Research Direction

1. Genome-Driven Mining of Crop Germplasm Resources

  • Utilize graph-based pan-genome technologies to dissect genetic diversity and mine key alleles for stress resistance, high yield, and quality traits.
  • Representative achievements: High-quality pan-genomes for foxtail millet and tomato, overcoming limitations of traditional reference genomes.

2. Multi-Omics Integration and Functional Regulatory Element Identification

  • Combine epigenomics, transcriptomics, and 3D genomics data with machine learning to decode regulatory networks.
  • Develop AI models to predict non-coding regulatory elements (e.g., enhancers, promoters) and elucidate molecular mechanisms underlying complex traits.

3. Big Data-Driven Intelligent Breeding Design

  • Construct multi-dimensional databases integrating phenotype-genotype-environment data.
  • Develop genomic selection and whole-genome optimization algorithms to transition from empirical breeding to precision design breeding, accelerating cultivar development.