MISCORE: a new scoring function for characterizing DNA regulatory motifs in promoter sequences.
Identifieur interne : 001D25 ( PubMed/Curation ); précédent : 001D24; suivant : 001D26MISCORE: a new scoring function for characterizing DNA regulatory motifs in promoter sequences.
Auteurs : Dianhui Wang [Australie] ; Sarwar TapanSource :
- BMC systems biology [ 1752-0509 ] ; 2012.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- genetics : Promoter Regions, Genetic.
- methods : Computational Biology.
- Algorithms, Nucleotide Motifs.
Abstract
Computational approaches for finding DNA regulatory motifs in promoter sequences are useful to biologists in terms of reducing the experimental costs and speeding up the discovery process of de novo binding sites. It is important for rule-based or clustering-based motif searching schemes to effectively and efficiently evaluate the similarity between a k-mer (a k-length subsequence) and a motif model, without assuming the independence of nucleotides in motif models or without employing computationally expensive Markov chain models to estimate the background probabilities of k-mers. Also, it is interesting and beneficial to use a priori knowledge in developing advanced searching tools.
DOI: 10.1186/1752-0509-6-S2-S4
PubMed: 23282090
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">MISCORE: a new scoring function for characterizing DNA regulatory motifs in promoter sequences.</title>
<author><name sortKey="Wang, Dianhui" sort="Wang, Dianhui" uniqKey="Wang D" first="Dianhui" last="Wang">Dianhui Wang</name>
<affiliation wicri:level="1"><nlm:affiliation>Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Victoria 3086, Australia. dh.wang@latrobe.edu.au</nlm:affiliation>
<country xml:lang="fr">Australie</country>
<wicri:regionArea>Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Victoria 3086</wicri:regionArea>
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<author><name sortKey="Tapan, Sarwar" sort="Tapan, Sarwar" uniqKey="Tapan S" first="Sarwar" last="Tapan">Sarwar Tapan</name>
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<sourceDesc><biblStruct><analytic><title xml:lang="en">MISCORE: a new scoring function for characterizing DNA regulatory motifs in promoter sequences.</title>
<author><name sortKey="Wang, Dianhui" sort="Wang, Dianhui" uniqKey="Wang D" first="Dianhui" last="Wang">Dianhui Wang</name>
<affiliation wicri:level="1"><nlm:affiliation>Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Victoria 3086, Australia. dh.wang@latrobe.edu.au</nlm:affiliation>
<country xml:lang="fr">Australie</country>
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<author><name sortKey="Tapan, Sarwar" sort="Tapan, Sarwar" uniqKey="Tapan S" first="Sarwar" last="Tapan">Sarwar Tapan</name>
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<series><title level="j">BMC systems biology</title>
<idno type="eISSN">1752-0509</idno>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term>
<term>Computational Biology (methods)</term>
<term>Nucleotide Motifs</term>
<term>Promoter Regions, Genetic (genetics)</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term>
<term>Biologie informatique ()</term>
<term>Motifs nucléotidiques</term>
<term>Régions promotrices (génétique) (génétique)</term>
</keywords>
<keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Promoter Regions, Genetic</term>
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<keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Régions promotrices (génétique)</term>
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<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term>
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<keywords scheme="MESH" xml:lang="en"><term>Algorithms</term>
<term>Nucleotide Motifs</term>
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<keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term>
<term>Biologie informatique</term>
<term>Motifs nucléotidiques</term>
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<front><div type="abstract" xml:lang="en">Computational approaches for finding DNA regulatory motifs in promoter sequences are useful to biologists in terms of reducing the experimental costs and speeding up the discovery process of de novo binding sites. It is important for rule-based or clustering-based motif searching schemes to effectively and efficiently evaluate the similarity between a k-mer (a k-length subsequence) and a motif model, without assuming the independence of nucleotides in motif models or without employing computationally expensive Markov chain models to estimate the background probabilities of k-mers. Also, it is interesting and beneficial to use a priori knowledge in developing advanced searching tools.</div>
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<DateCompleted><Year>2013</Year>
<Month>06</Month>
