SArKS: de novo discovery of gene expression regulatory motif sites and domains by suffix array kernel smoothing.
Identifieur interne : 000599 ( PubMed/Curation ); précédent : 000598; suivant : 000600SArKS: de novo discovery of gene expression regulatory motif sites and domains by suffix array kernel smoothing.
Auteurs : Dennis C. Wylie [États-Unis] ; Hans A. Hofmann [États-Unis] ; Boris V. Zemelman [États-Unis]Source :
- Bioinformatics (Oxford, England) [ 1367-4811 ] ; 2019.
Abstract
We set out to develop an algorithm that can mine differential gene expression data to identify candidate cell type-specific DNA regulatory sequences. Differential expression is usually quantified as a continuous score-fold-change, test-statistic, P-value-comparing biological classes. Unlike existing approaches, our de novo strategy, termed SArKS, applies non-parametric kernel smoothing to uncover promoter motif sites that correlate with elevated differential expression scores. SArKS detects motif k-mers by smoothing sequence scores over sequence similarity. A second round of smoothing over spatial proximity reveals multi-motif domains (MMDs). Discovered motif sites can then be merged or extended based on adjacency within MMDs. False positive rates are estimated and controlled by permutation testing.
DOI: 10.1093/bioinformatics/btz198
PubMed: 30903136
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Hofmann</name><affiliation wicri:level="1"><nlm:affiliation>Center for Computational Biology and Bioinformatics, University of Texas at Austin, Austin, TX, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Center for Computational Biology and Bioinformatics, University of Texas at Austin, Austin, TX</wicri:regionArea></affiliation></author><author><name sortKey="Zemelman, Boris V" sort="Zemelman, Boris V" uniqKey="Zemelman B" first="Boris V" last="Zemelman">Boris V. Zemelman</name><affiliation wicri:level="1"><nlm:affiliation>Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2019">2019</date><idno type="RBID">pubmed:30903136</idno><idno type="pmid">30903136</idno><idno type="doi">10.1093/bioinformatics/btz198</idno><idno type="wicri:Area/PubMed/Corpus">000599</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000599</idno><idno type="wicri:Area/PubMed/Curation">000599</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000599</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">SArKS: de novo discovery of gene expression regulatory motif sites and domains by suffix array kernel smoothing.</title><author><name sortKey="Wylie, Dennis C" sort="Wylie, Dennis C" uniqKey="Wylie D" first="Dennis C" last="Wylie">Dennis C. Wylie</name><affiliation wicri:level="1"><nlm:affiliation>Center for Computational Biology and Bioinformatics, University of Texas at Austin, Austin, TX, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Center for Computational Biology and Bioinformatics, University of Texas at Austin, Austin, TX</wicri:regionArea></affiliation></author><author><name sortKey="Hofmann, Hans A" sort="Hofmann, Hans A" uniqKey="Hofmann H" first="Hans A" last="Hofmann">Hans A. Hofmann</name><affiliation wicri:level="1"><nlm:affiliation>Center for Computational Biology and Bioinformatics, University of Texas at Austin, Austin, TX, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Center for Computational Biology and Bioinformatics, University of Texas at Austin, Austin, TX</wicri:regionArea></affiliation></author><author><name sortKey="Zemelman, Boris V" sort="Zemelman, Boris V" uniqKey="Zemelman B" first="Boris V" last="Zemelman">Boris V. Zemelman</name><affiliation wicri:level="1"><nlm:affiliation>Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX</wicri:regionArea></affiliation></author></analytic><series><title level="j">Bioinformatics (Oxford, England)</title><idno type="eISSN">1367-4811</idno><imprint><date when="2019" type="published">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">We set out to develop an algorithm that can mine differential gene expression data to identify candidate cell type-specific DNA regulatory sequences. Differential expression is usually quantified as a continuous score-fold-change, test-statistic, P-value-comparing biological classes. Unlike existing approaches, our de novo strategy, termed SArKS, applies non-parametric kernel smoothing to uncover promoter motif sites that correlate with elevated differential expression scores. SArKS detects motif k-mers by smoothing sequence scores over sequence similarity. A second round of smoothing over spatial proximity reveals multi-motif domains (MMDs). Discovered motif sites can then be merged or extended based on adjacency within MMDs. False positive rates are estimated and controlled by permutation testing.