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Dive into the research topics where Eric Viara is active.

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Featured researches published by Eric Viara.


Bioinformatics | 2006

VAMP: Visualization and analysis of array-CGH, transcriptome and other molecular profiles

Philippe La Rosa; Eric Viara; Philippe Hupé; Gaëlle Pierron; Stéphane Liva; Pierre Neuvial; Isabel Brito; Séverine Lair; Nicolas Servant; Nicolas Robine; Elodie Manié; Caroline Brennetot; Isabelle Janoueix-Lerosey; Virginie Raynal; Nadège Gruel; Céline Rouveirol; Nicolas Stransky; Marc-Henri Stern; Olivier Delattre; Alain Aurias; François Radvanyi; Emmanuel Barillot

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 http://bioinfo.curie.fr/vamp 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 http://bioinfo.curie.fr/vamp. The documentation is available at http://bioinfo.curie.fr/vamp/doc.


Genome Biology | 2015

HiC-Pro: an optimized and flexible pipeline for Hi-C data processing

Nicolas Servant; Nelle Varoquaux; Bryan R. Lajoie; Eric Viara; Chong-Jian Chen; Jean-Philippe Vert; Edith Heard; Job Dekker; Emmanuel Barillot

HiC-Pro is an optimized and flexible pipeline for processing Hi-C data from raw reads to normalized contact maps. HiC-Pro maps reads, detects valid ligation products, performs quality controls and generates intra- and inter-chromosomal contact maps. It includes a fast implementation of the iterative correction method and is based on a memory-efficient data format for Hi-C contact maps. In addition, HiC-Pro can use phased genotype data to build allele-specific contact maps. We applied HiC-Pro to different Hi-C datasets, demonstrating its ability to easily process large data in a reasonable time. Source code and documentation are available at http://github.com/nservant/HiC-Pro.


Bioinformatics | 2006

Computation of recurrent minimal genomic alterations from array-CGH data

Céline Rouveirol; Nicolas Stransky; Philippe Hupé; Philippe La Rosa; Eric Viara; Emmanuel Barillot; François Radvanyi

MOTIVATION The identification of recurrent genomic alterations can provide insight into the initiation and progression of genetic diseases, such as cancer. Array-CGH can identify chromosomal regions that have been gained or lost, with a resolution of approximately 1 mb, for the cutting-edge techniques. The extraction of discrete profiles from raw array-CGH data has been studied extensively, but subsequent steps in the analysis require flexible, efficient algorithms, particularly if the number of available profiles exceeds a few tens or the number of array probes exceeds a few thousands. RESULTS We propose two algorithms for computing minimal and minimal constrained regions of gain and loss from discretized CGH profiles. The second of these algorithms can handle additional constraints describing relevant regions of copy number change. We have validated these algorithms on two public array-CGH datasets. AVAILABILITY From the authors, upon request. CONTACT celine@lri.fr SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.


ONCOGENESIS , 4 (ARTN e16) (2015) | 2015

Atlas of Cancer Signalling Network: a systems biology resource for integrative analysis of cancer data with Google Maps

Inna Kuperstein; Eric Bonnet; Nguyen Ha; David P. A. Cohen; Eric Viara; Luca Grieco; Simon Fourquet; Laurence Calzone; Russo C; Kondratova M; Marie Dutreix; Emmanuel Barillot; Andrei Zinovyev

Cancerogenesis is driven by mutations leading to aberrant functioning of a complex network of molecular interactions and simultaneously affecting multiple cellular functions. Therefore, the successful application of bioinformatics and systems biology methods for analysis of high-throughput data in cancer research heavily depends on availability of global and detailed reconstructions of signalling networks amenable for computational analysis. We present here the Atlas of Cancer Signalling Network (ACSN), an interactive and comprehensive map of molecular mechanisms implicated in cancer. The resource includes tools for map navigation, visualization and analysis of molecular data in the context of signalling network maps. Constructing and updating ACSN involves careful manual curation of molecular biology literature and participation of experts in the corresponding fields. The cancer-oriented content of ACSN is completely original and covers major mechanisms involved in cancer progression, including DNA repair, cell survival, apoptosis, cell cycle, EMT and cell motility. Cell signalling mechanisms are depicted in detail, together creating a seamless ‘geographic-like’ map of molecular interactions frequently deregulated in cancer. The map is browsable using NaviCell web interface using the Google Maps engine and semantic zooming principle. The associated web-blog provides a forum for commenting and curating the ACSN content. ACSN allows uploading heterogeneous omics data from users on top of the maps for visualization and performing functional analyses. We suggest several scenarios for ACSN application in cancer research, particularly for visualizing high-throughput data, starting from small interfering RNA-based screening results or mutation frequencies to innovative ways of exploring transcriptomes and phosphoproteomes. Integration and analysis of these data in the context of ACSN may help interpret their biological significance and formulate mechanistic hypotheses. ACSN may also support patient stratification, prediction of treatment response and resistance to cancer drugs, as well as design of novel treatment strategies.


