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
Searched keyword : Statistical inference
Related people (8)
I obtained an engineering degree in Biomedical engineering from Université de Technologie de Compiègne (UTC) in 1989, a master degree in Control of Complex Systems from UTC in 1990, a PhD in Control of Complex Systems from UTC in 1993, a University Degree in Human Genetics from The University of Rennes 1 in 2001 and a master degree in Functional Genomics from University Paris Diderot (Paris 7) in 2002. I worked as a statistician at the Transcriptome and Epigenome Platform from 2002 to 2017, where I was responsible for the statistical analyses of the data and had an important training activity (on the campus and outside). Since 2015 I have been co-head of the Bioinformatics and Biostatistics Hub within the Center of Bioinformatics, Biostatistics and Integrative Biology (C3BI). I am co-director of the Pasteur course Introduction to Data Analysis and co-organiser of the sincellTE summer school (a school dedicated to single cell transcriptome and epigenome data analysis). I am also co-managing the StatOmique group which gathers more than 60 statisticians from France.
RNA-seqStatistical inferenceTranscriptomicsBiostatisticsApplication of mathematics in sciencesExploratory data analysisIllumina HiSeqStatistical experiment designSequencing
- Biomarqueurs d’identification précoce du sepsis aux urgences (BIPS)(Jean-Marc CAVAILLON - Cytokines and Inflammation) - Closed
- Study of the early pathogenesis during Lassa fever in cynomolgus monkeys and its correlation with the outcome(Sylvain BAIZE - Biology of Viral Emerging Infections) - In Progress
- Host microbiota modification by the pathogen Listeria monocytogenes(Javier PIZARRO-CERDA - Bacteria-Cell Interactions) - Closed
Data VisualizationMachine learningStatistical inferenceBiostatisticsApplication of mathematics in sciencesDimensional reductionMultidimensional data analysis
- High content screening of mitochondrial morphology defects in mitochondrial genetic diseases: discovery of new therapeutic compounds(Claire PUJOL - Mitochondrial Biology) - In Progress
- Red Cross health workers and the continuity of care in the COVID-19 pandemic: A mixed methods operational approach(Leonard HEYERDAHL - Group: Medical anthropology and environment) - In Progress
- A genome-wide RNAi screening for mitochondrial fission factors(Erwan RIVIÈRE - Mitochondrial Biology) - Closed
Data managementMachine learningStatistical inferenceScientific computingExploratory data analysisSofware development and engineeringParallel computingNeuroimaging and computational neuroscienceGrid and cloud computing
Image analysisStatistical inferenceBiostatisticsGeneticsSequencingDiagnostic tools
- Research of homopolymers in the integron integrase genes(Céline LOOT - Bacterial Genome Plasticity) - Awaiting Publication
- Effect of Chlamydia trachomatis infection on histone methylation and consequences(Agathe SUBTIL - Cellular Biology of Microbial Infection) - In Progress
- Modification and reliance of Chlamydia trachomatis on host cell metabolism(Agathe SUBTIL - Cellular Biology of Microbial Infection) - Closed
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
ClusteringModelingStatistical inferenceTranscriptomicsBiostatisticsExploratory data analysisDimensional reductionStatistical experiment designMultidimensional data analysis
- Asymmetric heart morphogenesis(BERNHEIM SÉGOLÈNE - Heart Morphogenesis) - Closed
- Modulation of Flu transmission in Niger , according to climate variations over the past ten years(Ronan JAMBOU - Other) - Awaiting Publication
- Left-right patterning of heart precursors(Tobias BØNNELYKKE - Heart Morphogenesis) - In Progress
Hugo Varet is a biostatistician engineer from the Ensai (Ecole Nationale de la Statistique et de l’Analyse de l’Information) and has been recruited in 2013 by the Transcriptome & Epigenome Platform of the Biomics Pole. Late 2014 he obtained a permanent position at the Bioinformatics & Biostatistics Hub and has been detached to the platform to continue the statistical analyses of RNA-Seq data and develop R pipelines and Shiny applications that help in this task. One of them is named SARTools and is available on GitHub: https://github.com/PF2-pasteur-fr/SARTools. In December 2019 he left the Biomics Platform and joined the Bioinformatics & Biostatistics Hub as a core-member.
MetabolomicsModelingSequence analysisStatistical inferenceTranscriptomicsBiostatisticsScientific computingApplication of mathematics in sciencesExploratory data analysisHigh Throughput ScreeningClinical research
- Evaluation in cellulo of the impact of insecticide usage on arbovirus population(VALLET THOMAS - Viral Populations and Pathogenesis) - Closed
- Analysis of the molecular pathways induced by the activation of the Nod2 receptor by MDP in hypothalamic neurons(Ilana GABANYI - Perception and Memory) - Pending
- Identification of factors influencing the activity of bacteriophage within the gut of mammals(Devon CONTI - Other) - In Progress
After a diploma of statistician engineer from the Ensai (Ecole Nationale de la Statistique et de l’Analyse de l’Information) and a Ph.D in applied mathematics in the Statistics & Genome lab (AgroParisTech), I worked as a developer for the XLSTAT software. I have implemented some statistical methods such as mixture models, log-linear regression, mood test, bayesian hierarchical modeling CBC/HB, … Then I worked as a head teacher in statistics for one year. I was recruited in the Bioinformatic and biostatistic hub of the C3BI (Center of Bioinformatics, Biostatistics and Integrative Biology) in 2014, I am in charge of the statistical analysis and the development of R/R shiny pipelines.
