Novel approaches to the prediction of CpG islands and their methylation status
Proceedings of the 2007 Summer Computer Simulation Conference
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Detailed methylation prediction of CpG islands on human chromosome 21
MCBC'09 Proceedings of the 10th WSEAS international conference on Mathematics and computers in biology and chemistry
Designing of a novel GA based on fuzzy system for prediction of CPG islands in the human genome
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
ChIP-seq data plays an important role in a cytosine-based DNA methylation prediction model
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Prediction of methylation CpGs and their methylation degrees in human DNA sequences
Computers in Biology and Medicine
| Hi-index | 3.84 |
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