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<DateRevised><Year>2018</Year>
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<JournalIssue CitedMedium="Internet"><Volume>6 Suppl 2</Volume>
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<Title>BMC systems biology</Title>
<ISOAbbreviation>BMC Syst Biol</ISOAbbreviation>
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<ArticleTitle>MISCORE: a new scoring function for characterizing DNA regulatory motifs in promoter sequences.</ArticleTitle>
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<Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Computational approaches for finding DNA regulatory motifs in promoter sequences are useful to biologists in terms of reducing the experimental costs and speeding up the discovery process of de novo binding sites. It is important for rule-based or clustering-based motif searching schemes to effectively and efficiently evaluate the similarity between a k-mer (a k-length subsequence) and a motif model, without assuming the independence of nucleotides in motif models or without employing computationally expensive Markov chain models to estimate the background probabilities of k-mers. Also, it is interesting and beneficial to use a priori knowledge in developing advanced searching tools.</AbstractText>
<AbstractText Label="RESULTS" NlmCategory="RESULTS">This paper presents a new scoring function, termed as MISCORE, for functional motif characterization and evaluation. Our MISCORE is free from: (i) any assumption on model dependency; and (ii) the use of Markov chain model for background modeling. It integrates the compositional complexity of motif instances into the function. Performance evaluations with comparison to the well-known Maximum a Posteriori (MAP) score and Information Content (IC) have shown that MISCORE has promising capabilities to separate and recognize functional DNA motifs and its instances from non-functional ones.</AbstractText>
<AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">MISCORE is a fast computational tool for candidate motif characterization, evaluation and selection. It enables to embed priori known motif models for computing motif-to-motif similarity, which is more advantageous than IC and MAP score. In addition to these merits mentioned above, MISCORE can automatically filter out some repetitive k-mers from a motif model due to the introduction of the compositional complexity in the function. Consequently, the merits of our proposed MISCORE in terms of both motif signal modeling power and computational efficiency will make it more applicable in the development of computational motif discovery tools.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Wang</LastName>
<ForeName>Dianhui</ForeName>
<Initials>D</Initials>
<AffiliationInfo><Affiliation>Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Victoria 3086, Australia. dh.wang@latrobe.edu.au</Affiliation>
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<Author ValidYN="Y"><LastName>Tapan</LastName>
<ForeName>Sarwar</ForeName>
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<MeshHeading><DescriptorName UI="D059372" MajorTopicYN="Y">Nucleotide Motifs</DescriptorName>
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</ArticleIdList>
<ReferenceList><Reference><Citation>Science. 2000 Dec 22;290(5500):2306-9</Citation>
<ArticleIdList><ArticleId IdType="pubmed">11125145</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Bioinformatics. 2003 Oct;19 Suppl 2:ii5-14</Citation>
<ArticleIdList><ArticleId IdType="pubmed">14534164</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Nucleic Acids Res. 2002 Mar 1;30(5):1255-61</Citation>
<ArticleIdList><ArticleId IdType="pubmed">11861919</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>J Comput Biol. 2002;9(2):447-64</Citation>
<ArticleIdList><ArticleId IdType="pubmed">12015892</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Mol Biol Evol. 2002 Jul;19(7):1114-21</Citation>
<ArticleIdList><ArticleId IdType="pubmed">12082130</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>BMC Evol Biol. 2003 Aug 28;3:19</Citation>
<ArticleIdList><ArticleId IdType="pubmed">12946282</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Bioinformatics. 2003 Dec 12;19(18):2369-80</Citation>
<ArticleIdList><ArticleId IdType="pubmed">14668220</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Nucleic Acids Res. 2004 Jan 1;32(Database issue):D91-4</Citation>
<ArticleIdList><ArticleId IdType="pubmed">14681366</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Nucleic Acids Res. 2004 Jan 1;32(Database issue):D303-6</Citation>
<ArticleIdList><ArticleId IdType="pubmed">14681419</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Nucleic Acids Res. 2004;32(1):189-200</Citation>
<ArticleIdList><ArticleId IdType="pubmed">14704356</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Nucleic Acids Res. 2004;32(3):949-58</Citation>
