DNA methylation plays an important role in epigenetic regulation of gene expression. Previous investigations of DNA methylation in five maize inbred lines identified 368 differential methylation regions (DMR) (1). Interestingly, the sequences of these DMR are nearly identical (99%), suggesting that at least a portion of the variations for DNA methylation is purely epigenetic. Furthermore, this study found that expression levels of only a subset of genes (20%) were associated with DNA methylations. The study of DMR can help us to understand the origin and impact of differential methylations in crop species. In my project, I plan to use Python to reproduce the DMR identification and DMR merging approaches in previous studies (1). The input data of DMR analysis are generated from the bisulfite sequence mapping aligner. Through efficient computer programming techniques, I aim to produce a streamlined framework that could be incorporated into the Zea Epigenomics Database (ZED) portal for other researchers around the world to utilize. The framework would enable faster and automated analysis of the differential methylation patterns in a number of genotypes and allow for the study of associations of gene expression levels and DNA methylations. This tool will accelerate and further enhance the progression of epigenetics research.
Reference
1. Li, Q., Song, J., West, P.T., Zynda, G., Eichten, S.R., Vaughn, M.W. and Springer, N.M. (2015) Examining the causes and consequences of context-specific differential DNA methylation in maize. Plant Physiol.
Reference
1. Li, Q., Song, J., West, P.T., Zynda, G., Eichten, S.R., Vaughn, M.W. and Springer, N.M. (2015) Examining the causes and consequences of context-specific differential DNA methylation in maize. Plant Physiol.