Mots-Clés

transcription factors, DNA binding, protein complexes, plants, crops, machine learning

Description

Two fully-Funded PhD Positions in Bioinformatics: Deciphering Transcription Factors' DNA Binding in Plants

About Us: Join our dynamic research team within the Flo_Re (Floral Regulators) group at the Laboratoire de Physiologie Cellulaire et Végétale (LPCV), located at CEA Grenoble, France. The Flo_Re team specializes in studying the developmental program leading to flower formation, with a specific focus on the function and evolutionary history of key regulators, predominantly transcription factors (TFs).

PhD Journey: Embark on a captivating PhD journey focused on unraveling the intricate world of gene regulation in plants. You will contribute to decode the DNA binding modes of plant transcription factors, discover new protein complexes interacting with DNA and unveil genetic variants influencing phenotypic traits in crops. Your work will advance our understanding of the regulatory code that shapes gene expression.

Research Approach: Our research approach involves leveraging available genomic data (ChIP-seq, DAP-seq, WGS, RNA-seq, MNase-seq, BS-seq) for plants and applying state-of-the-art machine learning techniques. A first PhD course consist in a comprehensive study of transcription factors encoded in Arabidopsis thaliana’s genome and modeling their DNA binding behaviors individually or in complex arrangements. The second phD course will explore how both trans and cis genetic variations impact TF-DNA binding, examining its correlation with gene expression and phenotype in cabbages. Applicants are encouraged to contribute to shaping their own research topics within this dynamic field, allowing for a degree of flexibility and personalization in their research pursuits.

Collaborative Opportunities: The successful candidate will have the chance to collaborate with machine learning experts both in Grenoble and Kyoto.

Candidate Profile:

• Proficiency in general bioinformatics and statistics for biology.
• Programming skills in bash, Python, or R.
• Prior experience with machine learning packages like caret or scikit-learn would be a valuable asset.
• A strong Master's degree in a relevant field (bioinformatics, computer science, or related subjects).
• Knowledge or a strong interest in plant science and gene expression regulation.
• Excellent proficiency in English (both oral and written).
• Strong writing skills.

Application Details: We will consider applications until end of May for a start in October 2024.

Bibliography ( * our team’s production):

1. Lai, X. et al. (2019) Building Transcription Factor Binding Site Models to Understand Gene Regulation in Plants. Mol. Plant 12, 743–763*
2. Blanc-Mathieu, R. et al. (2024) Plant-TFClass: a structural classification for plant transcription factors. Trends Plant Sci. 29, 40–51*
3. Lai, X. et al. (2021) The LEAFY floral regulator displays pioneer transcription factor properties. Mol. Plant 14, 829–837*
4. Rieu, P. et al. (2023) The F-box protein UFO controls flower development by redirecting the master transcription factor LEAFY to new cis-elements. Nat. Plants 9, 315–329*
5. Azpeitia, E. et al. (2021) Cauliflower fractal forms arise from perturbations of floral gene networks. Science 373, 192–197*
6. Li, X. et al. (2024) Large-scale gene expression alterations introduced by structural variation drive morphotype diversification in Brassica oleracea. Nat. Genet. DOI: 10.1038/s41588-024-01655-4