugs for known diseases. This problem is currently being attacked from many different points of view, a strong indication of its current importance. Precisely, being able to predict new drug–target interactions with both high precision and accuracy is the holy grail, a fundamental requirement for in silico methods to be useful in a biological setting. This, however, remains extremely challenging due to, amongst other things, the rarity of known drug–target interactions. Results: We propose a novel supervised inference method to predict unknown drug–target interactions, represented as a bipartite graph. We use this method, known as bipartite local models to first predict target proteins of a given drug, then to predict drugs targeting a given protein. This gives two independent predictions for each putative drug–target interaction, which we show can be combined to give a definitive prediction for each interaction. We demonstrate the excellent performance of the proposed method in the prediction of four classes of drug–target interaction networks involving enzymes, ion channels, G protein-coupled receptors (GPCRs) and nuclear receptors in human. This enables us to suggest a number of new potential drug–target interactions. Availability: An implementation of the proposed algorithm is available upon request from the authors. Datasets and all prediction results are available at http://cbio.ensmp.fr/~yyamanishi/bipartitelocal/. Contact: kevbleakley@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

We propose a nonlinear mixed-effects framework to jointly model longitudinal and repeated time-to-event data. A parametric nonlin-ear mixed-effects model is used for the longitudinal observations and a parametric mixed-effects hazard model for repeated event times. We show the importance for parameter estimation of properly calculating the conditional density of the observations (given the individual parameters) in the presence of interval and/or right censoring. Parameters are estimated by maximizing the exact joint likelihood with the Stochastic Approximation Expectation-Maximization algorithm. This workflow for joint models is now implemented in the Monolix software, and illustrated here on five simulated and two real data sets.

The problem of graph inference, or graph reconstruction, is to predict the presence or absence of edges between a set of given points known to form the vertices of a graph. Motivated by various applications including communication networks and systems biology, we propose a general model for studying the problem of graph inference in a supervised learning framework. In our setting, both the graph vertices and edges are assumed to be random, with a probability distribution that possibly depends on the size of the graph. We show that the problem can be transformed into one where we can use statistical learning methods based on empirical minimization of natural estimates of the reconstruction risk.Convex risk minimizationmethods are also studied to provide a theoretical framework for reconstruction algorithms based on boosting and support vector machines. Our approach is illustrated on simulated graphs.

Summary: More and more cancer studies use next-generation sequencing (NGS) data to detect various types of genomic variation. However, even when researchers have such data at hand, single-nucleotide polymorphism arrays have been considered necessary to assess copy number alterations and especially loss of heterozygosity (LOH). Here, we present the tool Control-FREEC that enables automatic calculation of copy number and allelic content profiles from NGS data, and consequently predicts regions of genomic alteration such as gains, losses and LOH. Taking as input aligned reads, Control-FREEC constructs copy number and B-allele frequency profiles. The profiles are then normalized, segmented and analyzed in order to assign genotype status (copy number and allelic content) to each genomic region. When a matched normal sample is provided, Control-FREEC discriminates somatic from germline events. Control-FREEC is able to analyze overdiploid tumor samples and samples contaminated by normal cells. Low mappability regions can be excluded from the analysis using provided mappability tracks. Availability: C++ source code is available at: http://bioinfo.curie.fr/projects/freec/ Contact: freec@curie.fr Supplementary information: Supplementary data are available at Bioinformatics online.

Purpose: A gene expression signature, predictive for local recurrence after breast-conserving treatment, has previously been identified from a series of 165 young patients with breast cancer. We evaluated this signature on both another platform and an independent series, compared its performance with other published gene-sets, and investigated the gene expression profile of a larger data set. Experimental Design: Gene expression tumor profiles were obtained on 148 of the initial 165 Dutch patients and on an independent validation series of 195 French patients. Both unsupervised and supervised classifications were used to study the gene expression profile of the 343 breast cancers and to identify subgroups that differ for their risk of local recurrence. Results: The previous local recurrence signature was validated across platforms. However, when applied to the French patients, the signature did not reproduce its reported performance and did not better classify the patients than other published gene sets. Hierarchical clustering of all 343 breast cancers did not show any grouping reflecting local recurrence status. Genes related to proliferation were found differentially expressed between patients with or without local recurrence only in triple-negative tumors. Supervised classification revealed no significant gene set predictive for local recurrence or able to outperform classification based on clinical variables. Conclusions: Although the previously identified local recurrence signature was robust on another platform, we were neither able to validate it on an independent data set, nor able to define a strong gene expression classifier for local recurrence using a larger data set. We conclude that there are no significant differences in gene expression pattern in tumors from patients with and without local recurrence after breast-conserving treatment. Clin Cancer Res; 18(6); 1704–15. ©2012 AACR.

