Predicting methylation status of CpG islands in the human brain.
BACKGROUND
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 experimental 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 classifier 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 two-third of the data from other tissues reported in the MethDB database.


BACKGROUND
An online predictor based on MethCGI is available at http://166.111.201.7/MethCGI.html


BACKGROUND
mzhang@cshl.edu


BACKGROUND
Supplementary data available at Bioinformatics online and http://166.111.201.7/help.html.
Fang
Fang
F
Bioinformatics Division, TNLIST, Department of Automation, Tsinghua University 100084 China.
Fan
Shicai
S
Zhang
Xuegong
X
Zhang
Michael Q
MQ
D000465:Algorithms; D001185:Artificial Intelligence; D001483:Base Sequence; D001921:Brain / Q000378:metabolism*; D002874:Chromosome Mapping / Q000379:methods*; D018899:CpG Islands / Q000235:genetics*; D019175:DNA Methylation; D008969:Molecular Sequence Data; D009419:Nerve Tissue Proteins / Q000235:genetics*; D010363:Pattern Recognition, Automated; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*
D009419:Nerve Tissue Proteins