Machine learningStatistical inferenceTargeted metagenomicsBiostatisticsApplication of mathematics in sciencesStatistical experiment design
- Etude de la réponse immunitaire néonatale dans la coqueluche maligne : approche transcriptomique(Soraya MATCZAK - Biodiversity and Epidemiology of Bacterial Pathogens) - Pending
- FLAVIMMUNITY(Giovanna BARBA SPAETH - Structural Virology) - Pending
- Etude de l’évolution des Troubles Olfactifs chez les patients ayant une perte de l’odorat persistante des suites de la COVID-19(Erwan POIVET - Department of Neuroscience) - Pending
Related projects (9)
Invasion of epithelial cells by the obligate intracellular bacterium Chlamydia trachomatis results in its enclosure inside a membrane-bound compartment termed an inclusion. The bacterium quickly begins manipulating interactions between host intracellular trafficking and the inclusion interface, diverging from the endocytic pathway and escaping lysosomal fusion. We have isolated a mutant strain that shows several developmental defects. The C3BI will contribute to the statistical analysis of the data.
Notre but est de trouver un test statistique capable de dire si deux matrices de nombres sont différents. Ces matrices correspondent à l’utilisation de fragments de gènes (V ou J) pour créer un gène ré-arrangé (V-J) codant pour un anticorps de spécificité particulière. Ici nous avons représenté pour chaque gène V la famille de gène J utilisé (parmi J1, J2, J3, J4). Le nombre correspondant au nombre d’occurrences que nous avons trouvé après séquençage du répertoire d’anticorps spécifiques à partir de souris immunisées contre l’un de ces antigènes. Les matrices ont été réalisées pour les deux chaines codant la spécificité d’un anticorps: la chaine lourde (VH) ou la chaine légère (VL). Le but est de comparer les deux matrices VH l’une avec l’autre, et les deux matrices VL l’une avec l’autre.
The present work is to systematically investigate the role of TLRs and NODs in the host defense during C. trachomatis infection using KO animals. The inflammatory cytokines and bacterial burden will be measured using Bio-Plex ELISA kit. C3BI will provide help in the data analysis.
Non human primates are an important reservoir for zoonotic disease. Here we analyze in Cameroon how human activities in the forest influence contact with non human primates to better understand processes of emergence.
Quantitatively understanding the stochastic dynamics of gene expression requires measurements at the level of single cells. A common approach to follow the expression of genes in single cells and in real time is to make use of fluorescent reporter proteins and to record the cells' fluorescence by microscopy. However, this provides only an indirect readout of the biological processes that are of interest such as the regulation mechanisms at the promoter. A possible way to uncover the unobservable biological processes is to infer the hidden dynamics from the available data through the use of mechanistic models of gene expression. The goal of this project is to develop methods for state estimation and parameter inference for such models and to test these methods on real data.
Réalisation d'un interactome entre des protéines de mammifères et une protéine bactérienne de virulence.
Mood disorders such as bipolar and major depressive illnesses are among the most severe psychiatric disorders. They have high prevalence and chronic course, and are associated with significant mental and somatic comorbidities and high personal and societal costs (lost productivity and increased medical expenses). Patients with bipolar disorder (BD), for example, exhibit a reduced lifespan compared with the general population, a finding that cannot only be explained by high suicide risk, reduced access to medical care and lifestyle factors. However, the pathophysiological mechanisms of BD are poorly understood, and patients often have incomplete treatment response. Advanced mathematical approaches such as machine learning techniques are increasingly being used to generate predictions based on complex data, and it has been successfully used to detect a number of clinical outcomes and to predict behaviours. In combination with mobile technologies (e.g. smartphones, wearables) to collect behavioural, physiological and environmental data, these big data predictive approaches may provide a much richer and deeper understanding of phenomenology and pathophysiological mechanisms of mood and bipolar disorders. By taking advantage of the high-standard bioinformatics expertise offered by the C3BI, this multidisciplinary, collaborative project aims to explore how clinical and biological factors, may contribute for better characterizing BD patients as well as to identify predictors of treatment response in BD. Our project also aims to explore how daily behavioural and physiological parameters may influence mood and behaviour in individuals at-risk or suffering from mood disorders.
Our goal is to have a bioinformatic tool that performs the 2 x 2 comparison of matrices of numbers in matrix batches. Each matrix corresponds to the use of gene fragments (V or J) to create a re-arranged gene (VJ) encoding a specific antibody, identified by droplet microfluidic or flow cytometry techniques followed by high throughput sequencing (NGS). ). The matrices are made for the two chains encoding the specificity of an antibody: the heavy chain (VH) or the light chain (VL). The goal is to compare VH matrices one against another, and VL matrices one against another for antibody gene rearrangements in mice, llama, and humans.
Because of the fast-growing number of assembled genomes available in the public repositories (GenBank/RefSeq), it is now quite common to end up with many genome assemblies that belong to the same species. In this current context, determining the type strain/species of any new species/genus requires to identify the representive one(s) within a large collection of genomes. The medoid of a genome collection being the one(s) whose sum of distances to all other genomes is minimal, it is an excellent candidate to be the representative genome of a collection. We therefore aim at procuring a bioinformatic tool able to quickly and accurately identify the medoid of any genome collections.