Detecting Superbubbles in Assembly Graphs
Identifieur interne : 000135 ( Istex/Corpus ); précédent : 000134; suivant : 000136Detecting Superbubbles in Assembly Graphs
Auteurs : Taku Onodera ; Kunihiko Sadakane ; Tetsuo ShibuyaSource :
- Lecture Notes in Computer Science [ 0302-9743 ]
Abstract
Abstract: We introduce a new concept of a subgraph class called a superbubble for analyzing assembly graphs, and propose an efficient algorithm for detecting it. Most assembly algorithms utilize assembly graphs like the de Bruijn graph or the overlap graph constructed from reads. From these graphs, many assembly algorithms first detect simple local graph structures (motifs), such as tips and bubbles, mainly to find sequencing errors. These motifs are easy to detect, but they are sometimes too simple to deal with more complex errors. The superbubble is an extension of the bubble, which is also important for analyzing assembly graphs. Though superbubbles are much more complex than ordinary bubbles, we show that they can be efficiently enumerated. We propose an average-case linear time algorithm (i.e., O(n + m) for a graph with n vertices and m edges) for graphs with a reasonable model, though the worst-case time complexity of our algorithm is quadratic (i.e., O(n(n + m))). Moreover, the algorithm is practically very fast: Our experiments show that our algorithm runs in reasonable time with a single CPU core even against a very large graph of a whole human genome.
Url:
DOI: 10.1007/978-3-642-40453-5_26
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Most assembly algorithms utilize assembly graphs like the de Bruijn graph or the overlap graph constructed from reads. From these graphs, many assembly algorithms first detect simple local graph structures (motifs), such as tips and bubbles, mainly to find sequencing errors. These motifs are easy to detect, but they are sometimes too simple to deal with more complex errors. The superbubble is an extension of the bubble, which is also important for analyzing assembly graphs. Though superbubbles are much more complex than ordinary bubbles, we show that they can be efficiently enumerated. We propose an average-case linear time algorithm (i.e., O(n + m) for a graph with n vertices and m edges) for graphs with a reasonable model, though the worst-case time complexity of our algorithm is quadratic (i.e., O(n(n + m))). Moreover, the algorithm is practically very fast: Our experiments show that our algorithm runs in reasonable time with a single CPU core even against a very large graph of a whole human genome.</div></front></TEI><istex><corpusName>springer-ebooks</corpusName><author><json:item><name>Taku Onodera</name><affiliations><json:string>Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, 108-8639, Minato-ku, Tokyo, Japan</json:string><json:string>E-mail: tk-ono@hgc.jp</json:string></affiliations></json:item><json:item><name>Kunihiko Sadakane</name><affiliations><json:string>National Institute of Informatics, 2-1-2 Hitotsubashi, 101-8430, Chiyoda-ku, Tokyo, Japan</json:string><json:string>E-mail: sada@nii.ac.jp</json:string></affiliations></json:item><json:item><name>Tetsuo Shibuya</name><affiliations><json:string>Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, 108-8639, Minato-ku, Tokyo, Japan</json:string><json:string>E-mail: tshibuya@hgc.jp</json:string></affiliations></json:item></author><arkIstex>ark:/67375/HCB-M738F4XW-6</arkIstex><language><json:string>eng</json:string></language><originalGenre><json:string>OriginalPaper</json:string></originalGenre><abstract>Abstract: We introduce a new concept of a subgraph class called a superbubble for analyzing assembly graphs, and propose an efficient algorithm for detecting it. Most assembly algorithms utilize assembly graphs like the de Bruijn graph or the overlap graph constructed from reads. From these graphs, many assembly algorithms first detect simple local graph structures (motifs), such as tips and bubbles, mainly to find sequencing errors. These motifs are easy to detect, but they are sometimes too simple to deal with more complex errors. The superbubble is an extension of the bubble, which is also important for analyzing assembly graphs. Though superbubbles are much more complex than ordinary bubbles, we show that they can be efficiently enumerated. We propose an average-case linear time algorithm (i.e., O(n + m) for a graph with n vertices and m edges) for graphs with a reasonable model, though the worst-case time complexity of our algorithm is quadratic (i.e., O(n(n + m))). 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Angeles</settlement><region>CA</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Doug</forename><surname>Tygar</surname></persName><affiliation><idno type="GRID" subtype="Institution">grid.47840.3f</idno><idno type="ISNI" subtype="Institution">0000000121817878</idno><orgName type="institution">University of California</orgName><address><settlement>Berkeley</settlement><region>CA</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Moshe</forename><forename type="first">Y.