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


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Article Info

DATE

2012-03-21

AUTHORS

Youngik Yang, Kenneth Nephew, Sun Kim

ABSTRACT

BackgroundDNA 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.ResultsUsing 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.ConclusionsThe 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. More... »

PAGES

s15

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-13-s3-s15

DOI

http://dx.doi.org/10.1186/1471-2105-13-s3-s15

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1013364999

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/22536899


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