Mining knowledge for the methylation status of CpG islands using alternating decision trees.
CpG island (CpGI) methylation is an epigenetic modification that occurs in eukaryotes and is based on the addition of a methyl group to the number 5 carbon of the pyrimidine ring of cytosine. When methylation of a CpGI occurs, the associated gene (if any) is not expressed [1]. Aberrant methylation is thought to be a causative agent in disease [2] and drug sensitivity [3], [4]. In this work, we have predicted the methylation status of CpGIs in human chromosome 21 using sequence patterns. These patterns showed a significantly different distribution between methylated and unmethylated islands in a previous work [5]. Using C4.5 with bagging and cost-sensitive learning, we achieved 85.6% accuracy, 82.8% sensitivity, and 86.4% specificity.We then constructed 1000 alternating decision trees using a bootstrapping method and analyzed the nodes that were conserved between the trees. This allowed us to find specific combinations of sequence patterns that distinguished between methylated and unmethylated CpGIs. Analysis of these characteristics offers certain insight into the conditions that permit or prevent methylation.
Carson
Matthew B
MB
Bioinformatics Program, Department of Bioengineering, University of Ilinois at Chicago, IL 60607, USA.
Langlois
Robert
R
Lu
Hui
H
D000465:Algorithms; D002891:Chromosomes, Human, Pair 21; D018899:CpG Islands / Q000235:genetics*; D019175:DNA Methylation; D003661:Decision Support Techniques; D015870:Gene Expression; D020868:Gene Silencing; D015894:Genome, Human; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D011401:Promoter Regions, Genetic; D015203:Reproducibility of Results