Time series prediction covers a vast field of every-day statistical applications in medical, environmental and economic domains. In this paper we develop nonparametric prediction strategies based on the combination of a set of 'experts' and show the universal consistency of these strategies under a minimum of conditions. We perform an in-depth analysis of real-world data sets and show that these nonparametric strategies are more flexible, faster and generally outperform ARMA methods in terms of normalized cumulative prediction error., Comment: article + 2 figures

Background DNA barcoding aims to assign individuals to given species according to their sequence at a small locus, generally part of the CO1 mitochondrial gene. Amongst other issues, this raises the question of how to deal with within-species genetic variability and potential transpecific polymorphism. In this context, we examine several assignation methods belonging to two main categories: (i) phylogenetic methods (neighbour-joining and PhyML) that attempt to account for the genealogical framework of DNA evolution and (ii) supervised classification methods (k-nearest neighbour, CART, random forest and kernel methods). These methods range from basic to elaborate. We investigated the ability of each method to correctly classify query sequences drawn from samples of related species using both simulated and real data. Simulated data sets were generated using coalescent simulations in which we varied the genealogical history, mutation parameter, sample size and number of species. Results No method was found to be the best in all cases. The simplest method of all, "one nearest neighbour", was found to be the most reliable with respect to changes in the parameters of the data sets. The parameter most influencing the performance of the various methods was molecular diversity of the data. Addition of genetically independent loci - nuclear genes - improved the predictive performance of most methods. Conclusion The study implies that taxonomists can influence the quality of their analyses either by choosing a method best-adapted to the configuration of their sample, or, given a certain method, increasing the sample size or altering the amount of molecular diversity. This can be achieved either by sequencing more mtDNA or by sequencing additional nuclear genes. In the latter case, they may also have to modify their data analysis method.

Background Nucleotides are trimmed from the ends of variable (V), diversity (D) and joining (J) genes during immunoglobulin (IG) and T cell receptor (TR) rearrangements in B cells and T cells of the immune system. This trimming is followed by addition of nucleotides at random, forming the N regions (N for nucleotides) of the V-J and V-D-J junctions. These processes are crucial for creating diversity in the immune response since the number of trimmed nucleotides and the number of added nucleotides vary in each B or T cell. IMGT® sequence analysis tools, IMGT/V-QUEST and IMGT/JunctionAnalysis, are able to provide detailed and accurate analysis of the final observed junction nucleotide sequences (tool "output"). However, as trimmed nucleotides can potentially be replaced by identical N region nucleotides during the process, the observed "output" represents a biased estimate of the "true trimming process." Results A probabilistic approach based on an analysis of the standardized tool "output" is proposed to infer the probability distribution of the "true trimmming process" and to provide plausible biological hypotheses explaining this process. We collated a benchmark dataset of TR alpha (TRA) and TR gamma (TRG) V-J rearranged sequences and junctions analysed with IMGT/V-QUEST and IMGT/JunctionAnalysis, the nucleotide sequence analysis tools from IMGT®, the international ImMunoGeneTics information system®, http://imgt.cines.fr. The standardized description of the tool output is based on the IMGT-ONTOLOGY axioms and concepts. We propose a simple first-order model that attempts to transform the observed "output" probability distribution into an estimate closer to the "true trimming process" probability distribution. We use this estimate to test the hypothesis that Poisson processes are involved in trimming. This hypothesis was not rejected at standard confidence levels for three of the four trimming processes: TRAV, TRAJ and TRGV. Conclusion By using trimming of rearranged TR genes as a benchmark, we show that a probabilistic approach, applied to IMGT® standardized tool "outputs" opens the way to plausible hypotheses on the events involved in the "true trimming process" and eventually to an exact quantification of trimming itself. With increasing high-throughput of standardized immunogenetics data, similar probabilistic approaches will improve understanding of processes so far only characterized by the "output" of standardized tools.

Motivation: Inference and reconstruction of biological networks from heterogeneous data is currently an active research subject with several important applications in systems biology. The problem has been attacked from many different points of view with varying degrees of success. In particular, predicting new edges with a reasonable false discovery rate is highly demanded for practical applications, but remains extremely challenging due to the sparsity of the networks of interest. Results: While most previous approaches based on the partial knowledge of the network to be inferred build global models to predict new edges over the network, we introduce here a novel method which predicts whether there is an edge from a newly added vertex to each of the vertices of a known network using local models. This involves learning individually a certain subnetwork associated with each vertex of the known network, then using the discovered classification rule associated with only that vertex to predict the edge to the new vertex. Excellent experimental results are shown in the case of metabolic and protein–protein interaction network reconstruction from a variety of genomic data. Availability: An implementation of the proposed algorithm is available upon request from the authors. Contact: Jean-Philippe.Vert@ensmp.fr

Summary: We present a tool for control-free copy number alteration (CNA) detection using deep-sequencing data, particularly useful for cancer studies. The tool deals with two frequent problems in the analysis of cancer deep-sequencing data: absence of control sample and possible polyploidy of cancer cells. FREEC (control-FREE Copy number caller) automatically normalizes and segments copy number profiles (CNPs) and calls CNAs. If ploidy is known, FREEC assigns absolute copy number to each predicted CNA. To normalize raw CNPs, the user can provide a control dataset if available; otherwise GC content is used. We demonstrate that for Illumina single-end, mate-pair or paired-end sequencing, GC-contentr normalization provides smooth profiles that can be further segmented and analyzed in order to predict CNAs. Availability: Source code and sample data are available at http://bioinfo-out.curie.fr/projects/freec/. Contact: freec@curie.fr Supplementary information: Supplementary data are available at Bioinformatics online.