Computational Biology
Transcriptome Analysis
Gene Regultory Network
Ph.D. in Computer Science and Information Engeering (National Taiwan Normal University)
As a computational medicine lab, we have been attempting to answer important biomedical questions by developing appropriate methods to analyze data generated from cutting-edge technologies. In recent years, high throughput nucleic acid sequencing technology provides an opportunity to decode the genetic material comprehensively and precisely. Many sequencing technologies (e.g. WGS, RNA-seq, ChIP-seq, ATAC-seq, etc.) had been widely used nowadays. Our research focuses on comparative time-series RNA-seq data which help biologist reveal the dynamics of biological processes and gene regulatory networks. We overcame the challenge in extracting the valuable time information from the series of transcriptomic data by developing a method to construct the time-ordered gene coexpression network (TO-GCN), incorporating gene expression levels, time points, and tissues/conditions altogether.
This method had been applied to different studies, development of human mesenchymal stem cells (MSCs), zebrafish injured heart regeneration, embryonic mouse heart development, human cell line with SARS-CoV-2 infection, etc. Through closely collaborating with different researchers in various projects, we believe this method can help a lot to answer the important but open questions for a long time.
Using the single cell or spatial RNA-seq technology, we are developing a new single cell version of TO-GCN method that can largely improve our analysis resolution to the next (cell-type) level.