Research

We are working at the interface of Systems Biology, Computer Science and Molecular Neuroscience.

Elucidating RNA-regulatory networks

It is increasingly recognized that post-transcriptional regulation at RNA level plays critical roles for orchestrated gene expression in mammalian systems. Such regulation is conferred through interaction of at least several hundreds of RNA-binding proteins (RBPs) with their target transcripts, or RNA-regulatory networks. The challenge to infer RNA regulatory networks lies in the fact that Most RBPs recognize very short and generate sequence motifs with limited information content. We takes advantage of recent advances in biochemical and high-throughput assays, such as HITS-CLIP (or CLIP-Seq) and RNA-Seq that profile transcriptomes and protein-RNA interactomes. We apply these assays, mostly using mouse brain as a model system, and intersect them with statistical and machine learning approaches to develop methods that makes specific protein-RNA interactions to complement and enhance the information we can obtained from experimental data. We also develop integrative modeling approaches to combine multiple types of data to infer direct and functional targets of specific RBPs.

RNA-regulatory networks in evo-devo processes and in neuronal disorders

One of the ultimate goals to infer RNA-regulatory networks is to understand their form and function in normal physiological and pathological contexts. In real systems, the networks are under combinatorial regulations and dynamic. We are working toward a better understanding of how such dynamic regulation drives the developing mammalian brain and evolution in the normal process. We are also interested in understanding how such networks are perturbed in motor neuron diseases (such as SMA and ALS) and brain tumors (such as glioblastoma).

High-throughput transcriptomic data analysis

Our work heavily rely on high-throughput technologies which produces enormous amount of data, and algorithms to transform these data into useful information. We are interested in developing better algorithms to process transcroptomic data, such as mapping RNA-Seq data, discovering and quantifying alterations in RNA processing, and analyzing CLIP data to map protein-RNA interactions at single nucleotide resolution.