</div></front></TEI><pubmed><MedlineCitation Status="In-Data-Review" Owner="NLM"><PMID Version="1">30903136</PMID><DateRevised><Year>2020</Year><Month>01</Month><Day>08</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1367-4811</ISSN><JournalIssue CitedMedium="Internet"><Volume>35</Volume><Issue>20</Issue><PubDate><Year>2019</Year><Month>Oct</Month><Day>15</Day></PubDate></JournalIssue><Title>Bioinformatics (Oxford, England)</Title><ISOAbbreviation>Bioinformatics</ISOAbbreviation></Journal><ArticleTitle>SArKS: de novo discovery of gene expression regulatory motif sites and domains by suffix array kernel smoothing.</ArticleTitle><Pagination><MedlinePgn>3944-3952</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/bioinformatics/btz198</ELocationID><Abstract><AbstractText Label="MOTIVATION" NlmCategory="BACKGROUND">We set out to develop an algorithm that can mine differential gene expression data to identify candidate cell type-specific DNA regulatory sequences. Differential expression is usually quantified as a continuous score-fold-change, test-statistic, P-value-comparing biological classes. Unlike existing approaches, our de novo strategy, termed SArKS, applies non-parametric kernel smoothing to uncover promoter motif sites that correlate with elevated differential expression scores. SArKS detects motif k-mers by smoothing sequence scores over sequence similarity. A second round of smoothing over spatial proximity reveals multi-motif domains (MMDs). Discovered motif sites can then be merged or extended based on adjacency within MMDs. False positive rates are estimated and controlled by permutation testing.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">We applied SArKS to published gene expression data representing distinct neocortical neuron classes in Mus musculus and interneuron developmental states in Homo sapiens. When benchmarked against several existing algorithms using a cross-validation procedure, SArKS identified larger motif sets that formed the basis for regression models with higher correlative power.</AbstractText><AbstractText Label="AVAILABILITY AND IMPLEMENTATION" NlmCategory="METHODS">https://github.com/denniscwylie/sarks.</AbstractText><AbstractText Label="SUPPLEMENTARY INFORMATION" NlmCategory="BACKGROUND">Supplementary data are available at Bioinformatics online.</AbstractText><CopyrightInformation>© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Wylie</LastName><ForeName>Dennis C</ForeName><Initials>DC</Initials><AffiliationInfo><Affiliation>Center for Computational Biology and Bioinformatics, University of Texas at Austin, Austin, TX, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hofmann</LastName><ForeName>Hans A</ForeName><Initials>HA</Initials><AffiliationInfo><Affiliation>Center for Computational Biology and Bioinformatics, University of Texas at Austin, Austin, TX, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Integrative Biology, University of Texas at Austin, Austin, TX, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Institute for Neuroscience, University of Texas at Austin, Austin, TX, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zemelman</LastName><ForeName>Boris V</ForeName><Initials>BV</Initials><AffiliationInfo><Affiliation>Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Institute for Neuroscience, University of Texas at Austin, Austin, TX, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Neuroscience, University of Texas at Austin, Austin, TX, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Center for Learning and Memory, University of Texas at Austin, Austin, TX, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Bioinformatics</MedlineTA><NlmUniqueID>9808944</NlmUniqueID><ISSNLinking>1367-4803</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2018</Year><Month>03</Month><Day>12</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2019</Year><Month>03</Month><Day>04</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2019</Year><Month>03</Month><Day>20</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2019</Year><Month>3</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2019</Year><Month>3</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2019</Year><Month>3</Month><Day>24</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">30903136</ArticleId><ArticleId IdType="pii">5418797</ArticleId><ArticleId IdType="doi">10.1093/bioinformatics/btz198</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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