MersV1, PubMed, Corpus, bibRecord, 001D25

MISCORE: a new scoring function for characterizing DNA regulatory motifs in promoter sequences.

Identifieur interne : 001D25 ( PubMed/Corpus ); précédent : 001D24; suivant : 001D26

MISCORE: a new scoring function for characterizing DNA regulatory motifs in promoter sequences.

Auteurs : Dianhui Wang ; Sarwar Tapan

Source :

BMC systems biology [ 1752-0509 ] ; 2012.

RBID : pubmed:23282090

English descriptors

KwdEn :
- Algorithms, Computational Biology (methods), Nucleotide Motifs, Promoter Regions, Genetic (genetics).
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

Links to Exploration step

pubmed:23282090

Le document en format XML

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<author><name sortKey="Wang, Dianhui" sort="Wang, Dianhui" uniqKey="Wang D" first="Dianhui" last="Wang">Dianhui Wang</name>
<affiliation><nlm:affiliation>Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Victoria 3086, Australia. dh.wang@latrobe.edu.au</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Tapan, Sarwar" sort="Tapan, Sarwar" uniqKey="Tapan S" first="Sarwar" last="Tapan">Sarwar Tapan</name>
</author>
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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><nlm:affiliation>Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Victoria 3086, Australia. dh.wang@latrobe.edu.au</nlm:affiliation>
</affiliation>
</author>
<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>
<imprint><date when="2012" type="published">2012</date>
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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="MESH" qualifier="genetics" xml:lang="en"><term>Promoter Regions, Genetic</term>
</keywords>
<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Algorithms</term>
<term>Nucleotide Motifs</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>
</front>
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<pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">23282090</PMID>
<DateCompleted><Year>2013</Year>
<Month>06</Month>
<Day>06</Day>
</DateCompleted>
<DateRevised><Year>2018</Year>
<Month>11</Month>
<Day>13</Day>
</DateRevised>
<Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1752-0509</ISSN>
<JournalIssue CitedMedium="Internet"><Volume>6 Suppl 2</Volume>
<PubDate><Year>2012</Year>
</PubDate>
</JournalIssue>
<Title>BMC systems biology</Title>
<ISOAbbreviation>BMC Syst Biol</ISOAbbreviation>
</Journal>
<ArticleTitle>MISCORE: a new scoring function for characterizing DNA regulatory motifs in promoter sequences.</ArticleTitle>
<Pagination><MedlinePgn>S4</MedlinePgn>
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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>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Tapan</LastName>
<ForeName>Sarwar</ForeName>
<Initials>S</Initials>
</Author>
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<Language>eng</Language>
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<Month>12</Month>
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<QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName>
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<MeshHeading><DescriptorName UI="D059372" MajorTopicYN="Y">Nucleotide Motifs</DescriptorName>
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<MeshHeading><DescriptorName UI="D011401" MajorTopicYN="N">Promoter Regions, Genetic</DescriptorName>
<QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName>
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MISCORE: a new scoring function for characterizing DNA regulatory motifs in promoter sequences.

MISCORE: a new scoring function for characterizing DNA regulatory motifs in promoter sequences.

Source :

English descriptors

Abstract

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

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