</forename><surname>Vardi</surname></persName><affiliation><idno type="GRID" subtype="Institution">grid.21940.3e</idno><idno type="ISNI" subtype="Institution"> 0000000419368278</idno><orgName type="institution">Rice University</orgName><address><settlement>Houston</settlement><region>TX</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Gerhard</forename><surname>Weikum</surname></persName><affiliation><idno type="GRID" subtype="Institution">grid.419607.d</idno><idno type="ISNI" subtype="Institution">0000000120969941</idno><orgName type="institution">Max-Planck Institute of Computer Science</orgName><address><settlement>Saarbrücken</settlement><country key="DE">GERMANY</country></address></affiliation></editor><idno type="pISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="seriesID">558</idno></series></biblStruct></sourceDesc></fileDesc><profileDesc><abstract xml:lang="en"><head>Abstract</head><p>We introduce a new concept of a subgraph class called a superbubble for analyzing assembly graphs, and propose an efficient algorithm for detecting it. Most assembly algorithms utilize assembly graphs like the de Bruijn graph or the overlap graph constructed from reads. From these graphs, many assembly algorithms first detect simple local graph structures (motifs), such as tips and bubbles, mainly to find sequencing errors. These motifs are easy to detect, but they are sometimes too simple to deal with more complex errors. The superbubble is an extension of the bubble, which is also important for analyzing assembly graphs. Though superbubbles are much more complex than ordinary bubbles, we show that they can be efficiently enumerated. We propose an average-case linear time algorithm (<hi rend="italic">i.e.</hi>, <hi rend="italic">O</hi>(<hi rend="italic">n</hi> + <hi rend="italic">m</hi>) for a graph with <hi rend="italic">n</hi> vertices and <hi rend="italic">m</hi> edges) for graphs with a reasonable model, though the worst-case time complexity of our algorithm is quadratic (<hi rend="italic">i.e.</hi>, <hi rend="italic">O</hi>(<hi rend="italic">n</hi>(<hi rend="italic">n</hi> + <hi rend="italic">m</hi>))). Moreover, the algorithm is practically very fast: Our experiments show that our algorithm runs in reasonable time with a single CPU core even against a very large graph of a whole human genome.</p></abstract><textClass ana="subject"><keywords scheme="book-subject-collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass ana="subject"><keywords scheme="book-subject"><list><label>SCI</label><item><term type="Primary">Computer Science</term></item><label>SCI23050</label><item><term type="Secondary" subtype="priority-1">Computational Biology/Bioinformatics</term></item><label>SCI16021</label><item><term type="Secondary" subtype="priority-2">Algorithm Analysis and Problem Complexity</term></item><label>SCI17028</label><item><term type="Secondary" subtype="priority-3">Discrete Mathematics in Computer Science</term></item><label>SCI16013</label><item><term type="Secondary" subtype="priority-4">Computation by Abstract 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Devices</BookSubject><BookSubject Code="SCI18030" Priority="5" Type="Secondary">Data Mining and Knowledge Discovery</BookSubject><BookSubject Code="SCI17036" Priority="6" Type="Secondary">Probability and Statistics in Computer Science</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID><SubSeriesID>5381</SubSeriesID></BookContext><ConferenceInfo><ConfSeriesName>International Workshop on Algorithms in Bioinformatics</ConfSeriesName><ConfSeriesID Type="Springer">wabi</ConfSeriesID><ConfSeriesID Type="DBLP">wabi</ConfSeriesID><ConfEventID Type="Springer">wabi2013</ConfEventID><ConfEventAbbreviation>WABI</ConfEventAbbreviation><ConfNumber>13</ConfNumber><ConfEventLocation><City>Sophia 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Sydney</OrgName><OrgAddress><Postcode>2007</Postcode><City>Ultimo</City><State>NSW</State><Country>Australia</Country></OrgAddress></Affiliation><Affiliation ID="Aff20"><OrgID Level="Institution" Type="GRID">grid.7491.b</OrgID><OrgID Level="Institution" Type="ISNI">0000000109449128</OrgID><OrgDivision>Faculty of Technology</OrgDivision><OrgName>Bielefeld University</OrgName><OrgAddress><Street>Universitätsstraße 25</Street><Postcode>33615</Postcode><City>Bielefeld</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Chapter ID="Chap26" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>26</ChapterID><ChapterDOI>10.1007/978-3-642-40453-5_26</ChapterDOI><ChapterSequenceNumber>26</ChapterSequenceNumber><ChapterTitle Language="En">Detecting Superbubbles in Assembly Graphs</ChapterTitle><ChapterFirstPage>338</ChapterFirstPage><ChapterLastPage>348</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2013</CopyrightYear></ChapterCopyright><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants><ChapterContext><SeriesID>558</SeriesID><BookID>978-3-642-40453-5</BookID><BookTitle>Algorithms