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

Search by keywords | Search by organisms

Searched keyword : Proteomics

Related people (5)

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)

Freddy CLIQUET


One of my projects consists in developing GRAVITY, a java tool based on Cytoscape to integrate genetic variants within protein-protein interaction networks to allow the visual and statistical interpretation of next-generation sequencing data, ultimately helping geneticists and clinicians to identify causal variants and better diagnose their patients. I’m also involved in several other projects in the lab, taking part in the design of pipelines for the processing and the analysis of genomics data, including SNP arrays, whole-exome and whole-genome sequencing data. This means being confronted to the big data problematic, the unit having to manage hundreds of terabytes of genomics data. Finally, I am now analysing these data in order to identify possible causes for autism, to help clinicians with their diagnosis but also to better understand the biological mechanisms at play in this complex disease. This is done through the project aiming at understanding the genetic architecture of autism in the Faroe Islands, and also with the newly starting IMI2 European project AIMS2-Trials.


Keywords
AlgorithmicsData managementData VisualizationGenomicsMachine learningProteomicsGenome analysisBiostatisticsProgram developmentScientific computingApplication of mathematics in sciencesExploratory data analysisSofware development and engineeringData and text miningGenetics
Organisms

Projects (0)

    Quentin GIAI-GIANETTO


    Since September 2016, I am a research engineer in the Bioinformatics and Biostatistics HUB of the Institut Pasteur and detached in the Proteomics facility. I have a PhD in Signal Processing from the Ecole Nationale Supérieure des Télécommunications de Bretagne (Telecom Bretagne) and a Master in Mathematics with a specialty in Statistical Engineering from Rennes 1 University. After my PhD, I was a research and teaching assistant in Mathematics at the Institut National des Sciences Appliquées (INSA) of Rennes, then I worked as a consultant for public local authorities in the company Ressources Consultants Finances. I started working in the field of Proteomics in October 2014 in the EDyP laboratory located in Grenoble (http://www.edyp.fr/). I have been working on the improvement of statistical analysis of bottom-up proteomics data. Today, most of the projects I work on consist of detecting changes in protein abundances using discovery-driven mass spectrometry. I am interested in the development of new methodologies to optimize proteomics data analysis pipelines, from the identification of peptides/proteins to their quantification and the interpretation of results. For this purpose, I worked on several R packages which can be downloaded from the CRAN and Bioconductor: cp4p (https://cran.r-project.org/web/packages/cp4p/index.html), imp4p (https://cran.r-project.org/web/packages/imp4p/index.html), DAPAR (http://bioconductor.org/packages/release/bioc/html/DAPAR.html) and its GUI ProStar.


    Keywords
    Machine learningModelingPathway AnalysisProteomicsStatistical inferenceBiostatisticsApplication of mathematics in sciencesData and text miningData integrationStatistical experiment designMultidimensional data analysis
    Organisms
    Non applicable
    Projects (1)

    Nicolas MAILLET

    Group : ALPS - Embedded : Structural Virology

    After a PhD in bioinformatics at Inria/IRISA, Université de Rennes 1, Rennes (France), under the supervision of Dominique Lavenier and Pierre Peterlongo, I did a postdoc in bioinformatics at Laboratory of Ecology and Evolution of Plankton in Stazione Zoologica Anton Dohrn of Naples, Italy. Both my thesis and my postdoc were about the Tara Oceans projet and the development of new software to analyze huge quantities of raw reads coming from metagenomics sample. I am currently occupying a research engineer position at the Hub as leader of ALPS group and focus on several different computing problems including metagenomics, protein assembly and several short term developments.


    Keywords
    AlgorithmicsData managementProteomicsDatabaseProgram developmentScientific computingSofware development and engineeringComparative metagenomics
    Organisms

    Projects (8)