Nucleic Acids Research | 2006

CAPweb: a bioinformatics CGH array Analysis Platform

Stéphane Liva; Philippe Hupé; Pierre Neuvial; Isabel Brito; Eric Viara; Philippe La Rosa; Emmanuel Barillot

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 visualization 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 normalization 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 .


BMC Systems Biology | 2013

NaviCell: a web-based environment for navigation, curation and maintenance of large molecular interaction maps

Inna Kuperstein; David P. A. Cohen; Stuart Pook; Eric Viara; Laurence Calzone; Emmanuel Barillot; Andrei Zinovyev

BackgroundMolecular biology knowledge can be formalized and systematically represented in a computer-readable form as a comprehensive map of molecular interactions. There exist an increasing number of maps of molecular interactions containing detailed and step-wise description of various cell mechanisms. It is difficult to explore these large maps, to organize discussion of their content and to maintain them. Several efforts were recently made to combine these capabilities together in one environment, and NaviCell is one of them.ResultsNaviCell is a web-based environment for exploiting large maps of molecular interactions, created in CellDesigner, allowing their easy exploration, curation and maintenance. It is characterized by a combination of three essential features: (1) efficient map browsing based on Google Maps; (2) semantic zooming for viewing different levels of details or of abstraction of the map and (3) integrated web-based blog for collecting community feedback. NaviCell can be easily used by experts in the field of molecular biology for studying molecular entities of interest in the context of signaling pathways and crosstalk between pathways within a global signaling network. NaviCell allows both exploration of detailed molecular mechanisms represented on the map and a more abstract view of the map up to a top-level modular representation. NaviCell greatly facilitates curation, maintenance and updating the comprehensive maps of molecular interactions in an interactive and user-friendly fashion due to an imbedded blogging system.ConclusionsNaviCell provides user-friendly exploration of large-scale maps of molecular interactions, thanks to Google Maps and WordPress interfaces, with which many users are already familiar. Semantic zooming which is used for navigating geographical maps is adopted for molecular maps in NaviCell, making any level of visualization readable. In addition, NaviCell provides a framework for community-based curation of maps.


BMC Systems Biology | 2012

Continuous time Boolean modeling for biological signaling: application of Gillespie algorithm.

Gautier Stoll; Eric Viara; Emmanuel Barillot; Laurence Calzone

Mathematical modeling is used as a Systems Biology tool to answer biological questions, and more precisely, to validate a network that describes biological observations and predict the effect of perturbations. This article presents an algorithm for modeling biological networks in a discrete framework with continuous time.BackgroundThere exist two major types of mathematical modeling approaches: (1) quantitative modeling, representing various chemical species concentrations by real numbers, mainly based on differential equations and chemical kinetics formalism; (2) and qualitative modeling, representing chemical species concentrations or activities by a finite set of discrete values. Both approaches answer particular (and often different) biological questions. Qualitative modeling approach permits a simple and less detailed description of the biological systems, efficiently describes stable state identification but remains inconvenient in describing the transient kinetics leading to these states. In this context, time is represented by discrete steps. Quantitative modeling, on the other hand, can describe more accurately the dynamical behavior of biological processes as it follows the evolution of concentration or activities of chemical species as a function of time, but requires an important amount of information on the parameters difficult to find in the literature.ResultsHere, we propose a modeling framework based on a qualitative approach that is intrinsically continuous in time. The algorithm presented in this article fills the gap between qualitative and quantitative modeling. It is based on continuous time Markov process applied on a Boolean state space. In order to describe the temporal evolution of the biological process we wish to model, we explicitly specify the transition rates for each node. For that purpose, we built a language that can be seen as a generalization of Boolean equations. Mathematically, this approach can be translated in a set of ordinary differential equations on probability distributions. We developed a C++ software, MaBoSS, that is able to simulate such a system by applying Kinetic Monte-Carlo (or Gillespie algorithm) on the Boolean state space. This software, parallelized and optimized, computes the temporal evolution of probability distributions and estimates stationary distributions.ConclusionsApplications of the Boolean Kinetic Monte-Carlo are demonstrated for three qualitative models: a toy model, a published model of p53/Mdm2 interaction and a published model of the mammalian cell cycle. Our approach allows to describe kinetic phenomena which were difficult to handle in the original models. In particular, transient effects are represented by time dependent probability distributions, interpretable in terms of cell populations.


