title: Genome-Wide Prediction of DNA Methylation Using DNA Composition and Sequence Complexity in Human.
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.
authors.affiliation: College of Informatics, Agricultural Bioinformatics Key Laboratory of Hubei Province, Huazhong Agricultural University, Wuhan 430070, China. woo.kidd@gmail.com.
authors.forename: Chengchao
authors.initials: C
authors.lastname: Wu
authors.affiliation: College of Science, Huazhong Agricultural University, Wuhan 430070, China. yaoshixin@webmail.hzau.edu.cn.
authors.forename: Shixin
authors.initials: S
authors.lastname: Yao
authors.affiliation: College of Science, Huazhong Agricultural University, Wuhan 430070, China. lxh8930217@gmail.com.
authors.forename: Xinghao
authors.initials: X
authors.lastname: Li
authors.affiliation: College of Informatics, Agricultural Bioinformatics Key Laboratory of Hubei Province, Huazhong Agricultural University, Wuhan 430070, China. ccjljm@webmail.hzau.edu.cn.
authors.forename: Chujia
authors.initials: C
authors.lastname: Chen
authors.affiliation: College of Informatics, Agricultural Bioinformatics Key Laboratory of Hubei Province, Huazhong Agricultural University, Wuhan 430070, China. huxuehai@mail.hzau.edu.cn.
authors.forename: Xuehai
authors.initials: X
authors.lastname: Hu
mesh_terms: D000818:Animals; D001482:Base Composition; D019295:Computational Biology / Q000379:methods; D018899:CpG Islands; D019175:DNA Methylation; D066264:Datasets as Topic; D057890:Epigenomics / Q000379:methods; D020869:Gene Expression Profiling; D015894:Genome, Human; D055106:Genome-Wide Association Study; D006801:Humans; D008957:Models, Genetic; D012372:ROC Curve; D015203:Reproducibility of Results; D013045:Species Specificity
keywords: DNA methylation; predicted model; sequence complexity