BOTUX: bayesian-like operational taxonomic unit examiner.
Identifieur interne : 000D94 ( Ncbi/Merge ); précédent : 000D93; suivant : 000D95BOTUX: bayesian-like operational taxonomic unit examiner.
Auteurs : Vishal N. Koparde [États-Unis] ; Ricky S. Adkins [États-Unis] ; Jennifer M. Fettweis [États-Unis] ; Myrna G. Serrano [États-Unis] ; Gregory A A. Buck [États-Unis] ; Mark A. Reimers [États-Unis] ; Nihar U. Sheth [États-Unis]Source :
- International journal of computational biology and drug design [ 1756-0756 ] ; 2014.
Descripteurs français
- KwdFr :
- MESH :
- génétique : ARN ribosomique 16S.
- Algorithmes, Analyse de séquence d'ADN, Théorème de Bayes.
English descriptors
- KwdEn :
- MESH :
- chemical , genetics : RNA, Ribosomal, 16S.
- Algorithms, Bayes Theorem, Sequence Analysis, DNA.
Abstract
Bayesian-like operational taxonomic unit examiner (BOTUX) is a new tool for the classification of 16S rRNA gene sequences into operational taxonomic units (OTUs) that addresses the problem of overestimation caused by errors introduced during PCR amplification and DNA sequencing steps. BOTUX utilises a grammar-based assignment strategy, where Bayesian models are built from each word of a given length (e.g., 8-mers). de novo analysis is possible with BOTUX as it does not require a training set, and updates probabilistic models as new sequences are recruited to an OTU. In benchmarking tests performed with real and simulated datasets of 16S rDNA sequences, BOTUX accurately identifies OTUs with comparable or better clustering efficiency and lower execution times than other OTU algorithms tested. BOTUX is the only OTU classifier, which allows incremental analysis of large datasets, and is also adept in clustering both 454 and Illumina datasets in a reasonable timeframe.
DOI: 10.1504/IJCBDD.2014.061652
PubMed: 24878725
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Reimers</name><affiliation wicri:level="2"><nlm:affiliation>Center for Study of Biological Complexity and Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA 23298, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Center for Study of Biological Complexity and Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA 23298</wicri:regionArea><placeName><region type="state">Virginie</region></placeName></affiliation></author><author><name sortKey="Sheth, Nihar U" sort="Sheth, Nihar U" uniqKey="Sheth N" first="Nihar U" last="Sheth">Nihar U. Sheth</name><affiliation wicri:level="2"><nlm:affiliation>Center for Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA 23284, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Center for Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA 23284</wicri:regionArea><placeName><region type="state">Virginie</region></placeName></affiliation></author></analytic><series><title level="j">International journal of computational biology and drug design</title><idno type="ISSN">1756-0756</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Bayes Theorem</term><term>RNA, Ribosomal, 16S (genetics)</term><term>Sequence Analysis, DNA</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>ARN ribosomique 16S (génétique)</term><term>Algorithmes</term><term>Analyse de séquence d'ADN</term><term>Théorème de Bayes</term></keywords><keywords scheme="MESH" type="chemical" qualifier="genetics" xml:lang="en"><term>RNA, Ribosomal, 16S</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>ARN ribosomique 16S</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Bayes Theorem</term><term>Sequence Analysis, DNA</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Analyse de séquence d'ADN</term><term>Théorème de Bayes</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Bayesian-like operational taxonomic unit examiner (BOTUX) is a new tool for the classification of 16S rRNA gene sequences into operational taxonomic units (OTUs) that addresses the problem of overestimation caused by errors introduced during PCR amplification and DNA sequencing steps. BOTUX utilises a grammar-based assignment strategy, where Bayesian models are built from each word of a given length (e.g., 8-mers). de novo analysis is possible with BOTUX as it does not require a training set, and updates probabilistic models as new sequences are recruited to an OTU. In benchmarking tests performed with real and simulated datasets of 16S rDNA sequences, BOTUX accurately identifies OTUs with comparable or better clustering efficiency and lower execution times than other OTU algorithms tested. BOTUX is the only OTU classifier, which allows incremental analysis of large datasets, and is also adept in clustering both 454 and Illumina datasets in a reasonable timeframe. </div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">24878725</PMID><DateCompleted><Year>2014</Year><Month>09</Month><Day>25</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">1756-0756</ISSN><JournalIssue CitedMedium="Print"><Volume>7</Volume><Issue>2-3</Issue><PubDate><Year>2014</Year></PubDate></JournalIssue><Title>International journal of computational biology and drug design</Title><ISOAbbreviation>Int J Comput Biol Drug Des</ISOAbbreviation></Journal><ArticleTitle>BOTUX: bayesian-like operational taxonomic unit examiner.