A novel k-mer mixture logistic regression for methylation susceptibility modeling of CpG dinucleotides in human gene promoters
Identifieur interne : 000950 ( Ncbi/Curation ); précédent : 000949; suivant : 000951A novel k-mer mixture logistic regression for methylation susceptibility modeling of CpG dinucleotides in human gene promoters
Auteurs : Youngik Yang [États-Unis] ; Kenneth Nephew [États-Unis] ; Sun Kim [Corée du Sud]Source :
- BMC Bioinformatics [ 1471-2105 ] ; 2012.
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Abstract
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.
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.
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.
Url:
DOI: 10.1186/1471-2105-13-S3-S15
PubMed: 22536899
PubMed Central: 3311103
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PMC:3311103Le document en format XML
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<author><name sortKey="Nephew, Kenneth" sort="Nephew, Kenneth" uniqKey="Nephew K" first="Kenneth" last="Nephew">Kenneth Nephew</name>
<affiliation wicri:level="2"><nlm:aff id="I2">Medical Sciences Program, Indiana University School of Medicine, Bloomington, IN, USA</nlm:aff>
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<term>CpG Islands</term>
<term>DNA Methylation</term>
<term>Down Syndrome (genetics)</term>
<term>Humans</term>
<term>Logistic Models</term>
<term>Models, Genetic</term>
<term>Neoplasms (genetics)</term>
<term>Promoter Regions, Genetic</term>
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<keywords scheme="KwdFr" xml:lang="fr"><term>Chromosomes humains de la paire 21</term>
<term>Humains</term>
<term>Ilots CpG</term>
<term>Modèles génétiques</term>
<term>Modèles logistiques</term>
<term>Méthylation de l'ADN</term>
<term>Régions promotrices (génétique)</term>
<term>Syndrome de Down (génétique)</term>
<term>Tumeurs (génétique)</term>
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<term>Neoplasms</term>
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<term>Tumeurs</term>
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<keywords scheme="MESH" xml:lang="en"><term>Chromosomes, Human, Pair 21</term>
<term>CpG Islands</term>
<term>DNA Methylation</term>
<term>Humans</term>
<term>Logistic Models</term>
<term>Models, Genetic</term>
<term>Promoter Regions, Genetic</term>
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<term>Humains</term>
<term>Ilots CpG</term>
<term>Modèles génétiques</term>
<term>Modèles logistiques</term>
<term>Méthylation de l'ADN</term>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>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.</p>
</sec>
<sec><title>Results</title>
<p>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.</p>
</sec>
<sec><title>Conclusions</title>
<p>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.</p>
</sec>
</div>
</front>
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