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Bioinformatics. 2006 Sep 15;22(18):2204-9. Epub 2006 Jul 12.

Predicting methylation status of CpG islands in the human brain.

Author information

1
Bioinformatics Division, TNLIST, Department of Automation, Tsinghua University 100084 China.

Abstract

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 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.

AVAILABILITY:

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

CONTACT:

mzhang@cshl.edu

SUPPLEMENTARY INFORMATION:

Supplementary data available at Bioinformatics online and http://166.111.201.7/help.html.

PMID:
16837523
DOI:
10.1093/bioinformatics/btl377
[Indexed for MEDLINE]
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