    Natalia PIETROSEMOLI

    Group : FUNGEN - Hub Core

    Dr. Natalia Pietrosemoli is an Engineer with a M. Sc. in Modeling and Simulation of Complex Realities from the International Center for Theoretical Physics, ICTP and the International School of Advanced Studies, SISSA (Triest, Italy). During her M. Sc. internships she mostly worked in modeling, optimization, combinatorics and information theory applied to medical imaging. In 2012 she got a Ph. D in Computational Biology from the School of Bioengineering of Rice University (Houston, TX, US), where she specialized in computational structural biology and functional genomics. Her doctoral thesis “Protein functional features extracted with from primary sequences : a focus on disordered regions”, contributed to a better understanding of the functional and evolutionary role of intrinsic disorder in protein plasticity, complexity and adaptation to stress conditions. As part of her Ph. D., Natalia was a visiting scholar in two labs in Madrid: the Structural Computational Biology Group at the Spanish National Cancer Research Centre (CNIO), where she mainly worked in sequence analysis and the functional-structural relationships of proteins, and the Computational Systems Biology Group at the Spanish National Centre for Biotechnology (CNB-CSIC ), where she studied the functional implications of intrinsically disordered proteins at the genomic level for several organisms, collaborating with different experimental and theoretical groups. In 2013, she joined the Swiss Institute of Bioinformatics as a postdoctoral fellow in the Bioinformactics Core Facility. Her main project consisted in the molecular classification of a rare type of lymphoma, which involved the integration of transcriptomic, clinical and mutational data for the identification of molecular markers for classification, diagnosis and prognosis. This work was performed in collaboration with the Pathology Institute at the University Hospital of Lausanne (CHUV). In November of 2015 Natalia joined the Hub Team @ Pasteur C3BI as a Senior Bioinformatician. Natalia is especially interested in the integrative analysis of different omics data, both at large-scale and for small datasets, and loves collaborating in interdisciplinary environments and having feedback from her fellow experimental colleagues. Currently, she’s coordinating several projects performing functional and pathway analysis at the genomic level. By grouping genes, proteins and other biological molecules into the pathways they are involved in, the complexity of the analyses is significantly reduced, while the explanatory power increases with respect to having a list of differentially expressed genes or proteins.


    Keywords
    AlgorithmicsData managementGenomicsImage analysisMachine learningModelingProteomicsSequence analysisStructural bioinformaticsTranscriptomicsDatabaseGenome analysisBiostatisticsScientific computingDatabases and ontologiesApplication of mathematics in sciencesData and text miningGeneticsGraphics and Image ProcessingBiosensors and biomarkersClinical researchCell biology and developmental biologyInteractomicsBioimage analysis
    Organisms

    Projects (22)

    Related projects (19)

    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

    Modeling mitochondrial metabolism dormant Cryptococcus neoformans

    Cryptococcus neoformans is a ubiquitous yeast present in the environment that is able to interact closely with numerous organisms including amoeba, paramecium or nematodes. The interaction with these organisms shaped its virulence with acquisition of infectious properties as a consequence especially in mammals . The ability to survive nutrient starvation, oxidative stress, desiccation, both in the environment and during infection, indicates a high level of physiological and metabolic plasticity of the yeast. In humans, after primary infection during childhood, the yeast is able to survive within the host for years before reactivation upon immunosuppression, leading to a life threatening  disseminated fungal infection. This phenomenon, called dormancy / quiescence is one of the main biological features of this fungus in relation with disease's pathogenesis. It is well known in bacteria (tuberculosis), parasites (Plasmodium, Toxoplasma). In C. neoformans, dormancy has only been demonstrated epidemiologically in our laboratory but not experimentally so far. We developed an assay where yeasts cells exhibiting characteristics of potentially dormant cells were generated. Indeed, dormant cells are characterized by a low metabolic activity sometimes undetectable under normal laboratory conditions, altered growth capacity, and the ability to resuscitate upon adequate stimulus. Dormant cells are known to have increased mitochondrial masse and activity justifying a screening strategy of a collection of KO mutants for mitochondrial proteins. In parallel the whole proteome, transcriptome and secretome will be obtain with the ambition to correlate these parameters. Our current project aims at exploring the metabolism of the dormant yeast to have a comprehensive picture of the pathways that are required for the maintenance of dormancy and fo exit from dormancy.  



    Project status : In Progress

    Development of top-down proteomics for clinical microbiology

    Rapid and accurate identification of microorganisms is a prerequisite for appropriate patient care and infection control. In the last decade, Mass Spectrometry (MS) has revolutionized the field of clinical microbiology with the introduction of MALDI-TOF for rapid microbial identification. However, MALDI-TOF MS suffers from important limitations. Some bacteria remain difficult to identify, either because they do not give a specific profile or because the database lacks the appropriate reference. In addition, the discriminatory power of the technique is often insufficient for reliably differentiating sub-species within species or clones within sub-species. More importantly, virulence or resistance determinants cannot be characterized, which is a severe obstacle for appropriate patient care and antibiotics prescription in hospitals. In recent years, proteomics approaches have been increasingly used to study host-pathogen interactions. State-of-the-art bottom-up approaches rely on the enzymatic digestion of proteins and LC-MS/MS analysis of peptides. In contrast, top-down proteomics is an emerging technology based on the analysis of intact proteins by high-resolution mass spectrometry. The major advantage of top-down proteomics is its ability to address protein variations and characterize proteoforms arising from alternative splicing, allelic variation, or post-translational modification. We have recently set-up a robust top-down proteomics platform for the analysis of intact bacterial proteomes. Our final objective is to use this platform to better characterize bacterial pathogens in a clinical context, but a major requirement to achieve this goal is to build up accurate bacterial proteoform databases.  



    Project status : In Progress

    NOXO1 interacting partners in intestinal epithelial cells under inflammatory and infectious conditions



    Project status : Closed