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 : Multidimensional data analysis

Related people (6)

Amine GHOZLANE

Group : SINGLE - Hub Core

After a PhD in informatics on graph analysis (metabolic networks and sRNA-mRNA interaction graphs) at the LaBRI (Université de Bordeaux), I joined the DSIMB team (INTS) for a post-doc on structural modeling. Then, I performed a second post-doc at Metagenopolis – INRA Jouy-en-Josas, where I was initiated to the analysis of metagenomic data. I was recruited at the HUB in 2015, and since I pursue the development of methods dedicated to the treatment of metagenomic data by combining either the treatment of sequencing data, the statistics, the protein structural modeling and the graph analysis.


Keywords
AlgorithmicsClusteringGenome assemblyGenomicsMetabolomicsModelingNon coding RNASequence analysisStructural bioinformaticsTargeted metagenomicsDatabaseGenome analysisBiostatisticsProgram developmentScientific computingDatabases and ontologiesExploratory data analysisData and text miningIllumina HiSeqComparative metagenomicsRead mappingIllumina MiSeqSequence homology analysisGene predictionMultidimensional data analysisSequencingShotgun metagenomics
Organisms

Projects (28)

Quentin GIAI

Group : - Hub Core


Keywords

Organisms

Projects (0)

    Hanna JULIENNE

    Group : DETACHED - Hub Core

    I am seeking to apply my knowledge in computer science and statistics to understand real world data. I have interdisciplinary background spanning complex systems, Big Data, machine learning, biostatistics and genomics. I have completed a PhD in which I applied clustering and PCA to epigenomics data and discovered new insights on the coupling between replication and epigenetics. I worked at Dataiku, a dynamic start up in which I was actively engaged to help their clients to build their Big Data strategy and draw value from their data. I studied the human microbiota during two years at MetaGenoPolis (MGP), an innovative research center. We aim at improving human health by developing strategies (eg. nutritional, therapeutical, preventive…) to restore dysbiosed microbiota with our industrial and academical partners. I currently work in the statistical genetics group at the Pasteur Institut where I apply my software development and data science skills to quantify the impact of the human genome variation on diverse health parameters.


    Keywords
    ClusteringData managementGenomicsGenome analysisExploratory data analysisGeneticsComparative metagenomicsDimensional reductionMultidimensional data analysis
    Organisms

    Projects (2)

    Christophe MALABAT

    Group : HEAD - Hub Core

    After a PhD in biochemistry of the rapeseed proteins, during which I developed my first automated scripts for handling data processing and analysis, I join Danone research facility center for developing multivariate models for the prediction of milk protein composition using infrared spectrometry.
    As I was already developing my own informatics tools, I decided to join the course of informatic for biology of the Institut Pasteur in 2007. At the end of the course I was recruited by the Institute and integrate the unit of “génétique des interactions macromoléculaires” of Alain Jacquier. Within this group, I learn to handle sequencing data and I developed processing and analysis tools using python and R. I also create a genome browser and database system for storing, retrieving and visualizing microarray data. After 8 years within the Alain Jacquier’s lab, I join the Hub of bioinformatics and biostatistics as co-head of the team.


    Keywords
    ClusteringData managementSequence analysisTranscriptomicsWeb developmentDatabaseGenome analysisProgram developmentScientific computingExploratory data analysisData and text miningIllumina HiSeqRead mappingLIMSIllumina MiSeqHigh Throughput ScreeningMultidimensional data analysisWorkflow and pipeline developmentRibosome profilingMotifs and patterns detection
    Organisms

    Projects (10)

    Emeline PERTHAME

    Group : Stats - Hub Core

    Since February 2017 Research engineer, Hub of Bioinformatics and Biostatistics of the C3BI, Institut Pasteur 2015-2017 Post doctoral position, team MISTIS, INRIA Grenoble Topic: Robust clustering and robust non linear regression in high dimension. Collaboration with Florence Forbes (INRIA). 2012-2015 PhD thesis in Statistics, Applied Mathematics Department of Agrocampus-Ouest, IRMAR UMR 6625 CNRS, Rennes Topic: Stability of variable selection in regression and classification issues for correlated data in high dimension. Supervisor: David Causeur (Agrocampus-Ouest, IRMAR). Education 2015 PhD thesis in Statistics, Applied Mathematics Department of Agrocampus-Ouest, IRMAR UMR 6625 CNRS, Rennes 2012 ISUP degree (Institut de Statistique de l’UPMC), Université Pierre et Marie Curie, Paris 2012 Master 2 of Statistics, Université Pierre et Marie Curie, Paris


    Keywords
    ClusteringModelingStatistical inferenceTranscriptomicsBiostatisticsExploratory data analysisDimensional reductionStatistical experiment designMultidimensional data analysis
    Organisms

    Projects (22)

    Related projects (2)

    3D PATH

    Complex chronic diseases are caused by the accumulation of genetic, microbial and lifestyle factors. The number and complexity of such factors makes prediction of pathogenesis and therapy particularly difficult. Although a single factor is rarely sufficient to trigger pathology, genetic and environmental factors have so far been studied in isolation. Nevertheless, a substantial number of genetic variants have been associated with disease risk and the concomitant lifestyle shift and excessive hygiene are thought to contribute to the increased incidence in inflammatory diseases in industrialized countries. Moreover, clinical and experimental observations suggest a strong impact of gut microbiota on susceptibility to inflammatory diseases. The aim of 3D PATH is to explore the multiplicity and complexity of genetic, microbial and lifestyle factors associated with vulnerability to inflammatory pathology, using mice of the Collaborative Cross (CC), that model human genetic variability. Quantitative trait loci analyses, as well as integrative data analyses on metabolic and inflammatory outcomes will reveal new genetic variants and combinations of variants associated with disease susceptibility, and whether alterations of the gut microbiota in genetically susceptible mouse strains can trigger the phenotype. Moreover, the longitudinal design of experiments will allow us to identify early biomarkers that predict the pathology later in life. In a second step, validation of the identified risk factor combinations and exploration of the underlying molecular mechanisms will be performed taking advantage of the mouse model.



    Project status : Closed

    Coaching in R

    Background : The Immunoregulation Unit is composed at present of: Lab Head, one senior staff scientists, two “ingegnieurs” (one IP, one Fondation APHP), two PhD students. The focus of the research is the study of immuno-mechanisms in the pathogenesis of chronic inflammatory diseases, and of the molecular basis of response to treatment. All members of the lab have expressed an interest in acquiring basic R skills for the analysis of large gene expression data sets, collected from the study of patients’ samples. Only one PhD student in the lab is currently using R tools for data analysis, the other members have all received some previous instructions in biostatistics and/or R, but have not been using R tools currently. Requirements : Lab members have expressed a specific need to be instructed in the following areas: 1.refresh basic notions of R language, with a particular focus on handling large gene expression data sets 2.introduction to graphic tools for data representation (ggplot) 3.introduction to tools for differential gene expression analysis (limma) 4.data exploration using Principal Component Analysis Constraints : 1. For the course, it has been decided to use data generated by the lab. Placing the course in the lab’s data context has the advantage of ensuring that the course content is adapted to the “real-life” situations that lab members face in the analysis of their own data. 2.The present situation of “confinement” due to the Covid19 pandemic offers some opportunities (time availability of all members to follow the instruction), but obvious restriction. To maintain interactivity, classes are held at a distance, using Skype group meetings.



    Project status : In Progress