Nucleic Acids Research | 2015

NaviCell Web Service for network-based data visualization

Eric Bonnet; Eric Viara; Inna Kuperstein; Laurence Calzone; David P. A. Cohen; Emmanuel Barillot; Andrei Zinovyev

Data visualization is an essential element of biological research, required for obtaining insights and formulating new hypotheses on mechanisms of health and disease. NaviCell Web Service is a tool for network-based visualization of ‘omics’ data which implements several data visual representation methods and utilities for combining them together. NaviCell Web Service uses Google Maps and semantic zooming to browse large biological network maps, represented in various formats, together with different types of the molecular data mapped on top of them. For achieving this, the tool provides standard heatmaps, barplots and glyphs as well as the novel map staining technique for grasping large-scale trends in numerical values (such as whole transcriptome) projected onto a pathway map. The web service provides a server mode, which allows automating visualization tasks and retrieving data from maps via RESTful (standard HTTP) calls. Bindings to different programming languages are provided (Python and R). We illustrate the purpose of the tool with several case studies using pathway maps created by different research groups, in which data visualization provides new insights into molecular mechanisms involved in systemic diseases such as cancer and neurodegenerative diseases.


Bioinformatics | 2017

MaBoSS 2.0: an environment for stochastic Boolean modeling

Gautier Stoll; Barthélémy Caron; Eric Viara; Aurélien Dugourd; Andrei Zinovyev; Aurélien Naldi; Guido Kroemer; Emmanuel Barillot; Laurence Calzone

Motivation: Modeling of signaling pathways is an important step towards the understanding and the treatment of diseases such as cancers, HIV or auto‐immune diseases. MaBoSS is a software that allows to simulate populations of cells and to model stochastically the intracellular mechanisms that are deregulated in diseases. MaBoSS provides an output of a Boolean model in the form of time‐dependent probabilities, for all biological entities (genes, proteins, phenotypes, etc.) of the model. Results: We present a new version of MaBoSS (2.0), including an updated version of the core software and an environment. With this environment, the needs for modeling signaling pathways are facilitated, including model construction, visualization, simulations of mutations, drug treatments and sensitivity analyses. It offers a framework for automated production of theoretical predictions. Availability and Implementation: MaBoSS software can be found at https://maboss.curie.fr, including tutorials on existing models and examples of models. Contact: gautier.stoll@upmc.fr or laurence.calzone@curie.fr Supplementary information: Supplementary data are available at Bioinformatics online.


Nucleic Acids Research | 1998

HuGeMap: A Distributed and Integrated Human Genome Map Database

Emmanuel Barillot; Frédéric Guyon; Christophe Cussat-Blanc; Eric Viara; Guy Vaysseix

The HuGeMap database stores the major genetic and physical maps of the human genome. It is also interconnected with the gene radiation hybrid mapping database RHdb. HuGeMap is accessible through a Web server for interactive browsing at URL http://www.infobiogen. fr/services/Hugemap , as well as through a CORBA server for effective programming. HuGeMap is intended as an attempt to build open, interconnected databases, that is databases that distribute their objects worldwide in compliance with a recognized standard of distribution. Maps can be displayed and compared with a java applet (http://babbage.infobiogen.fr:15000/Mappet/Show. html ) that queries the HuGeMap ORB server as well as the RHdb ORB server at the EBI.

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Pierre Neuvial

Centre national de la recherche scientifique

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