Super Paramagnetic Clustering of DNA Sequences
Identifieur interne : 000688 ( Istex/Corpus ); précédent : 000687; suivant : 000689Super Paramagnetic Clustering of DNA Sequences
Auteurs : Sugiarto Radjiman ; Han Lianyi ; Wang Jian-Sheng ; Chen Yu ZongSource :
- Journal of Biological Physics [ 0092-0606 ] ; 2006-01-01.
English descriptors
- KwdEn :
Abstract
Abstract: An unsupervised clustering of 4541 DNA sequences containing active promoter regions from vertebrate and arthropod classes (including their viral genes) was performed. All necessary information was solely gathered a priori from the DNA sequences by measuring frequencies of tri-nucleotides and tetra-nucleotides. We employed Super Paramagnetic Clustering, a novel clustering algorithm based on physical properties of an inhomogeneous granular ferromagnet. This method utilizes Swendsen-Wang cluster Monte Carlo simulations to distinguish clusters by measuring pairs of correlation functions from different resolutions. We identified two strongly separated clusters of human viral genes corresponding to the Epstein-Barr virus and the Herpes Simplex virus type 1. In addition, vertebrate and arthropod sequences were successfully separated into two different classes with merely 9.25% of arthropod sequences being misclassified. From a functional perspective, these sequences have high gene function correlations with sequences from the vertebrate cluster. By tuning a clustering parameter, Super Paramagnetic Clustering was able to classify vertebrate class further into two major clusters, from where a large number of housekeeping genes and tissue-specific genes were found respectively. The indications came from observation of gene expression function and consensus transcription factors which were found grouped together in specific positions of the DNA sequences.
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DOI: 10.1007/s10867-006-2120-0
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ISTEX:26E3AE23957EF24AC44D870CB1866BB4F49C3740Le document en format XML
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All necessary information was solely gathered a priori from the DNA sequences by measuring frequencies of tri-nucleotides and tetra-nucleotides. We employed Super Paramagnetic Clustering, a novel clustering algorithm based on physical properties of an inhomogeneous granular ferromagnet. This method utilizes Swendsen-Wang cluster Monte Carlo simulations to distinguish clusters by measuring pairs of correlation functions from different resolutions. We identified two strongly separated clusters of human viral genes corresponding to the Epstein-Barr virus and the Herpes Simplex virus type 1. In addition, vertebrate and arthropod sequences were successfully separated into two different classes with merely 9.25% of arthropod sequences being misclassified. From a functional perspective, these sequences have high gene function correlations with sequences from the vertebrate cluster. By tuning a clustering parameter, Super Paramagnetic Clustering was able to classify vertebrate class further into two major clusters, from where a large number of housekeeping genes and tissue-specific genes were found respectively. The indications came from observation of gene expression function and consensus transcription factors which were found grouped together in specific positions of the DNA sequences.</div></front></TEI><istex><corpusName>springer-journals</corpusName><author><json:item><name>Sugiarto Radjiman</name><affiliations><json:string>Department of Computational Science, National University of Singapore, 117543, Singapore, Republic of Singapore</json:string><json:string>E-mail: sugiarto@cz3.nus.edu.sg</json:string></affiliations></json:item><json:item><name>Han Lianyi</name><affiliations><json:string>Department of Computational Science, National University of Singapore, 117543, Singapore, Republic of Singapore</json:string></affiliations></json:item><json:item><name>Wang Jian-Sheng</name><affiliations><json:string>Department of Computational Science, National University of Singapore, 117543, Singapore, Republic of Singapore</json:string></affiliations></json:item><json:item><name>Chen Yu Zong</name><affiliations><json:string>Department of Computational Science, National University of Singapore, 117543, Singapore, Republic of Singapore</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>DNA sequence</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>promoters</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>transcription factor binding sites</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>cluster algorithm</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>data clustering</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>statistical physics</value></json:item></subject><articleId><json:string>2120</json:string><json:string>DO00022120</json:string></articleId><arkIstex>ark:/67375/VQC-BLW8RPP5-1</arkIstex><language><json:string>eng</json:string></language><originalGenre><json:string>OriginalPaper</json:string></originalGenre><abstract>Abstract: An unsupervised clustering of 4541 DNA sequences containing active promoter regions from vertebrate and arthropod classes (including their viral genes) was performed. 