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Genomic networks

Team GNet / Marie-Laure Martin-Magniette


Our team develops statistical models and bioinformatics approaches to improve functional and relational annotations of genes of the model plant Arabidopsis. Our long term goal is to transfer this knowledge from the model plant to plants of agronomic interest.

To achieve this goal, it requires to develop and maintain several skills, which can be divided into three parts:


Statistical methods for omics analysis

We develop original statistical methods to analyze high-throughput molecular biology data. Our goal is to cope with the intrinsic difficult of analyzing such data set, in particular their high dimensionality, while facilitating their biological interpretation. We are experts of model-based approach for clustering, linear models and algorithms for molecular biology. These methods allowed us to investigate genome activity: gene expression, gene interactions and control mechanisms. Main topics are about:

  • Neutral comparison studies
  • Development of mixture models for co-expression networks and protein-DNA interactions
  • Segmentation methods for longitudinal analysis of the genome.
  • Statistical inference of gene networks.


Data integration for functional annotation

The team is involved in several integrative projects merging biological resources, bioinformatics predictions and statistical analyses. Databases and graphical users interfaces are relevant approaches to organize and visualize data integration efforts.

For several years now, we develop and maintain two databases and   which help to improve insight in the biological roles of plant genes and give us an ideal medium to release the results of our projects to a broader community.

  •  is a database on the structural and functional annotations of 6 plant genomes
  •  is a database dedicated to data generated by the transcriptomic platform of IPS2.

The module of CATdb presents a large co-expression study of 18 stress categories. Functions were associated with computed clusters by integrating various resources (GO, sub-cellular localization of proteins, Hormone Families, Transcription Factor Families and a refined stress-related gene list associated to publications) and gene networks were displayed by exploiting protein–protein and transcription factors-targets interactions.

Besides, we are involved in several integrative projects merging biological resources, bioinformatics predictions and statistical analyses.


Investigate genes involved in plant stress responses

Our methods are general and could be applied to various projects. Nevertheless our interest is to work at the interface between statistics, bioinformatics and biology. Since 2010, we have focused on plant stress responses and are involved in several biological projects:

  • We coordinated a meta-analysis of transcriptome data in targeting the genes involved in the response to various stresses.
  • We were involved in projects coordinated by other teams. We are in charge of the statistical and bioinformatic analyses of large omics data sets.



Our team regularly welcomes visiting scientists to start a collaboration or strengthen ongoing ones. Our visitors can either come for a fixed period of time or on a regular basis once or twice a week if they live in the neighborhood of Paris-Saclay. If you are interested in visiting us, send us an email Marie-laure Martin Magniette (mlmartin @ agroparistech.fr)


SPOmics transcriptomic platform

The GNet team co-hosts the transcriptomic platform of IPS2 with the OGE team