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 : Proteomics

Related people (5)

Christophe BÉCAVIN

Group : GORE - 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

    Group : - Hub Core


    Keywords

    Organisms

    Projects (0)

      Nicolas MAILLET

      Group : SINGLE - 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 : SysBio - 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 (32)

      Related projects (23)

      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 : Closed

      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 : Closed

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



      Project status : Closed

      The Flemmingsome: the proteome of intact cytokinetic midbodies

      The central part of the intercellular bridge connecting the two daughter cells during cytokinesis is a highly dense structure named the Midbody first described by Flemming in 1891. Work in the past ten years revealed that the midbody is a platform that concentrates essential proteins involved in cytokinetic abscission. After abscission, the midbody is cut on both sides, thus generating a midbody remnant (named MBR). The MBR usually interacts with the cell surface of one of the two daughter cells, before being engulfed in a phagocytic-like manner. We also found that the MBR can be easily released from cells before their engulfment by calcium chelation. Of note, MBRs at the cell surface might act as pro-proliferative, signalling entities but the proteins involved and the mechanisms of MBR anchoring are unknown. A previous proteomic study of the midbody conducted by Skop purified intercellular bridges from cell lysates recovered after cell synchronization, microtubule stabilization and detergent treatment. This pioneer proteomic study, although informative, did not allow the recovery of many key known proteins of the midbody. Here, we set up an experimental protocol to purify intact, detergent-free MBRs in order to have the full proteome of this organelle. Quantitative, label-free proteomics enabled us to identify 529 proteins enriched at least 2 times as compared to whole cell lysates, that we named the “Flemmingsome”. Besides known and well-established proteins of the midbody (MKLP1, MgcRacGAP, AuroraB, INCENP, MKLP2, Rab8, Rab11, Rab35, Citron Kinase, ESCRTs…), we identified new and promising candidates potentially involved in cytokinetic abscission. In addition, we identified 27 transmembrane proteins that are excellent candidates for mediating interactions between the MBR and the receiving daughter cells after cytokinetic abscission. We are also currently exploring whether newly identified candidates could participate in the signalling mediated by the MBRs. We would thus like to create a website that recapitulates the findings of our screen. The proteins discovered represent new candidates for the understanding of cytokinesis and tumorigenesis. This should be instrumental in the field as the previous websites are not updated (Microkits, Uniprot) and do not focus on this particular step of cytokinesis.



      Project status : Closed

      Secretome Analysis of OIS IL6KO SASP

      Cellular senescence is a stable cell cycle arrest that can be triggered by various biological stresses. Importantly, senescent cells remain metabolically active and secrete numerous molecules, such as cytokines, chemokines, proteases and growth factors. This secretome is called SASP (senescence associated secretory phenotype). Senescence plays a role in several processes, most notably in cancer, where it acts as an intrinsic tumor suppressor mechanism by inhibiting cell growth of premalignant cells. More recently, senescence has been shown to be involved in other biological responses, notably in tissue repair and aging. We recently showed that senescence upon tissue damage was promoting cellular reprogramming and cellular plasticity, notably via SASP. More precisely, we showed that IL-6 was an important mediator of SASP effect. Blocking IL-6 abolished beneficial effect of the SASP on reprogramming. Nonetheless, we speculate that other factors may be important for reprogramming. Indeed SASP factors have been previously shown to play redundant roles, notably in mediating senescence. Therefore we performed mass spectrometry analysis to identify other SASP factors in collaboration with proteomic platform of Institut Pasteur. We already identified promising candidates but we would also like to have a better global understanding of SASP complexity and which pathways it could activate in recipient cells. Thus, to investigate SASP effects more in details, we collaborate with C3BI platform of Institut Pasteur. Finally, understanding how SASP impact cellular plasticity in the context to tissue regeneration is essential for devising new strategies based on in vivo reprogramming.



      Project status : In Progress

      Global BioID-based SARS-CoV-2 proteins proximal interactome unveils novel ties between viral polypeptides and host factors involved in multiple COVID19-associated mechanisms

      The worldwide SARS-CoV-2 outbreak poses a serious challenge to human societies and economies. SARS-CoV-2 proteins orchestrate complex pathogenic mechanisms that underlie COVID-19 disease. Thus, understanding how viral polypeptides rewire host protein networks enables better-founded therapeutic research. In complement to existing proteomic studies, in this study we define the first proximal interaction network of SARS-CoV-2 proteins, at the whole proteome level in human cells. Applying a proximity-dependent biotinylation (BioID)-based approach greatly expanded the current knowledge by detecting interactions within poorly soluble compartments, transient, and/or of weak affinity in living cells. Our BioID study was complemented by a stringent filtering and uncovered 2,128 unique cellular targets (1,717 not previously associated with SARS-CoV-1 or 2 proteins) connected to the N- and C-ter BioID-tagged 28 SARS-CoV-2 proteins by a total of 5,415 (5,236 new) proximal interactions. In order to facilitate data exploitation, an innovative interactive 3D web interface was developed to allow customized analysis and exploration of the landscape of interactions (accessible at http://www.sars-cov-2-interactome.org/). Interestingly, 342 membrane proteins including interferon and interleukin pathways factors, were associated with specific viral proteins. We uncovered ORF7a and ORF7b protein proximal partners that could be related to anosmia and ageusia symptoms. Moreover, comparing proximal interactomes in basal and infection-mimicking conditions (poly(I:C) treatment) allowed us to detect novel links with major antiviral response pathway components, such as ORF9b with MAVS and ISG20; N with PKR and TARB2; NSP2 with RIG-I and STAT1; NSP16 with PARP9-DTX3L. Altogether, our study provides an unprecedented comprehensive resource for understanding how SARS-CoV-2 proteins orchestrate host proteome remodeling and innate immune response evasion, which can inform development of targeted therapeutic strategies.



      Project status : In Progress