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By tuning a clustering parameter, Super Paramagnetic Clustering was able to classify vertebrate class further into two major clusters, from where a large number of housekeeping genes and tissue-specific genes were found respectively. 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All necessary information was solely gathered a priori from the DNA sequences by measuring frequencies of tri-nucleotides and tetra-nucleotides. We employed Super Paramagnetic Clustering, a novel clustering algorithm based on physical properties of an inhomogeneous granular ferromagnet. This method utilizes Swendsen-Wang cluster Monte Carlo simulations to distinguish clusters by measuring pairs of correlation functions from different resolutions. We identified two strongly separated clusters of human viral genes corresponding to the Epstein-Barr virus and the Herpes Simplex virus type 1. In addition, vertebrate and arthropod sequences were successfully separated into two different classes with merely 9.25% of arthropod sequences being misclassified. From a functional perspective, these sequences have high gene function correlations with sequences from the vertebrate cluster. By tuning a clustering parameter, Super Paramagnetic Clustering was able to classify vertebrate class further into two major clusters, from where a large number of housekeeping genes and tissue-specific genes were found respectively. The indications came from observation of gene expression function and consensus transcription factors which were found grouped together in specific positions of the DNA sequences.</p></abstract><textClass xml:lang="en"><keywords scheme="keyword"><list><head>Key words</head><item><term>DNA sequence</term></item><item><term>promoters</term></item><item><term>transcription factor binding sites</term></item><item><term>cluster algorithm</term></item><item><term>data clustering</term></item><item><term>statistical physics</term></item></list></keywords></textClass><textClass><keywords scheme="Journal Subject"><list><head>Physics</head><item><term>Neurosciences</term></item><item><term>Polymer Sciences</term></item><item><term>Bioinformatics</term></item><item><term>Statistical Physics</term></item><item><term>Condensed Matter</term></item><item><term>Biophysics/Biomedical Physics</term></item></list></keywords></textClass></profileDesc><revisionDesc><change 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DisplayOrder="Western"><GivenName>Sugiarto</GivenName><FamilyName>Radjiman</FamilyName></AuthorName><Contact><Email>sugiarto@cz3.nus.edu.sg</Email></Contact></Author><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Han</GivenName><FamilyName>Lianyi</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Wang</GivenName><FamilyName>Jian-Sheng</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Chen</GivenName><GivenName>Yu</GivenName><FamilyName>Zong</FamilyName></AuthorName></Author><Affiliation ID="Aff1"><OrgDivision>Department of Computational Science</OrgDivision><OrgName>National University of Singapore</OrgName><OrgAddress><Postcode>117543</Postcode><City>Singapore</City><Country Code="SG">Republic of Singapore</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>An unsupervised clustering of 4541 DNA sequences containing active promoter regions from vertebrate and arthropod classes (including their viral genes) was performed. All necessary information was solely gathered a priori from the DNA sequences by measuring frequencies of tri-nucleotides and tetra-nucleotides. We employed Super Paramagnetic Clustering, a novel clustering algorithm based on physical properties of an inhomogeneous granular ferromagnet. This method utilizes Swendsen-Wang cluster Monte Carlo simulations to distinguish clusters by measuring pairs of correlation functions from different resolutions. We identified two strongly separated clusters of human viral genes corresponding to the Epstein-Barr virus and the Herpes Simplex virus type 1. In addition, vertebrate and arthropod sequences were successfully separated into two different classes with merely 9.25% of arthropod sequences being misclassified. From a functional perspective, these sequences have high gene function correlations with sequences from the vertebrate cluster. By tuning a clustering parameter, Super Paramagnetic Clustering was able to classify vertebrate class further into two major clusters, from where a large number of housekeeping genes and tissue-specific genes were found respectively. The indications came from observation of gene expression function and consensus transcription factors which were found grouped together in specific positions of the DNA sequences.