CpGIMethPred: computational model for predicting methylation status of CpG islands in human genome.

DNA methylation is an inheritable chemical modification of cytosine, and represents one of the most important epigenetic events. Computational prediction of the DNA methylation status can be employed to speed up the genome-wide methylation profiling, and to identify the key features that are correlated with various methylation patterns. Here, we develop CpGIMethPred, the support vector machine-based models to predict the methylation status of the CpG islands in the human genome under normal conditions. The features for prediction include those that have been previously demonstrated effective (CpG island specific attributes, DNA sequence composition patterns, DNA structure patterns, distribution patterns of conserved transcription factor binding sites and conserved elements, and histone methylation status) as well as those that have not been extensively explored but are likely to contribute additional information from a biological point of view (nucleosome positioning propensities, gene functions, and histone acetylation status). Statistical tests are performed to identify the features that are significantly correlated with the methylation status of the CpG islands, and principal component analysis is then performed to decorrelate the selected features. Data from the Human Epigenome Project (HEP) are used to train, validate and test the predictive models. Specifically, the models are trained and validated by using the DNA methylation data obtained in the CD4 lymphocytes, and are then tested for generalizability using the DNA methylation data obtained in the other 11 normal tissues and cell types. Our experiments have shown that (1) an eight-dimensional feature space that is selected via the principal component analysis and that combines all categories of information is effective for predicting the CpG island methylation status, (2) by incorporating the information regarding the nucleosome positioning, gene functions, and histone acetylation, the models can achieve higher specificity and accuracy than the existing models while maintaining a comparable sensitivity measure, (3) the histone modification (methylation and acetylation) information contributes significantly to the prediction, without which the performance of the models deteriorate, and, (4) the predictive models generalize well to different tissues and cell types. The developed program CpGIMethPred is freely available at http://users.ece.gatech.edu/~hzheng7/CGIMetPred.zip.