Expertise

Hub members Have many expertise, covering most of the fields in bioinformatics and biostatistics. You'll find below a non-exhaustive list of these expertise

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Searched keyword : Listeria

Related people (1)

Christophe BÉCAVIN

Group : TEG - Hub Core

CV Senior Bioinformatician August 2015 – Present : Institut Pasteur, Paris PostDoc fellow 2011 – 2015 : Pascale Cossart’s laboratory, Unité des Interactions Bactéries-Cellules, Institut Pasteur, Paris Phd fellow 2007 – 2010 : Institut des Hautes Etudes Scientifiques, ann Ecole Normale Supérieure, Paris Magister of Science, Theoretical Physics 2003 – 2007 : Dynamical systems and statistics of complex matter, Université Paris 7 and Université Paris 6


Keywords
BiophysicsMachine learningModelingProteomicsBiostatisticsDatabases and ontologiesHost-pathogen interactions
Organisms
ListeriaLeishmania
Projects (12)

Related projects (11)

Listeriomics - Development of a web platform for visualization and analysis of Listeria omics data

Over the past three decades Listeria has become a model organism for host-pathogen interactions, leading to critical discoveries in a broad range of fields including virulence-factor regulation, cell biology, and bacterial pathophysiology. More recently, the number of Listeria “omics” data produced has increased exponentially, not only in term of number, but also in term of heterogeneity of data. There are now more than 40 published Listeria genomes, around 400 different transcriptomics data and 10 proteomics studies available. The capacity to analyze these data through a systems biology approach and generate tools for biologists to analyze these data themselves is a challenge for bioinformaticians. To tackle these challenges we are developing a web-based platform named Listeriomics which integrates different type of tools for “omics” data manipulation, the two most important being: 1) a genome viewer for displaying gene expression array, tiling array, and RNASeq data along with proteomics and genomics data. 2) An expression atlas, which is a query based tool which connects every genomics elements (genes, smallRNAs, antisenseRNAs) to the most relevant “omics” data. Our platform integrates already all genomics, and transcriptomics data ever published on Listeria and will thus allow biologists to analyze dynamically all these data, and bioinformaticians to have a central database for network analysis. Finally, it has been used already several times in our laboratory for different types of studies, including transcriptomics analysis in different biological conditions, and whole genome analysis of Listeria proteins N-termini. This project is funded by an ANR Investissement d'avenir: BACNET  10-BINF-02-01



Project status : Closed

Systems Biology of Cell Infection by the Bacterial Pathogen Listeria monocytogenes

In the context of the Swiss consortium InfectX (www.infectx.ch), Javier PIZARRO-CERDA previously performed siRNA, microRNA, drug screens and proteomic analyses to investigate signaling pathways modulating invasion of host cells by the bacterial pathogen Listeria monocytogenes. In a first consortium study, based on results from drug and siRNA screens targeting the human kinome, we identified major kinases which up- or down-regulate cell invasion by L. monocytogenes and by 7 additional bacterial and viral pathogens (Rämö et al. 2014). Subsequently, a siRNA genome-wide screen allowed us to revisit and redefine the role of cytoskeletal complexes required for L. monocytogenes cellular invasion and actin-based motility (Kühbacher et al. 2015). Applying a proteomic ‘surfaceome’ analysis, we also revealed that late endosomal compartments are recruited to L. monocytogenes infection foci to promote invasion (Kühbacher et al. Submitted). More recently, we have started the analysis of a microRNA screen which highlights novel gene clusters associated to regulation of phosphoinositide metabolism during L. monocytogenes cell entry (Kühbacher et al. Unpublished Results). These different projects have generated vast amounts of data that have been until now only independently analyzed. However, this information can now be exploited from a systems biology perspective to identify hidden connections between relevant signaling cascades and gene networks which may highlight novel cellular functions exploited by pathogens in the context of infection. The team of Benno SCHWIKOWSKI will perform two types of analysis on the data generated by Javier PIZARRO-CERDA. In both cases, p-values will be aggregated across gene sets using suitable statistical approaches. We will then

  • Pathway-based analysis. This type of analysis considers genes in sets that have been recognized to operate together to perform certain biological functions (e.g.,



Project status : In Progress