A novel k-mer mixture logistic regression for methylation susceptibility modeling of CpG dinucleotides in human gene promoters
Identifieur interne : 000A85 ( Pmc/Curation ); précédent : 000A84; suivant : 000A86A 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.
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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<author><name sortKey="Nephew, Kenneth" sort="Nephew, Kenneth" uniqKey="Nephew K" first="Kenneth" last="Nephew">Kenneth Nephew</name>
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">A novel k-mer mixture logistic regression for methylation susceptibility modeling of CpG dinucleotides in human gene promoters</title>
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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>
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<author><name sortKey="Jeltsch, A" uniqKey="Jeltsch A">A Jeltsch</name>
</author>
</analytic>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">BMC Bioinformatics</journal-id>
<journal-id journal-id-type="iso-abbrev">BMC Bioinformatics</journal-id>
<journal-title-group><journal-title>BMC Bioinformatics</journal-title>
</journal-title-group>
<issn pub-type="epub">1471-2105</issn>
<publisher><publisher-name>BioMed Central</publisher-name>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">22536899</article-id>
<article-id pub-id-type="pmc">3311103</article-id>
<article-id pub-id-type="publisher-id">1471-2105-13-S3-S15</article-id>
<article-id pub-id-type="doi">10.1186/1471-2105-13-S3-S15</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Proceedings</subject>
</subj-group>
</article-categories>
<title-group><article-title>A novel k-mer mixture logistic regression for methylation susceptibility modeling of CpG dinucleotides in human gene promoters</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" id="A1"><name><surname>Yang</surname>
<given-names>Youngik</given-names>
</name>
<xref ref-type="aff" rid="I1">1</xref>
<email>yyang@jcvi.org</email>
</contrib>
<contrib contrib-type="author" id="A2"><name><surname>Nephew</surname>
<given-names>Kenneth</given-names>
</name>
<xref ref-type="aff" rid="I2">2</xref>
<email>knephew@indiana.edu</email>
</contrib>
<contrib contrib-type="author" corresp="yes" id="A3"><name><surname>Kim</surname>
<given-names>Sun</given-names>
</name>
<xref ref-type="aff" rid="I3">3</xref>
<email>sunkim.bioinfo@snu.ac.kr</email>
</contrib>
</contrib-group>
<aff id="I1"><label>1</label>
J Craig Venter Institute, San Diego, CA, USA</aff>
<aff id="I2"><label>2</label>
Medical Sciences Program, Indiana University School of Medicine, Bloomington, IN, USA</aff>
<aff id="I3"><label>3</label>
School of Computer Science and Engineering, Bioinformatics Institute, Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea</aff>
<pub-date pub-type="collection"><year>2012</year>
</pub-date>
<pub-date pub-type="epub"><day>21</day>
<month>3</month>
<year>2012</year>
</pub-date>
<volume>13</volume>
<issue>Suppl 3</issue>
<supplement><named-content content-type="supplement-title">ACM Conference on Bioinformatics, Computational Biology and Biomedicine 2011</named-content>
<named-content content-type="supplement-editor">Sun Kim and Wei Wang</named-content>
<named-content content-type="supplement-sponsor">Publication of this supplement has been supported by NSF support number NSF IIS1137427: III: Small: Women in Bioinformatics Initiative at ACM-BCB 2011.</named-content>
</supplement>
<fpage>S15</fpage>
<lpage>S15</lpage>
<permissions><copyright-statement>Copyright ©2012 Yang et al.; licensee BioMed Central Ltd.</copyright-statement>
<copyright-year>2012</copyright-year>
<copyright-holder>Yang et al.; licensee BioMed Central Ltd.</copyright-holder>
<license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/2.0"><license-p>This is an open access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/2.0">http://creativecommons.org/licenses/by/2.0</ext-link>
), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
</license>
</permissions>
<self-uri xlink:href="http://www.biomedcentral.com/1471-2105/13/S3/S15"></self-uri>
<abstract><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>
</abstract>
<conference><conf-date>1-3 August 2011</conf-date>
<conf-name>ACM Conference on Bioinformatics, Computational Biology and Biomedicine 2011 (ACM-BCB)</conf-name>
<conf-loc>Chicago, IL, USA</conf-loc>
</conference>
</article-meta>
</front>
</pmc>
</record>
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