Interpolated variable order motifs for identification of horizontally acquired DNA: revisiting the Salmonella pathogenicity islands.
Identifieur interne : 002241 ( PubMed/Curation ); précédent : 002240; suivant : 002242Interpolated variable order motifs for identification of horizontally acquired DNA: revisiting the Salmonella pathogenicity islands.
Auteurs : Georgios S. Vernikos [Royaume-Uni] ; Julian ParkhillSource :
- Bioinformatics (Oxford, England) [ 1367-4811 ] ; 2006.
Descripteurs français
- KwdFr :
- MESH :
- génétique : ADN bactérien, Facteurs de virulence, Génome bactérien, Ilots génomiques, Salmonella, Transfert horizontal de gène, Virulence.
- pathogénicité : Salmonella.
- Algorithmes, Évolution biologique.
English descriptors
- KwdEn :
- MESH :
- chemical , genetics : DNA, Bacterial, Virulence Factors.
- genetics : Gene Transfer, Horizontal, Genome, Bacterial, Genomic Islands, Salmonella, Virulence.
- pathogenicity : Salmonella.
- Algorithms, Biological Evolution.
Abstract
There is a growing literature on the detection of Horizontal Gene Transfer (HGT) events by means of parametric, non-comparative methods. Such approaches rely only on sequence information and utilize different low and high order indices to capture compositional deviation from the genome backbone; the superiority of the latter over the former has been shown elsewhere. However even high order k-mers may be poor estimators of HGT, when insufficient information is available, e.g. in short sliding windows. Most of the current HGT prediction methods require pre-existing annotation, which may restrict their application on newly sequenced genomes.
DOI: 10.1093/bioinformatics/btl369
PubMed: 16837528
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pubmed:16837528Le document en format XML
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<affiliation wicri:level="1"><nlm:affiliation>The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus Hinxton, Cambridge CB10 1SA, UK. gsv@sanger.ac.uk</nlm:affiliation>
<country xml:lang="fr">Royaume-Uni</country>
<wicri:regionArea>The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus Hinxton, Cambridge CB10 1SA</wicri:regionArea>
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<author><name sortKey="Parkhill, Julian" sort="Parkhill, Julian" uniqKey="Parkhill J" first="Julian" last="Parkhill">Julian Parkhill</name>
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<series><title level="j">Bioinformatics (Oxford, England)</title>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term>
<term>Biological Evolution</term>
<term>DNA, Bacterial (genetics)</term>
<term>Gene Transfer, Horizontal (genetics)</term>
<term>Genome, Bacterial (genetics)</term>
<term>Genomic Islands (genetics)</term>
<term>Salmonella (genetics)</term>
<term>Salmonella (pathogenicity)</term>
<term>Virulence (genetics)</term>
<term>Virulence Factors (genetics)</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>ADN bactérien (génétique)</term>
<term>Algorithmes</term>
<term>Facteurs de virulence (génétique)</term>
<term>Génome bactérien (génétique)</term>
<term>Ilots génomiques (génétique)</term>
<term>Salmonella (génétique)</term>
<term>Salmonella (pathogénicité)</term>
<term>Transfert horizontal de gène (génétique)</term>
<term>Virulence (génétique)</term>
<term>Évolution biologique</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="genetics" xml:lang="en"><term>DNA, Bacterial</term>
<term>Virulence Factors</term>
</keywords>
<keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Gene Transfer, Horizontal</term>
<term>Genome, Bacterial</term>
<term>Genomic Islands</term>
<term>Salmonella</term>
<term>Virulence</term>
</keywords>
<keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>ADN bactérien</term>
<term>Facteurs de virulence</term>
<term>Génome bactérien</term>
<term>Ilots génomiques</term>
<term>Salmonella</term>
<term>Transfert horizontal de gène</term>
<term>Virulence</term>
</keywords>
<keywords scheme="MESH" qualifier="pathogenicity" xml:lang="en"><term>Salmonella</term>
</keywords>
<keywords scheme="MESH" qualifier="pathogénicité" xml:lang="fr"><term>Salmonella</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Algorithms</term>
<term>Biological Evolution</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term>
<term>Évolution biologique</term>
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<front><div type="abstract" xml:lang="en">There is a growing literature on the detection of Horizontal Gene Transfer (HGT) events by means of parametric, non-comparative methods. Such approaches rely only on sequence information and utilize different low and high order indices to capture compositional deviation from the genome backbone; the superiority of the latter over the former has been shown elsewhere. However even high order k-mers may be poor estimators of HGT, when insufficient information is available, e.g. in short sliding windows. Most of the current HGT prediction methods require pre-existing annotation, which may restrict their application on newly sequenced genomes.</div>
