▲ Photo by Me. 📍Montgomery, Texas, US.
ShiNyP: AI-Assisted Platform for Rapid and Interactive Visual Exploration
Yen-Hsiang Huang, Ling-Yu Chen, Endang M Septiningsih, Pei-Hsiu Kao, Chung-Feng Kao*
ShiNyP offers an integrated, user-friendly platform for efficient and reproducible genome-wide SNP analysis, lowering the technical barrier for population genomics research.
- Why do we need this? Current SNP analysis pipelines are fragmented and require command-line skills, making them inaccessible to many researchers.
- How is it done? ShiNyP integrates all major SNP analysis modules into a single, user-friendly R/Shiny platform, covering the entire workflow with automated visualizations and AI-generated reports.
DOI: 10.1093/molbev/msaf117
ShiNyP: ShiNyP User Guide
Timeline: 2024-07 (Start) ➜ 2024-11 (Submit) ➜ 2024-05 (Publish) ➜ Now (Maintaining).
Core Collection Framework for Selection Footprints in Vegetable Soybeans
Yen-Hsiang Huang, Chung-Feng Kao
Our core collection-based pipeline unveils distinctive selection patterns between vegetable and grain soybeans, identifying selection footprints and favorable alleles in vegetable soybeans, and guiding genomic insights for enhanced breeding strategies.
- Why do we want to know? Vegetable soybean is an important East Asian crop with distinct traits from grain soybeans, shaped by long-term selection, but the underlying genomic changes are not well understood.
- What are the results? The study identified reduced genetic diversity and significant differentiation in vegetable soybeans, revealed key selective regions, and highlighted two candidate genes for targeted breeding.
Link: Book
Timeline: 2024-07 (Start) ➜ 2024-03 (Finish) ➜ 2024-05 (Retract).
Note: This work was conducted as part of my Master’s research. Restrictions on genotypic data availability have made formal publication infeasible.
A Multiple Phenotype Imputation for Genetic Diversity and Core Collection
Yen-Hsiang Huang, Hsin-Mei Ku, Chong-An Wang, Ling-Yu Chen, Shan-Syue He, Shu Chen, Po-Chun Liao, Pin-Yuan Juan, Chung-Feng Kao*
Multiple imputation enables efficient and accurate selection of representative core collections in vegetable soybean, overcoming challenges of missing and correlated phenotypic data for plant breeding.
- Why do we need this? Accurately selecting core collections for breeding is challenging due to missing and correlated phenotypic data, which limits the efficient use of germplasm resources.
- What are the results? By applying multiple imputation, we reliably selected a representative core collection with high morphological diversity, demonstrating improved efficiency and accuracy over traditional methods.
DOI: 10.3389/fpls.2022.948349
Timeline: 2022-01 (Start) ➜ 2022-05 (Submit) ➜ 2022-09 (Publish).
Research Review
Through these projects, I have mastered the ability to manipulate both genotypic and phenotypic data. Building on this foundation, I have actively broadened my research scope and contributed to the research community by developing an easy-to-use analysis platform. I am now continuing to expand my research into new fields. Jun 1, 2025