Data mining to identify gene regulatory elements

Gene regulatory elements are essential for the survival and development of all organisms but only a handful of gene regulatory elements have been discovered thus far in plants. Unfortunately, traditional methods to identify regulatory elements are ineffective as these regions are typically short (4-12 base pairs) and can be thousands of base pairs away from target genes. My project aimed to identify regulatory elements in plant genomes by mining a large and diverse set of histone modifications, chromatin modifications, and chromatin accessibility data with statistical approaches including K-means clustering and smoothing spline clustering. The results revealed an abundance of distal accessible chromatin regions in the maize genome which contain distinct combinations of chromatin modifications. Results from this project provide valuable clues for the future improvement of economically important crop traits.