in Bioinformatics</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff21"><AuthorName DisplayOrder="Western"><GivenName>Taku</GivenName><FamilyName>Onodera</FamilyName></AuthorName><Contact><Email>tk-ono@hgc.jp</Email></Contact></Author><Author AffiliationIDS="Aff22"><AuthorName DisplayOrder="Western"><GivenName>Kunihiko</GivenName><FamilyName>Sadakane</FamilyName></AuthorName><Contact><Email>sada@nii.ac.jp</Email></Contact></Author><Author AffiliationIDS="Aff21"><AuthorName DisplayOrder="Western"><GivenName>Tetsuo</GivenName><FamilyName>Shibuya</FamilyName></AuthorName><Contact><Email>tshibuya@hgc.jp</Email></Contact></Author><Affiliation ID="Aff21"><OrgID Level="Institution" Type="GRID">grid.26999.3d</OrgID><OrgID Level="Institution" Type="ISNI">000000012151536X</OrgID><OrgDivision>Human Genome Center, Institute of Medical Science</OrgDivision><OrgName>University of Tokyo</OrgName><OrgAddress><Street>4-6-1 Shirokanedai</Street><City>Minato-ku</City><State>Tokyo</State><Postcode>108-8639</Postcode><Country>Japan</Country></OrgAddress></Affiliation><Affiliation ID="Aff22"><OrgID Level="Institution" Type="GRID">grid.250343.3</OrgID><OrgID Level="Institution" Type="ISNI">0000000110185342</OrgID><OrgName>National Institute of Informatics</OrgName><OrgAddress><Street>2-1-2 Hitotsubashi</Street><City>Chiyoda-ku</City><State>Tokyo</State><Postcode>101-8430</Postcode><Country>Japan</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>We introduce a new concept of a subgraph class called a superbubble for analyzing assembly graphs, and propose an efficient algorithm for detecting it. Most assembly algorithms utilize assembly graphs like the de Bruijn graph or the overlap graph constructed from reads. From these graphs, many assembly algorithms first detect simple local graph structures (motifs), such as tips and bubbles, mainly to find sequencing errors. These motifs are easy to detect, but they are sometimes too simple to deal with more complex errors. The superbubble is an extension of the bubble, which is also important for analyzing assembly graphs. Though superbubbles are much more complex than ordinary bubbles, we show that they can be efficiently enumerated. We propose an average-case linear time algorithm (<Emphasis Type="Italic">i.e.</Emphasis>, <Emphasis Type="Italic">O</Emphasis>(<Emphasis Type="Italic">n</Emphasis> + <Emphasis Type="Italic">m</Emphasis>) for a graph with <Emphasis Type="Italic">n</Emphasis> vertices and <Emphasis Type="Italic">m</Emphasis> edges) for graphs with a reasonable model, though the worst-case time complexity of our algorithm is quadratic (<Emphasis Type="Italic">i.e.</Emphasis>, <Emphasis Type="Italic">O</Emphasis>(<Emphasis Type="Italic">n</Emphasis>(<Emphasis Type="Italic">n</Emphasis> + <Emphasis Type="Italic">m</Emphasis>))). Moreover, the algorithm is practically very fast: Our experiments show that our algorithm runs in reasonable time with a single CPU core even against a very large graph of a whole human genome.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Book></Series></Publisher><!-- Converted from LaTeX with LaTeX2A++ V3.1.46 --></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Detecting Superbubbles in Assembly Graphs</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>Detecting Superbubbles in Assembly Graphs</title></titleInfo><name type="personal"><namePart type="given">Taku</namePart><namePart type="family">Onodera</namePart><affiliation>Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, 108-8639, Minato-ku, Tokyo, Japan</affiliation><affiliation>E-mail: tk-ono@hgc.jp</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Kunihiko</namePart><namePart type="family">Sadakane</namePart><affiliation>National Institute of Informatics, 2-1-2 Hitotsubashi, 101-8430, Chiyoda-ku, Tokyo, Japan</affiliation><affiliation>E-mail: sada@nii.ac.jp</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Tetsuo</namePart><namePart type="family">Shibuya</namePart><affiliation>Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, 108-8639, Minato-ku, Tokyo, Japan</affiliation><affiliation>E-mail: tshibuya@hgc.jp</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre displayLabel="OriginalPaper" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" type="conference" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-BFHXPBJJ-3">conference</genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2013</dateIssued><copyrightDate encoding="w3cdtf">2013</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract lang="en">Abstract: We introduce a new concept of a subgraph class called a superbubble for analyzing assembly graphs, and propose an efficient algorithm for detecting it. Most assembly algorithms utilize assembly graphs like the de Bruijn graph or the overlap graph constructed from reads. From these graphs, many assembly algorithms first detect simple local graph structures (motifs), such as tips and bubbles, mainly to find sequencing errors. These motifs are easy to detect, but they are sometimes too simple to deal with more complex errors. The superbubble is an extension of the bubble, which is also important for analyzing assembly graphs. Though superbubbles are much more complex than ordinary bubbles, we show that they can be efficiently enumerated. We propose an average-case linear time algorithm (i.e., O(n + m) for a graph with n vertices and m edges) for graphs with a reasonable model, though the worst-case time complexity of our algorithm is quadratic (i.e., O(n(n + m))). Moreover, the algorithm is practically very fast: Our experiments show that our algorithm runs in reasonable time with a single CPU core even against a very large graph of a whole human genome.