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Julie Aubert

Research Engineer INRAE

UMR518 Université Paris-Saclay, AgroParisTech, INRAE of Applied Mathematics and Computer Sciences

Statistical mOdelling and Learning for environnemenT and lIfe Sciences Team
22 place de l'agronomie
91 120 Palaiseau

Lien vers mon site hébergé sur github

  • Co-leader of the StatOmique Group
  • Member of the CATI Bioinformatics for Omics and metaOmics of Microbes

Research interests

My research and teaching activities concern the development and application of statistical and computational methods to address problems in genomic research.

Statistical Methods in Microbial Ecology
  • Design and analysis of amplicon-based metagenomic data
  • Methods for analyzing (complex) interactions between (plants), microbes and their environment

Applications include bioremediation, biocontrol of plants diseases, microbial ecology of foods

Statistical Methods in Molecular Biology
  • Design and analysis of high-throughput gene expression experiments based on next-generation sequencing: mRNA-Seq for transcriptome analysis
  • Comparative Metagenomics based on next-generation sequencing data
Statistical computing - R Packages
  • CHMM: An exact and a variational inference for Coupled Hidden Markov Models applied to the joint detection of copy number variations
  • cobiclust: Biclustering via Latent Block Model Adapted to Overdispersed Count Data
  • shinySbm: 'shiny' Application to Use the Stochastic Block Model
  • scimo: Extra Recipes Steps for Dealing with Omics Data
  • PLNmodels: Poisson Lognormal Models

    OLD packages

  • anapuce: Tools for microarray data analysis
  • MixThres: mixture model of truncated gaussians to detect a hybridization threshold for microarray data


Formation analyse statistique du transcriptome proposée par la plateforme de bioinformatique MIGALE

State Of the R

Groupe de travail et réseau d'animation autour des dernières innovations de R et autres outils utiles à l'implémentation et la diffusion de méthodes statistiques