My research area is bioinformatics and computational biology. My goal is to understand how gene expression is regulated during development, by environmental stimulation, or in diseases in a genome-wide scale. The major approach is to apply computational and statistical methods to dicpher next generation sequencing data from those experiments.
Research Interests
My long-term goal is to understand genetic and epigenetic regulatory networks. The rapid advancement of DNA sequencing technology in recent years makes it possible to interrogate the gene regulation networks in a much quicker and deeper way. We focus on analyzing the next generation high-throughput sequencing data, including RNA-Seq, and ChIP-Seq from various projects to answer the specific biological questions. In addition, the current specific aims of my lab are: (1) microRNA target prediction using machine learing; (2) the differential binding of CREB and its mechanisms; and (3) integrative genomic data mining using supervised or semi-supervised learning approaches.
Shen R., and Goodman R. H. (2014) CREB’s differential binding to DNA by stimulus. In preparation.
Zhang G., Shen R., Chatzi C., Westbrook G. L., and Goodman R. H. (2014) Follow the transcriptome of adult newborn neurons during their functional maturation in the dentate gyrus. In preparation.
Lee S., Shen R., Cho H. H., Kwon R. J., Lee J. W., Lee S. K., (2013) Stat3 promotes motor neuron differentiation by collaborating with motor neuron-specific LIM complex. Proc Natl Acad Sci USA. 110: 11445-50
Cambronne X. A., Shen R., Auer P. L., and Goodman R. H. (2012) Capturing microRNA targets using an RNA-induced silencing complex (RISC)-trap approach. Proc Natl Acad Sci USA. 109: 20473-8
