A comparative study for characterisation and prediction of tissue-specific DNA methylation of CpG islands in chromosomes 6, 20 and 22

Advanced technology has enabled identification of tissue-specific methylated CpG islands of different human tissues. As methylation of CpG islands is involved in various biological phenomena and function of the DNA methylation is linked to various human diseases such as cancer, analysis of the CpG islands has become important and useful in characterising and modelling biological phenomena and understanding mechanism of such diseases. However, analysis of the data associated with the CpG islands is a quite new and challenging subject in bioinformatics, systems biology and epigenetics. In this paper, the problem associated with the prediction of methylated and unmethylated CpG islands on human chromosomes 6, 20 and 22 is addressed. In order to carry out the prediction, a data set of 451 samples of the CpG islands from 12 tissues of chromosomes 6, 20 and 22 was obtained. In addition, four different feature sub-sets totalling 50 attributes that characterise the methylated and unmethylated groups are extracted for each sample. These four feature sub-sets are (1) Tissue-specific CpGI methylation, (2) Evolutionary and conservation, (3) Sequence distribution and (4) DNA structure and properties. Due to the nature of this unbalanced data set, in order to avoid disadvantages of traditional leave-one-out (LOO) and m-fold cross validation methods, the LOO method is modified by incorporating the m-fold cross validation approach. The K-nearest neighbour classifier is then adapted for the prediction. The results obtained through 450 different comprehensive analyses show that the methylated CpG islands can be distinguished from the unmethylated CpG islands by a predictive accuracy of between 93.33% and 100%. More importantly, the modified LOO identifies more clearly and reliably these two groups when the feature sub-sets are combined. In addition, the modified-LOO cross validation identified the tissue-specific CpGI methylation feature sub-set as one of the most significant sets whereas it is not the case in the traditional cross validation methods.