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Predicted methylation landscape of all CpG islands on the human genome
Authors:ShiCai Fan  JianXiao Zou  HongBing Xu  XueGong Zhang
Affiliation:1 School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China; 
2 MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST / Department of Automation, Tsinghua University, Beijing 100084, China
Abstract:CpG island methylation plays important role in various biological processes. To investigate methylation landscape of all CpG islands on the human genome, we develop a model for predicting the CpG island methylation status. This model outperforms other existing methods. We apply the model on the whole human genome and predict the landscape of DNA methylation of all CpG islands. Based on the methylation profile, we find that about 31% of CpG islands are methylation-prone and CpG islands located in promoter regions are seldom methylated. There is no significant difference in the CpG island methylation level between R and G bands among the chromosomes. The occupancy of RNA polymerase II is significantly higher in methylation-resistant promoter CpG islands, indicating that genes with such promoter CpG islands tend to be more active.
Keywords:DNA methylation landscape  CpG island  prediction model  feature selection
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