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What is our purpose?
The PCGL offers a dynamic and supportive research environment spanning the full breadth of plant genomics. Master's students gain foundational experience in transcriptomics, while Doctoral students specialize in either digital breeding or genomics. PCGL also provides numerous opportunities for researchers to expand their expertise through projects in epigenomics, pan-genomics, population genomics, comparative genomics, phenomics, AI applications in plant science, and plant breeding.
We are deeply committed to nurturing the individual research goals of every lab member, providing extensive support and resources to facilitate their success. Ultimately, PCGL aims to contribute to the development of innovative crop breeding technologies that can address climate change and food security challenges.
Jeon D, Sung YJ, Kim C. High-quality Chromosomal-Level Genome Assembly of the Wasabi (Eutrema japonicum) 'Magic'. Sci Data. 2024;11(1):1044.
Genome assembly and evolutionary study
PCGL studies plant genomics using high-throughput sequencing technologies, including NGS illumina, Oxford Nanopore and PacBio. Our research of interest is conducting genome assembly to construct high-quality reference genomes for various plant species, including crops and medicinal plants. By analyzing genomic structures, genetic variations, and conserved regions, we investigate plant genome organization and evolutionary patterns.
PCGL compares various plant genomes to identify genetic elements associated with species-specific traits and adaptation. Structural variations and gene conservation patterns are analyzed to understand evolutionary changes across species. NGS-based sequencing approaches allow for a detailed investigation of gene regulation mechanisms. Transcriptome analysis is performed to examine gene expression patterns under different environmental conditions, particularly in wheat and sorghum. By identifying differentially expressed genes, we explore plants’ responses to stress and environmental stimuli.
Lee S, Kim C. Chromosome-scale genome assembly of Korean goosegrass (Eleusine indica). Sci Data. 2025;12(1):156.
Choi S, Kang Y, Kim C. Chromosome-level genome assembly of Salvia sclarea. Sci Data. 2025;12(1):14.
Jeon D, Kim C. Polyploids of Brassicaceae: Genomic Insights and Assembly Strategies. Plants (Basel). 2024;13(15).
Digital breeding
PCGL studies digital breeding by integrating large-scale genomic data with AI-driven predictive models to enhance breeding efficiency. By utilizing genotypic data, breeding cycles can be shortened, and selection accuracy for desirable traits can be improved compared to conventional breeding methods. A core breeding population is established as the foundation for this approach, enabling efficient data generation and model development.
To optimize the selection process, PCGL applies statistical and AI-driven methods, including regression analysis, machine learning (RF, SVM, RKHS), and deep learning models such as MLP, CNN, and RNN. These models are iteratively refined to enhance prediction accuracy, allowing for rapid and data-driven selection of individuals with target traits. This approach improves breeding efficiency by reducing time and resource requirements while increasing precision in cultivar development.
Jeon D, Kang Y, Lim Y, Kim C. The First Application of Genomic Selection for Tree Morphological Traits in Pinus koraiensis Using Markers Identified through a Genome-wide Association Study. Forestry. 2025.
Kang Y, Choi C, Kim JY, Min KD, Kim C. Optimizing genomic selection of agricultural traits using K-wheat core collection. Front Plant Sci. 2023;14:1112297.
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