BIOINFORMATICS Predicting methylation status of CpG islands in the human brain

Motivation: Over 50 % of human genes contain CpG islands in their 5’-regions. Methylation patterns of CpG islands are involved in tissue-specific gene expression and regulation. Mis-epigenetic silencing associated with aberrant CpG island methylation is one mechanism leading to the loss of tumor suppressor functions in cancer cells. Large-scale experimen-tal detection of DNA methylation is still both labor-intensive and time-consuming. Therefore, it is necessary to develop in silico approaches for predicting methylation status of CpG islands. Results: Based on a recent genome-scale dataset of DNA methylation in human brain tissues, we developed a classi-fier called MethCGI for predicting methylation status of CpG islands using a Support Vector Machine (SVM). Nucleotide sequence contents as well as Transcription Factor Binding Sites (TFBSs) are used as features for the classification. The method achieves specificity of 84.65 % and sensitivity of 84.32 % on the brain data, and can also correctly predict about 2/3 of the data from other tissues reported in the MethDB database. Availability: An online predictor based on MethCGI is avail-able a