<ArticleIdList><ArticleId IdType="pubmed">14963262</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Nucleic Acids Res. 2004;32(4):1372-81</Citation>
<ArticleIdList><ArticleId IdType="pubmed">14988425</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Proc Natl Acad Sci U S A. 2004 Mar 16;101(11):3797-802</Citation>
<ArticleIdList><ArticleId IdType="pubmed">14985506</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>J Mol Biol. 2004 Apr 23;338(2):207-15</Citation>
<ArticleIdList><ArticleId IdType="pubmed">15066426</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Bioinformatics. 2004 Apr 12;20(6):909-16</Citation>
<ArticleIdList><ArticleId IdType="pubmed">14751969</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Genome Biol. 2004;5(7):R48</Citation>
<ArticleIdList><ArticleId IdType="pubmed">15239833</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Brief Bioinform. 2004 Sep;5(3):217-36</Citation>
<ArticleIdList><ArticleId IdType="pubmed">15383209</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Nucleic Acids Res. 1994 Nov 11;22(22):4673-80</Citation>
<ArticleIdList><ArticleId IdType="pubmed">7984417</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Nucleic Acids Res. 1996 Oct 1;24(19):3836-45</Citation>
<ArticleIdList><ArticleId IdType="pubmed">8871566</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Trends Biochem Sci. 1998 Mar;23(3):109-13</Citation>
<ArticleIdList><ArticleId IdType="pubmed">9581503</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Nat Biotechnol. 2005 Jan;23(1):137-44</Citation>
<ArticleIdList><ArticleId IdType="pubmed">15637633</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Bioinformatics. 2005 May 1;21(9):1807-14</Citation>
<ArticleIdList><ArticleId IdType="pubmed">15647296</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W438-41</Citation>
<ArticleIdList><ArticleId IdType="pubmed">15980506</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Nucleic Acids Res. 2005;33(15):4899-913</Citation>
<ArticleIdList><ArticleId IdType="pubmed">16284194</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Nucleic Acids Res. 2006 Jan 1;34(Database issue):D108-10</Citation>
<ArticleIdList><ArticleId IdType="pubmed">16381825</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Nucleic Acids Res. 2006 Jan 1;34(Database issue):D63-7</Citation>
<ArticleIdList><ArticleId IdType="pubmed">16381947</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Cell. 2006 Apr 21;125(2):301-13</Citation>
<ArticleIdList><ArticleId IdType="pubmed">16630818</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Brief Bioinform. 2006 Mar;7(1):2-24</Citation>
<ArticleIdList><ArticleId IdType="pubmed">16761361</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Bioinformatics. 2006 Jul 1;22(13):1577-84</Citation>
<ArticleIdList><ArticleId IdType="pubmed">16632495</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>BMC Bioinformatics. 2006;7:396</Citation>
<ArticleIdList><ArticleId IdType="pubmed">16945132</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Annu Rev Genomics Hum Genet. 2006;7:315-38</Citation>
<ArticleIdList><ArticleId IdType="pubmed">16824019</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Genome Res. 2006 Dec;16(12):1455-64</Citation>
<ArticleIdList><ArticleId IdType="pubmed">17053094</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Bioinformatics. 2007 May 15;23(10):1188-94</Citation>
<ArticleIdList><ArticleId IdType="pubmed">17341493</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>BMC Bioinformatics. 2007;8 Suppl 7:S21</Citation>
<ArticleIdList><ArticleId IdType="pubmed">18047721</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>IEEE/ACM Trans Comput Biol Bioinform. 2008 Jan-Mar;5(1):110-9</Citation>
<ArticleIdList><ArticleId IdType="pubmed">18245880</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Bioinformatics. 2009 Jun 15;25(12):i356-64</Citation>
<ArticleIdList><ArticleId IdType="pubmed">19478010</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>BMC Bioinformatics. 2011;12 Suppl 1:S16</Citation>
<ArticleIdList><ArticleId IdType="pubmed">21342545</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Nat Biotechnol. 2002 Aug;20(8):835-9</Citation>
<ArticleIdList><ArticleId IdType="pubmed">12101404</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Nat Biotechnol. 2002 Aug;20(8):831-5</Citation>
<ArticleIdList><ArticleId IdType="pubmed">12101405</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Nucleic Acids Res. 2002 Dec 15;30(24):5549-60</Citation>
<ArticleIdList><ArticleId IdType="pubmed">12490723</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Bioinformatics. 2001 Dec;17(12):1113-22</Citation>
<ArticleIdList><ArticleId IdType="pubmed">11751219</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
</PubmedData>
</pubmed>
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