</front>
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<DateCompleted><Year>2006</Year>
<Month>09</Month>
<Day>25</Day>
</DateCompleted>
<DateRevised><Year>2010</Year>
<Month>11</Month>
<Day>18</Day>
</DateRevised>
<Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1367-4811</ISSN>
<JournalIssue CitedMedium="Internet"><Volume>22</Volume>
<Issue>18</Issue>
<PubDate><Year>2006</Year>
<Month>Sep</Month>
<Day>15</Day>
</PubDate>
</JournalIssue>
<Title>Bioinformatics (Oxford, England)</Title>
<ISOAbbreviation>Bioinformatics</ISOAbbreviation>
</Journal>
<ArticleTitle>Interpolated variable order motifs for identification of horizontally acquired DNA: revisiting the Salmonella pathogenicity islands.</ArticleTitle>
<Pagination><MedlinePgn>2196-203</MedlinePgn>
</Pagination>
<Abstract><AbstractText Label="MOTIVATION" NlmCategory="BACKGROUND">There is a growing literature on the detection of Horizontal Gene Transfer (HGT) events by means of parametric, non-comparative methods. Such approaches rely only on sequence information and utilize different low and high order indices to capture compositional deviation from the genome backbone; the superiority of the latter over the former has been shown elsewhere. However even high order k-mers may be poor estimators of HGT, when insufficient information is available, e.g. in short sliding windows. Most of the current HGT prediction methods require pre-existing annotation, which may restrict their application on newly sequenced genomes.</AbstractText>
<AbstractText Label="RESULTS" NlmCategory="RESULTS">We introduce a novel computational method, Interpolated Variable Order Motifs (IVOMs), which exploits compositional biases using variable order motif distributions and captures more reliably the local composition of a sequence compared with fixed-order methods. For optimal localization of the boundaries of each predicted region, a second order, two-state hidden Markov model (HMM) is implemented in a change-point detection framework. We applied the IVOM approach to the genome of Salmonella enterica serovar Typhi CT18, a well-studied prokaryote in terms of HGT events, and we show that the IVOMs outperform state-of-the-art low and high order motif methods predicting not only the already characterized Salmonella Pathogenicity Islands (SPI-1 to SPI-10) but also three novel SPIs (SPI-15, SPI-16, SPI-17) and other HGT events.</AbstractText>
<AbstractText Label="AVAILABILITY" NlmCategory="BACKGROUND">The software is available under a GPL license as a standalone application at http://www.sanger.ac.uk/Software/analysis/alien_hunter</AbstractText>
<AbstractText Label="CONTACT" NlmCategory="BACKGROUND">gsv@sanger.ac.uk</AbstractText>
<AbstractText Label="SUPPLEMENTARY INFORMATION" NlmCategory="BACKGROUND">Supplementary data are available at Bioinformatics online.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Vernikos</LastName>
<ForeName>Georgios S</ForeName>
<Initials>GS</Initials>
<AffiliationInfo><Affiliation>The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus Hinxton, Cambridge CB10 1SA, UK. gsv@sanger.ac.uk</Affiliation>
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<Author ValidYN="Y"><LastName>Parkhill</LastName>
<ForeName>Julian</ForeName>
<Initials>J</Initials>
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<Language>eng</Language>
<GrantList CompleteYN="Y"><Grant><Agency>Wellcome Trust</Agency>
<Country>United Kingdom</Country>
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<PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType>
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<ArticleDate DateType="Electronic"><Year>2006</Year>
<Month>07</Month>
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<MedlineJournalInfo><Country>England</Country>
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<MeshHeading><DescriptorName UI="D004269" MajorTopicYN="N">DNA, Bacterial</DescriptorName>
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<MeshHeading><DescriptorName UI="D012475" MajorTopicYN="N">Salmonella</DescriptorName>
<QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName>
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<MeshHeading><DescriptorName UI="D014774" MajorTopicYN="N">Virulence</DescriptorName>
<QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName>
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<MeshHeading><DescriptorName UI="D037521" MajorTopicYN="N">Virulence Factors</DescriptorName>
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