</Para></Abstract><KeywordGroup Language="En"><Heading>Key words</Heading><Keyword>DNA sequence</Keyword><Keyword>promoters</Keyword><Keyword>transcription factor binding sites</Keyword><Keyword>cluster algorithm</Keyword><Keyword>data clustering</Keyword><Keyword>statistical physics</Keyword></KeywordGroup></ArticleHeader><NoBody></NoBody></Article></Issue></Volume></Journal></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Super Paramagnetic Clustering of DNA Sequences</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>Super Paramagnetic Clustering of DNA Sequences</title></titleInfo><name type="personal" displayLabel="corresp"><namePart type="given">Sugiarto</namePart><namePart type="family">Radjiman</namePart><affiliation>Department of Computational Science, National University of Singapore, 117543, Singapore, Republic of Singapore</affiliation><affiliation>E-mail: sugiarto@cz3.nus.edu.sg</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Han</namePart><namePart type="family">Lianyi</namePart><affiliation>Department of Computational Science, National University of Singapore, 117543, Singapore, Republic of Singapore</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Wang</namePart><namePart type="family">Jian-Sheng</namePart><affiliation>Department of Computational Science, National University of Singapore, 117543, Singapore, Republic of Singapore</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Chen</namePart><namePart type="given">Yu</namePart><namePart type="family">Zong</namePart><affiliation>Department of Computational Science, National University of Singapore, 117543, Singapore, Republic of Singapore</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="research-article" displayLabel="OriginalPaper" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-1JC4F85T-7">research-article</genre><originInfo><publisher>Kluwer Academic Publishers</publisher><place><placeTerm type="text">Dordrecht</placeTerm></place><dateIssued encoding="w3cdtf">2006-01-01</dateIssued><copyrightDate encoding="w3cdtf">2006</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract lang="en">Abstract: An unsupervised clustering of 4541 DNA sequences containing active promoter regions from vertebrate and arthropod classes (including their viral genes) was performed. All necessary information was solely gathered a priori from the DNA sequences by measuring frequencies of tri-nucleotides and tetra-nucleotides. We employed Super Paramagnetic Clustering, a novel clustering algorithm based on physical properties of an inhomogeneous granular ferromagnet. This method utilizes Swendsen-Wang cluster Monte Carlo simulations to distinguish clusters by measuring pairs of correlation functions from different resolutions. We identified two strongly separated clusters of human viral genes corresponding to the Epstein-Barr virus and the Herpes Simplex virus type 1. In addition, vertebrate and arthropod sequences were successfully separated into two different classes with merely 9.25% of arthropod sequences being misclassified. From a functional perspective, these sequences have high gene function correlations with sequences from the vertebrate cluster. By tuning a clustering parameter, Super Paramagnetic Clustering was able to classify vertebrate class further into two major clusters, from where a large number of housekeeping genes and tissue-specific genes were found respectively. The indications came from observation of gene expression function and consensus transcription factors which were found grouped together in specific positions of the DNA sequences.</abstract><subject lang="en"><genre>Key words</genre><topic>DNA sequence</topic><topic>promoters</topic><topic>transcription factor binding sites</topic><topic>cluster algorithm</topic><topic>data clustering</topic><topic>statistical physics</topic></subject><relatedItem type="host"><titleInfo><title>Journal of Biological Physics</title></titleInfo><titleInfo type="abbreviated"><title>J Biol Phys</title></titleInfo><genre type="journal" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0GLKJH51-B">journal</genre><originInfo><publisher>Springer</publisher><dateIssued encoding="w3cdtf">2006-01-01</dateIssued><copyrightDate encoding="w3cdtf">2006</copyrightDate></originInfo><subject><genre>Physics</genre><topic>Neurosciences</topic><topic>Polymer Sciences</topic><topic>Bioinformatics</topic><topic>Statistical Physics</topic><topic>Condensed Matter</topic><topic>Biophysics/Biomedical Physics</topic></subject><identifier type="ISSN">0092-0606</identifier><identifier type="eISSN">1573-0689</identifier><identifier type="JournalID">10867</identifier><identifier type="JournalSPIN">JOBP</identifier><identifier type="IssueArticleCount">5</identifier><identifier type="VolumeIssueCount">4</identifier><part><date>2006</date><detail type="volume"><number>32</number><caption>vol.</caption></detail><detail type="issue"><number>1</number><caption>no.</caption></detail><extent unit="pages"><start>11</start><end>25</end></extent></part><recordInfo><recordOrigin>Springer Science + Business Media, Inc., 2006</recordOrigin></recordInfo></relatedItem><identifier type="istex">26E3AE23957EF24AC44D870CB1866BB4F49C3740</identifier><identifier type="ark">ark:/67375/VQC-BLW8RPP5-1</identifier><identifier type="DOI">10.1007/s10867-006-2120-0</identifier><identifier type="ArticleID">2120</identifier><identifier type="ArticleID">DO00022120</identifier><accessCondition type="use and reproduction" contentType="copyright">Springer Science + Business Media, Inc., 2006</accessCondition><recordInfo><recordContentSource authority="ISTEX" authorityURI="https://loaded-corpus.data.istex.fr" valueURI="https://loaded-corpus.data.istex.fr/ark:/67375/XBH-3XSW68JL-F">springer</recordContentSource><recordOrigin>Springer Science + Business Media, Inc., 2006</recordOrigin></recordInfo></mods><json:item><extension>json</extension><original>false</original><mimetype>application/json</mimetype><uri>https://api.istex.fr/ark:/67375/VQC-BLW8RPP5-1/record.json</uri></json:item></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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