MetExploreViz: web component for interactive metabolic network visualization

Summary: MetExploreViz is an open source web component that can be easily embedded in any web site. It provides features dedicated to the visualization of metabolic networks and pathways and thus offers a flexible solution to analyse omics data in a biochemical context. Availability and implementation: Documentation and link to GIT code repository (GPL 3.0 license) are available at this URL: http://metexplore.toulouse.inra.fr/metexploreViz/doc

Un algorithme stable de décomposition pour l'analyse des réseaux sociaux dynamiques

National audienceDynamical networks raise new analysis problems. An efficient analysis tool has to not only allow to split the network into groups of \og similar~\fg{} elements but also allow to detect changes in the network structure. In this article, we describe a new method for analyzing such dynamical networks. This technique is based on an algorithm of decomposition of graph into overlapping clusters. Time complexity of this algorithm is $O(|E| \cdot deg_{max}^2 + |V| \cdot log(|V|)))$. The stability of that algorithm allows to detect the changes of the studied network over the time

A stable decomposition algorithm for dynamical social network analysis

Dynamic networks raise new knowledge discovery challenges. To handle efficiently this kind of data, an analysis method has to both decompose the network (modelled by a graph) into similar set of nodes and let the user detect structural changes in the graph. In this article we present a graph decomposition algorithm generating overlapping clusters. The complexity of this algorithmis O(|E|·deg_max^2+|V| · log(|V|))). This algorithm is particularly efficient due to its ability to detect major modifications along dynamic processes such as time related ones

Metabolic network visualization eliminating node redundance and preserving metabolic pathways

<p>Abstract</p> <p>Background</p> <p>The tools that are available to draw and to manipulate the representations of metabolism are usually restricted to metabolic pathways. This limitation becomes problematic when studying processes that span several pathways. The various attempts that have been made to draw genome-scale metabolic networks are confronted with two shortcomings: 1- they do not use contextual information which leads to dense, hard to interpret drawings, 2- they impose to fit to very constrained standards, which implies, in particular, duplicating nodes making topological analysis considerably more difficult.</p> <p>Results</p> <p>We propose a method, called MetaViz, which enables to draw a genome-scale metabolic network and that also takes into account its structuration into pathways. This method consists in two steps: a clustering step which addresses the pathway overlapping problem and a drawing step which consists in drawing the clustered graph and each cluster.</p> <p>Conclusion</p> <p>The method we propose is original and addresses new drawing issues arising from the no-duplication constraint. We do not propose a single drawing but rather several alternative ways of presenting metabolism depending on the pathway on which one wishes to focus. We believe that this provides a valuable tool to explore the pathway structure of metabolism.</p

ProbMetab: an R package for Bayesian probabilistic annotation of LC-MS based metabolomics

We present ProbMetab, an R package which promotes substantial improvement in automatic probabilistic LC-MS based metabolome annotation. The inference engine core is based on a Bayesian model implemented to: (i) allow diverse source of experimental data and metadata to be systematically incorporated into the model with alternative ways to calculate the likelihood function and; (ii) allow sensitive selection of biologically meaningful biochemical reactions databases as Dirichlet-categorical prior distribution. Additionally, to ensure result interpretation by system biologists, we display the annotation in a network where observed mass peaks are connected if their candidate metabolites are substrate/product of known biochemical reactions. This graph can be overlaid with other graph-based analysis, such as partial correlation networks, in a visualization scheme exported to Cytoscape, with web and stand alone versions. ProbMetab was implemented in a modular fashion to fit together with established upstream (xcms, CAMERA, AStream, mzMatch.R, etc) and downstream R package tools (GeneNet, RCytoscape, DiffCorr, etc). ProbMetab, along with extensive documentation and case studies, is freely available under GNU license at: http://labpib.fmrp.usp.br/methods/probmetab/.Comment: Manuscript to be submitted very soon. 7 pages, 3 color figures. There is a companion material, the two case studies, which are going to be posted here together with the main text in next updated versio

A computational solution to automatically map metabolite libraries in the context of genome scale metabolic networks

This article describes a generic programmatic method for mapping chemical compound libraries on organism-specific metabolic networks from various databases (KEGG, BioCyc) and flat file formats (SBML and Matlab files). We show how this pipeline was successfully applied to decipher the coverage of chemical libraries set up by two metabolomics facilities MetaboHub (French National infrastructure for metabolomics and fluxomics) and Glasgow Polyomics (GP) on the metabolic networks available in the MetExplore web server. The present generic protocol is designed to formalize and reduce the volume of information transfer between the library and the network database. Matching of metabolites between libraries and metabolic networks is based on InChIs or InChIKeys and therefore requires that these identifiers are specified in both libraries and networks. In addition to providing covering statistics, this pipeline also allows the visualization of mapping results in the context of metabolic networks. In order to achieve this goal, we tackled issues on programmatic interaction between two servers, improvement of metabolite annotation in metabolic networks and automatic loading of a mapping in genome scale metabolic network analysis tool MetExplore. It is important to note that this mapping can also be performed on a single or a selection of organisms of interest and is thus not limited to large facilities

IL-6 supports the generation of human long-lived plasma cells in combination with either APRIL or stromal cell-soluble factors.

International audienceThe recent understanding of plasma cell (PC) biology has been obtained mainly from murine models. The current concept is that plasmablasts home to the BM and further differentiate into long-lived PCs (LLPCs). These LLPCs survive for months in contact with a complex niche comprising stromal cells (SCs) and hematopoietic cells, both producing recruitment and survival factors. Using a multi-step culture system, we show here the possibility to differentiate human memory B cells into LLPCs surviving for at least 4 months in vitro and producing immunoglobulins continuously. A remarkable feature is that IL-6 is mandatory to generate LLPCs in vitro together with either APRIL or soluble factors produced by SCs, unrelated to APRIL/BAFF, SDF-1, or IGF-1. These LLPCs are out of the cell cycle, express highly PC transcription factors and surface markers. This model shows a remarkable robustness of human LLPCs, which can survive and produce highly immunoglobulins for months in vitro without the contact with niche cells, providing the presence of a minimal cocktail of growth factors and nutrients. This model should be useful to understand further normal PC biology and its deregulation in premalignant or malignant PC disorders