A novel k-mer mixture logistic regression for methylation susceptibility modeling of CpG dinucleotides in human gene promoters.
Identifieur interne : 001D86 ( PubMed/Curation ); précédent : 001D85; suivant : 001D87A novel k-mer mixture logistic regression for methylation susceptibility modeling of CpG dinucleotides in human gene promoters.
Auteurs : Youngik Yang [États-Unis] ; Kenneth Nephew ; Sun KimSource :
- 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.
DOI: 10.1186/1471-2105-13-S3-S15
PubMed: 22536899
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">A novel k-mer mixture logistic regression for methylation susceptibility modeling of CpG dinucleotides in human gene promoters.</title><author><name sortKey="Yang, Youngik" sort="Yang, Youngik" uniqKey="Yang Y" first="Youngik" last="Yang">Youngik Yang</name><affiliation wicri:level="1"><nlm:affiliation>J Craig Venter Institute, San Diego, CA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>J Craig Venter Institute, San Diego, CA</wicri:regionArea></affiliation></author><author><name sortKey="Nephew, Kenneth" sort="Nephew, Kenneth" uniqKey="Nephew K" first="Kenneth" last="Nephew">Kenneth Nephew</name></author><author><name sortKey="Kim, Sun" sort="Kim, Sun" uniqKey="Kim S" first="Sun" last="Kim">Sun Kim</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2012">2012</date><idno type="RBID">pubmed:22536899</idno><idno type="pmid">22536899</idno><idno type="doi">10.1186/1471-2105-13-S3-S15</idno><idno type="wicri:Area/PubMed/Corpus">001D86</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">001D86</idno><idno type="wicri:Area/PubMed/Curation">001D86</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">001D86</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">A novel k-mer mixture logistic regression for methylation susceptibility modeling of CpG dinucleotides in human gene promoters.</title><author><name sortKey="Yang, Youngik" sort="Yang, Youngik" uniqKey="Yang Y" first="Youngik" last="Yang">Youngik Yang</name><affiliation wicri:level="1"><nlm:affiliation>J Craig Venter Institute, San Diego, CA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>J Craig Venter Institute, San Diego, CA</wicri:regionArea></affiliation></author><author><name sortKey="Nephew, Kenneth" sort="Nephew, Kenneth" uniqKey="Nephew K" first="Kenneth" last="Nephew">Kenneth Nephew</name></author><author><name sortKey="Kim, Sun" sort="Kim, Sun" uniqKey="Kim S" first="Sun" last="Kim">Sun Kim</name></author></analytic><series><title level="j">BMC bioinformatics</title><idno type="eISSN">1471-2105</idno><imprint><date when="2012" type="published">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Chromosomes, Human, Pair 21</term><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></keywords><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></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Down Syndrome</term><term>Neoplasms</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Syndrome de Down</term><term>Tumeurs</term></keywords><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></keywords><keywords scheme="MESH" 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></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">22536899</PMID><DateCompleted><Year>2013</Year><Month>03</Month><Day>14</Day></DateCompleted><DateRevised><Year>2018</Year><Month>12</Month><Day>01</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2105</ISSN><JournalIssue CitedMedium="Internet"><Volume>13 Suppl 3</Volume><PubDate><Year>2012</Year><Month>Mar</Month><Day>21</Day></PubDate></JournalIssue><Title>BMC bioinformatics</Title><ISOAbbreviation>BMC Bioinformatics</ISOAbbreviation></Journal><ArticleTitle>A novel k-mer mixture logistic regression for methylation susceptibility modeling of CpG dinucleotides in human gene promoters.</ArticleTitle><Pagination><MedlinePgn>S15</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/1471-2105-13-S3-S15</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="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.</AbstractText><AbstractText Label="RESULTS" NlmCategory="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.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="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.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Yang</LastName><ForeName>Youngik</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>J Craig Venter Institute, San Diego, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Nephew</LastName><ForeName>Kenneth</ForeName><Initials>K</Initials></Author><Author ValidYN="Y"><LastName>Kim</LastName><ForeName>Sun</ForeName><Initials>S</Initials></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>U54 CA11300-02</GrantID><Acronym>CA</Acronym><Agency>NCI NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2012</Year><Month>03</Month><Day>21</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMC 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UI="D009369" MajorTopicYN="N">Neoplasms</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011401" MajorTopicYN="Y">Promoter Regions, Genetic</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2012</Year><Month>4</Month><Day>28</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2012</Year><Month>5</Month><Day>2</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2013</Year><Month>3</Month><Day>15</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">22536899</ArticleId><ArticleId IdType="pii">1471-2105-13-S3-S15</ArticleId><ArticleId IdType="doi">10.1186/1471-2105-13-S3-S15</ArticleId><ArticleId 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