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Int. J. Mol. Sci. 2017, 18(2), 420; doi:10.3390/ijms18020420

Genome-Wide Prediction of DNA Methylation Using DNA Composition and Sequence Complexity in Human

1
College of Informatics, Agricultural Bioinformatics Key Laboratory of Hubei Province, Huazhong Agricultural University, Wuhan 430070, China
2
College of Science, Huazhong Agricultural University, Wuhan 430070, China
*
Author to whom correspondence should be addressed.
Academic Editor: Mateus Webba da Silva
Received: 3 January 2017 / Revised: 3 February 2017 / Accepted: 8 February 2017 / Published: 16 February 2017
(This article belongs to the Section Biochemistry, Molecular and Cellular Biology)
View Full-Text   |   Download PDF [5014 KB, uploaded 16 February 2017]   |  

Abstract

DNA methylation plays a significant role in transcriptional regulation by repressing activity. Change of the DNA methylation level is an important factor affecting the expression of target genes and downstream phenotypes. Because current experimental technologies can only assay a small proportion of CpG sites in the human genome, it is urgent to develop reliable computational models for predicting genome-wide DNA methylation. Here, we proposed a novel algorithm that accurately extracted sequence complexity features (seven features) and developed a support-vector-machine-based prediction model with integration of the reported DNA composition features (trinucleotide frequency and GC content, 65 features) by utilizing the methylation profiles of embryonic stem cells in human. The prediction results from 22 human chromosomes with size-varied windows showed that the 600-bp window achieved the best average accuracy of 94.7%. Moreover, comparisons with two existing methods further showed the superiority of our model, and cross-species predictions on mouse data also demonstrated that our model has certain generalization ability. Finally, a statistical test of the experimental data and the predicted data on functional regions annotated by ChromHMM found that six out of 10 regions were consistent, which implies reliable prediction of unassayed CpG sites. Accordingly, we believe that our novel model will be useful and reliable in predicting DNA methylation. View Full-Text
Keywords: DNA methylation; predicted model; sequence complexity DNA methylation; predicted model; sequence complexity
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Wu, C.; Yao, S.; Li, X.; Chen, C.; Hu, X. Genome-Wide Prediction of DNA Methylation Using DNA Composition and Sequence Complexity in Human. Int. J. Mol. Sci. 2017, 18, 420.

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