賴俊傑   |   Lai Joon Keat (JK) Agronomist & Data Analyst

About

Hi, I'm JK, short for Lai Joon-Keat. I am Malaysian and came to Taiwan to pursue my education. I hold a bachelor's degree with double majors in agriculture from the Department of Plant Industry (DPI) and life science from the Department of Biotechnology at the National Pingtung University of Science and Technology (NPUST). I subsequently earned my doctoral degree from the Department of Plant Industry in 2023.

As part of my Bachelor's program, I carried out a project titled "SSR analysis of hybrids derived from triploid guava PolyR". I received training in conducting SSR analysis, which involved tasks such as DNA extraction, PCR, and Southern blotting. My primary contribution to the project was identifying indications suggesting that the hybrids were deficient in the 5^th chromosome. After graduation, I was eligible to bypass the master's program and enter the PhD program directly.

During my PhD program, I transitioned my research focus from genotype to phenotype. During this period, my interest gravitated towards high-throughput phenotyping. My specialization now encompasses various areas, including data analysis, data modeling, and remote sensing techniques. This expertise involves a wide range of skills, such as high-dimensional data analysis, image analysis, programming, as well as hands-on experience in botany and plant physiology experiments.

During my postdoctoral research, I have been involved in studying ROS signaling pathways in the roots of Arabidopsis and rice. My primary responsibility is to identify candidate genes that regulate or are regulated by superoxide signaling. In the Arabidopsis project, my work is primarily computational. I provided insights into the downstream regulation of the RGF1 peptide and the upstream regulators of the PLT2 protein through transcriptome analysis. I also proposed a novel algorithm to quantify and visualize the redox status of roots by detecting the cytGRX-roGFP2 signal. In the rice project, my work combines both experimental and computational approaches. Due to the sensitivity and developmental diversity of rice roots, I optimized the entire growth procedure and experimental protocols to obtain more reliable and reproducible data. I addressed root curling and morphological variability by modifying seed propagation and seedling growth conditions. I also resolved issues with superoxide detection and data variability by optimizing the NBT staining protocol and developing an algorithm to systematically quantify the staining signal. For ease of use, I further packaged the algorithm into standalone software with a graphical user interface. The rice project is currently ongoing.

Portfolio

Education