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
prenom.nom@inrae.fr

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

Teaching

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

State Of the R