ANR

This project aims to explore the potential of long noncoding RNAs (lncRNAs) to enhance rice resilience to climate change-induced stress. By using AI to analyze lncRNA patterns in rice and Arabidopsis, the goal is to identify key molecules involved in stress response and apply this knowledge to develop RNA-based solutions, like synthetic lncRNAs, to improve crop tolerance without genetic modification.

The multidisciplinary RESOLAT project aims to understand the impact of lncRNAs on the gene network regulating lateral root development in Brassicaceae. Using time-series RNA-seq data, we propose to reconstruct the gene regulatory network controlling lateral root development in eight Brassicaceae, integrating coding and non-coding genes (small and long).

The EvoRNAs project aims to leverage the evolution of RNA-RNA Binding Protein (RBP) interactions and machine learning to predict RNA recognition rules of two highly conserved key RBPs and their interacting lncRNAs and mRNAs, to assess the impact on the generation of splicing diversity across evolutionary distant species.

The first objective of this project is to characterize the roles of the CUC genes during Arabidopsis root development. The second objective is to establish the CUC gene regulatory network (GRN) and determine its possible modulation by the developmental context. The third objective is to determine to what extent the apparently diverse developmental roles of the CUC genes may in fact hide a unique still-to-identify fundamental role.