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.

2. 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.

2. Cohesin’s functions and related diseases.

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.

Facilities

• 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.