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This article is part of the supplement: ACM Conference on Bioinformatics, Computational Biology and Biomedicine 2011

Open Access Proceedings

A novel k-mer mixture logistic regression for methylation susceptibility modeling of CpG dinucleotides in human gene promoters

Youngik Yang1, Kenneth Nephew2 and Sun Kim3*

Author Affiliations

1 J Craig Venter Institute, San Diego, CA, USA

2 Medical Sciences Program, Indiana University School of Medicine, Bloomington, IN, USA

3 School of Computer Science and Engineering, Bioinformatics Institute, Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea

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BMC Bioinformatics 2012, 13(Suppl 3):S15  doi:10.1186/1471-2105-13-S3-S15

Published: 21 March 2012

Abstract

Background

DNA methylation is essential for normal development and differentiation and plays a crucial role in the development of nearly all types of cancer. Aberrant DNA methylation patterns, including genome-wide hypomethylation and region-specific hypermethylation, are frequently observed and contribute to the malignant phenotype. A number of studies have recently identified distinct features of genomic sequences that can be used for modeling specific DNA sequences that may be susceptible to aberrant CpG methylation in both cancer and normal cells. Although it is now possible, using next generation sequencing technologies, to assess human methylomes at base resolution, no reports currently exist on modeling cell type-specific DNA methylation susceptibility. Thus, we conducted a comprehensive modeling study of cell type-specific DNA methylation susceptibility at three different resolutions: CpG dinucleotides, CpG segments, and individual gene promoter regions.

Results

Using a k-mer mixture logistic regression model, we effectively modeled DNA methylation susceptibility across five different cell types. Further, at the segment level, we achieved up to 0.75 in AUC prediction accuracy in a 10-fold cross validation study using a mixture of k-mers.

Conclusions

The significance of these results is three fold: 1) this is the first report to indicate that CpG methylation susceptible "segments" exist; 2) our model demonstrates the significance of certain k-mers for the mixture model, potentially highlighting DNA sequence features (k-mers) of differentially methylated, promoter CpG island sequences across different tissue types; 3) as only 3 or 4 bp patterns had previously been used for modeling DNA methylation susceptibility, ours is the first demonstration that 6-mer modeling can be performed without loss of accuracy.