We are studying Bioinformatics and Computational genomics

Bioinformatics is an interdisciplinary field that involves the application of computational techniques to store, manage, analyze, and interpret biological data. Computational genomics is a subset of bioinformatics that specifically focuses on the analysis of genomic data.

The characteristics of this laboratory

  1. This lab is contructed with the idea of both wet-lab and dry-lab. A “wet lab” refers to a traditional laboratory setting where experiments are conducted using biological or chemical materials. A “dry lab” refers to a laboratory setting where computational and analytical work is performed using computers and software tools.
  2. The main differences from the 100% wet lab:
    • Data-driven research, where the data obtained is authentic.
    • Requires the development of new methods and tools, cannot solely rely on existing tools.
  3. The main differences from the 100% dry lab:
    • Guided by life science questions (not computer problem-solving),
    • Focusing on the latest sequencing technologies.
    • Does not require highly specialized computer knowledge, with an emphasis on applications.
    • Both programming and biomedical knowledge are needed.
    • Emphasis on collaboration and interdisciplinary approaches.
    • Available for wet lab facilities, allowing for wet-lab work as support.

Research Topics

This lab has a primary focus on g next-generation sequencing (NGS) technologies such as ChIP-seq/CUT&TAG, Hi-C/Hi-ChIP/ChIAPET, RNA-seq/PROseq/GROseq, ATAC-seq, Bisulfite-seq, and single-cell sequencing. We primarily developing software, algorithms, and databases related to these NGS technologies. We also leverages multi-omics big data mining to explore biomedical questions. The main research keywords (until now) are summaried as the following wordcloud picture:

Our topics can also be summaried as:

  • Epigenetic Genomics (e.g., ChIP-seq, ATAC-seq)
  • 3D Genomics and Chromosome Architecture (e.g., Hi-C, ChIA-PET)
  • Transcriptional Regulatory Mechanisms (Cohesin protein, cis-regulatory module)
  • Multi-omics Integration (Large-scale multiomics analysis)
  • Disease Genomics (e.g., CdLS genetic disorder, Cancer Genomics)
  • Bioinformatics Software and Database Development
  • Machine Learning/Deep Learning
  • Single-cell Genomics

Two types of research projects

  1. Develop new methods/tools for NGS data:
  • Software, Algorithms, or Databases.
  • Addressing the Challenges in Genomic Data Analysis:
  • Command-line (CLI), Web-based (Web), or Graphical User Interface (GUI).
  • Require Programming and Interdisciplinary Skills.
  • Do not rely on datasets.
  • Purely dry experiments.
  1. Big Data Mining for Biomedical Research:
  • Public Data, or Collaborative Research with Novel Data, or Integration of Wet and Dry Labs.
  • Large-Scale Multi-omics Data in Human Diseases.
  • Genetic Disorders, or Cancer genomics, or Other Diseases.
  • Data-Driven Research.
  • Purely dry experiments, or Predominantly dry Work with Wet-experiments Support

Current research projects

1. Develop a computational system for large-scale multiomics analysis.
3. Cancer research based on multiomics.
4. Re-analysis of high-quality public datasets.

Programing Language

Linux/Shell, Python, R, JavaScript, Docker, HTML/CSS, Django, MySQL, C++, Java.


  • CPU server
  • GPU server
  • HDD server
  • Workstation (Linux, MacOS, Windows)
  • Benches, clean benches, cell incubators, and other basic wet lab equipment.
  • (Shared within our department) Advanced equipment for all commonly used molecular biological experiments.