Introduction to Professor Jing Zhang and Her Lab at University of California, Irvine

1. Could you briefly introduce yourself (and your University/Lab)?

My research focuses on developing computational and statistical methods to uncover the underlying principles of the multi-step, tightly coordinated process of gene regulation, studying how genetic variations can manifest in phenotypic changes and even diseases. Particularly, I am interested in interpreting the noncoding regulome, where the overwhelming bulk of mutations – particularly those discovered from recent large-scale genomics initiatives – lie in.

2. What have been your most significant research contributions up to now?

I have a broad background in computational biology, bioinformatics, and machine learning, with specific training in identifying functional elements in the noncoding genome and quantifying genetic variants’ impact in diseases. My current research encompasses large-scale integration of multi-omics data, deep noncoding genome annotation, as well as the development of machine learning methods for disease genome mining. In the past, I have applied my expertise in machine learning to molecular biology and developed computational methods to uncover the fundamental principles of gene regulation. I have also advanced several computational methods elucidating how genetic and epigenetic variations can result in phenotypic changes in psychiatric diseases.

3. What problems in your research field deserve more attention (or what problems will you like to solve) in the next few years, and why?

Much of current genome annotation work centers on bulk sequencing data at the tissue level. However, cells, rather than tissues, are the basic structural and functional units of life. Therefore, in the next few years, I would like to develop computational methods to push our understanding of the gene regulation process to a single-cell resolution. In detail, we aim to build robust and scalable machine learning algorithms to integrate multiple single-cell multi-omics data and dissect its multi-scale regulatory grammar to evaluate both individual and combinatorial impacts variants have in genetic disorders.

4. What advice would you like to give to the young generation of researchers/engineers?

Perseverance. I always tell my students that research is different from class. Classes, at the longest, last only for a semester and exist in a controlled environment. Contrarily, research is a long and narrow road; most researchers will experience different types of difficulties at different stages. It takes time and perseverance to appreciate the final picture of a research project. Keeping a positive attitude is the key to success.