Cis-regulatory sequences in animals and plants are as important as coding sequences, but our understanding of them in most sequenced organisms is limited due to the difficulty in characterizing them. Recent developments of powerful functional genomic technologies, in particular, chromatin immunoprecipitation coupled with sequencing (ChIP-seq) techniques, have provided an unprecedented opportunity to decipher cis-regulatory sequences in a genome scale. However, it remains a highly challenging task to derive cis-regulatory sequences from a large number of very big ChIP-seq datasets. To tackle this challenge, this project will develop a set of novel algorithms and tools and apply them to annotate cis-regulatory sequences using a large number of ChIP-seq datasets in animals and plants. These tools and predicted cis-regulatory sequences will fundamentally change the ways that biologists study regulatory genomes and transcriptional regulation in humans and model organisms.
The project involves high school students, undergraduates, graduates and a postdoctoral fellow from the very beginning, thereby exposing them to algorithm and tool development, and helping them develop critical thinking skills needed to solve complex biological problems. The PI will particularly encourage traditionally underrepresented minority and/or female students to participate in the project. Whenever appropriate, the resulting algorithms and results will be incorporated into the PI's relevant courses taught at the University of North Carolina at Charlotte: undergraduate and graduate Molecular Sequence Analysis, Computational Comparative Genomics, and Mathematical Systems Biology. Thus, the project will also provide an ideal educational platform to train the next generation of computational biologists who can use very big datasets solving important biological problems. The tools and resources will be publicly available at http://bioinfo.uncc.edu/mniu/pcrms/www/.
