Liang Chen

Professor of Quantitative and Computational Biology
Email Office RRI 416E Office Phone (213) 740-2143

Research & Practice Areas

Computational Biology, Bioinformatics

  • Summary Statement of Research Interests

    My long-term research goal is to develop statistical and computational methods to discover the underlying principles of gene expression regulation in eukaryotes, and to explore how variations or defects in gene regulation cause phenotypic variation or diseases. How a cell controls its gene expression is one of the most fundamental and interesting questions in various biological processes, from intrinsic developmental program to response to extrinsic stimuli. Thus, it is not surprising that gene expression is tightly regulated and coordinated at multiple levels. At the transcriptional level, the interactions between transcription factors and DNA binding motifs play pivotal roles in transcription initiation. So do epigenetic effects, including histone modifications and DNA modifications. At the post-transcriptional level, mRNA processing, mRNA degradation, and translational control all increase the complexity of gene expression regulation.

    Currently I am particularly interested in one type of gene regulation mechanisms: alternative pre-mRNA splicing. Alternative splicing (AS) is an important way to expand proteomic diversity in higher eukaryotes because multiple transcript isoforms produced from a single gene can result in protein isoforms with distinct functions. It has been estimated that more than 90% of human genes are alternatively spliced. The AS of multiple pre-mRNAs is tightly regulated and coordinated, which is an essential component for many biological processes including nervous system development and programmed cell death. Abnormal mRNA splicing contributes to many human diseases. Understanding the mechanisms of AS will shed light on therapeutic opportunities for these AS-related disabilities and diseases.

    My research is to address biological questions in AS by integrating and fully utilizing available experimental data. Therefore, my research depends on the analysis of large-scale data from biological sciences such as microarray data, high-throughput sequencing data, and so on. In addition, I am interested in genetic linkage and association studies to identify genetic variants underlying disease.

    Research Keywords

    mRNA alternative splicing, Microarray expression data analysis, High-throughput sequencing data analysis,
    Expression Quantitative Trait Loci (eQTL) mapping, Genetic linkage and association studies