</abstract><relatedItem type="host"><titleInfo><title>Algorithms in Bioinformatics</title><subTitle>13th International Workshop, WABI 2013, Sophia Antipolis, France, September 2-4, 2013. Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Aaron</namePart><namePart type="family">Darling</namePart><affiliation>ithree institute,, University of Technology Sydney, 2007, Ultimo, NSW, Australia</affiliation><affiliation>E-mail: aaron.darling@uts.edu.au</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jens</namePart><namePart type="family">Stoye</namePart><affiliation>Faculty of Technology, Bielefeld University, Universitätsstraße 25, 33615, Bielefeld, Germany</affiliation><affiliation>E-mail: jens.stoye@uni-bielefeld.de</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="book-series" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0G6R5W5T-Z">book-series</genre><originInfo><publisher>Springer</publisher><copyrightDate 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type="family">Hutchison</namePart><affiliation>Lancaster University, Lancaster, UK</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Takeo</namePart><namePart type="family">Kanade</namePart><affiliation>Carnegie Mellon University, Pittsburgh, PA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Josef</namePart><namePart type="family">Kittler</namePart><affiliation>University of Surrey, Guildford, UK</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jon</namePart><namePart type="given">M.</namePart><namePart type="family">Kleinberg</namePart><affiliation>Cornell University, Ithaca, NY, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Friedemann</namePart><namePart type="family">Mattern</namePart><affiliation>ETH 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type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moshe</namePart><namePart type="given">Y.</namePart><namePart type="family">Vardi</namePart><affiliation>Rice University, Houston, TX, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Gerhard</namePart><namePart type="family">Weikum</namePart><affiliation>Max-Planck Institute of Computer Science, Saarbrücken, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><originInfo><publisher>Springer</publisher><copyrightDate encoding="w3cdtf">2013</copyrightDate><issuance>serial</issuance></originInfo><relatedItem type="constituent"><titleInfo><title>Lecture Notes in Bioinformatics</title></titleInfo><name type="personal"><namePart type="given">David</namePart><namePart type="family">Hutchison</namePart><affiliation>Lancaster University, Lancaster, UK</affiliation><role><roleTerm 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type="given">Bernhard</namePart><namePart type="family">Steffen</namePart><affiliation>University of Dortmund, Dortmund, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Madhu</namePart><namePart type="family">Sudan</namePart><affiliation>Massachusetts Institute of Technology, MA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Demetri</namePart><namePart type="family">Terzopoulos</namePart><affiliation>University of California, Los Angeles, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Doug</namePart><namePart type="family">Tygar</namePart><affiliation>University of California, Berkeley, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moshe</namePart><namePart 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type="given">S.</namePart><namePart type="family">Waterman</namePart><affiliation>University of Southern California, Los Angeles, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Aaron</namePart><namePart type="family">Darling</namePart><affiliation>ithree institute,, University of Technology Sydney, 2007, Ultimo, NSW, Australia</affiliation><affiliation>E-mail: aaron.darling@uts.edu.au</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jens</namePart><namePart type="family">Stoye</namePart><affiliation>Faculty of Technology, Bielefeld University, Universitätsstraße 25, 33615, Bielefeld, Germany</affiliation><affiliation>E-mail: jens.stoye@uni-bielefeld.de</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="sub-series"></genre><identifier type="ISSN">0302-9743</identifier><identifier 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