iPSC system for studying the genetic basis of psychiatric disorders: to use iPSC technology and systems genomics approaches for studying neural development and abnormal gene regulation in neuropsychiatric disorders like schizophrenia and autism spectrum disorders.
In collaboration with experimentalist experts, we grow human neurons in dish by induced pluripotent stem cell (iPSC) technology in order to model human neuronal development and differentiation. We begin by developing iPSC lines from both patients and healthy subjects, differentiate them to neural progenitors and neurons, then use RNA-seq and other deep sequencing technology to identify differentially regulate genes by comparing the transcriptomes between patient-derived neurons and controls. Using this systems biology approach, we have identified many novel long non-coding RNA genes that are involved in embryonic neurogenesis and potentially neuropsychiatric disorders. We also find that many genes show allele-biased gene expression in different brain regions, including some that have been implicated in major psychiatric disorders, which may help explain some aspects of parent-of-origin effects, twin discordance and reduced penetrance. Our studies have also continously to uncover molecular pathways that are affected by critical genes that are major risk factors for schizophrenia and autisms.
Integrated analysis of functional genomics data: to explore computational techniques for combining experimental and computational genomics data in order to achieve a global understanding of the function of the human genome.
We are interested in developing bioinformatics algorithms to mine big genomics data and to conduct cross-genome sequence comparisons (e.g., syntenic assignment) with a focus on deciphering the gene regultory networks underlying normal development and development disorders. As large amount of functional genomic data from diverse sources (microarray, high-throughput sequencing, protein-protein interaction, etc.) are combined and used in our studies, our group develop and apply effective computational methods for data integration and at the same time for addressing common concerns of data quality in genome-scale experiments. A rigorous statistical framework needs to be built in order to extract biologically meaningful signals from noises or stochastic background.
Gene duplication: to investigate how gene duplication has shaped the human genome and how novel genes or regulatory elements emerge in humans and other primates.
Gene expansion (through either duplication or retrotransposition) is a major driving force for the emergence of novel functions during evolution. Inter- and intra-genome sequence comparison can reveal DNA elements that are either uniquely present or specifically selected in certain species. Tracking the evolutionary history of such sequences can lead to the discovery of genes or regulatory elements (including non-coding RNAs) that function specifically in humans. Thus, the long-term goal is to search for functional genomic components that set us apart from other animals. To this end, our research will compare the human genome with other mammalian (e.g., chimp, macaque, dog, and mouse) genomes to exploit the role of gene duplication in generating novel protein-protein interactions and novel biochemical pathways. We are interested in both the birth and the death (pseudogenization or loss) of such lineage-specific functional DNA sequences, especially for those involved in the development of nervous system and brains.
Develop software for improving data quality and addressing new bioinformatics challenges: to investigate how new data analysis algorithms can remove experimental noises and artifacts to increase data and result reproducibbility in big data analysis.
We are particularly interested in the generation of isoforms in different tissues / cells and how these special isoforms carry out their tissue-specific functions.