PNAS publishes research by Group of Jixian Zhai from Department of Biology
DATE:2018-01-19
On January 17th 2018, the research group of Associate Professor Jixian Zhai from SUSTech and Professor Steven Jacobsen from UCLA had their work published in PNAS, entitled “Large-scale comparative epigenomics reveals hierarchical regulation of non-CG methylation in Arabidopsis”. Dr. Yu Zhang from the Institute of Plant and Food Science of the Department of Biology, is the co-first author; Dr. Yanping Long, Dr. Lidan Xiao, Ph.D student Feng Li and R.A. Xu Chen are co-authors; Associate professor Jixian Zhai is one of the co-corresponding authors.
In plants, DNA cytosine methylation plays a central role in diverse cellular functions, from transcriptional regulation to maintenance of genome integrity. Vast amounts of Whole-Genome Bisulfite Sequencing (WGBS) datasets have been generated to profile DNA methylation at single nucleotide resolution, yet computational analyses vary widely among research groups making it difficult to cross-compare findings. In this study, group of Jixian Zhai processed hundreds of publicly available Arabidopsis WGBS libraries using a uniform pipeline. They identified high-confidence differentially methylated regions and compared libraries using a hierarchical framework, allowing them to identify known and novel relationships between methylation pathways. Furthermore, by using a large number of independent wild-type controls, they effectively filtered out spontaneous methylation changes from those that are biologically meaningful. Their work demonstrates that large-scale mining of genomics data can uncover biologically meaningful connections in this big-data era.
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Clustering of overlapping of high-confidence differentially methylated region of mutants
This work was supported by National Natural Science Foundation of China, Program for Guangdong Introducing Innovative and Entrepreneurial Teams and SUSTech Presidential Postdoctoral Fellowship.
Link to the paper:http://www.pnas.org/content/early/2018/01/10/1716300115.full
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