</ArticleTitle><Pagination><MedlinePgn>130-45</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1504/IJCBDD.2014.061652</ELocationID><ELocationID EIdType="pii" ValidYN="Y">IJCBDD.2014.061652</ELocationID><Abstract><AbstractText>Bayesian-like operational taxonomic unit examiner (BOTUX) is a new tool for the classification of 16S rRNA gene sequences into operational taxonomic units (OTUs) that addresses the problem of overestimation caused by errors introduced during PCR amplification and DNA sequencing steps. BOTUX utilises a grammar-based assignment strategy, where Bayesian models are built from each word of a given length (e.g., 8-mers). de novo analysis is possible with BOTUX as it does not require a training set, and updates probabilistic models as new sequences are recruited to an OTU. In benchmarking tests performed with real and simulated datasets of 16S rDNA sequences, BOTUX accurately identifies OTUs with comparable or better clustering efficiency and lower execution times than other OTU algorithms tested. BOTUX is the only OTU classifier, which allows incremental analysis of large datasets, and is also adept in clustering both 454 and Illumina datasets in a reasonable timeframe. </AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Koparde</LastName><ForeName>Vishal N</ForeName><Initials>VN</Initials><AffiliationInfo><Affiliation>Center for Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA 23284, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Adkins</LastName><ForeName>Ricky S</ForeName><Initials>RS</Initials><AffiliationInfo><Affiliation>Center for Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA 23284, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Fettweis</LastName><ForeName>Jennifer M</ForeName><Initials>JM</Initials><AffiliationInfo><Affiliation>Center for Study of Biological Complexity and Department of Microbiology and Immunology, Virginia Commonwealth University, Richmond, VA 23284, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Serrano</LastName><ForeName>Myrna G</ForeName><Initials>MG</Initials><AffiliationInfo><Affiliation>Center for Study of Biological Complexity and Department of Microbiology and Immunology, Virginia Commonwealth University, Richmond, VA 23284, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Buck</LastName><ForeName>Gregory A A</ForeName><Initials>GA</Initials><AffiliationInfo><Affiliation>Center for Study of Biological Complexity and Department of Microbiology and Immunology, Virginia Commonwealth University, Richmond, VA 23284, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Reimers</LastName><ForeName>Mark A</ForeName><Initials>MA</Initials><AffiliationInfo><Affiliation>Center for Study of Biological Complexity and Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA 23298, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sheth</LastName><ForeName>Nihar U</ForeName><Initials>NU</Initials><AffiliationInfo><Affiliation>Center for Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA 23284, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>UH3 AI083263</GrantID><Acronym>AI</Acronym><Agency>NIAID NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>4UH3A1083263-02</GrantID><Agency>PHS HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2014</Year><Month>05</Month><Day>28</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Int J Comput Biol Drug Des</MedlineTA><NlmUniqueID>101479540</NlmUniqueID><ISSNLinking>1756-0756</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D012336">RNA, Ribosomal, 16S</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="N">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001499" MajorTopicYN="N">Bayes Theorem</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012336" MajorTopicYN="N">RNA, Ribosomal, 16S</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D017422" MajorTopicYN="Y">Sequence Analysis, 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IdType="pubmed">22369513</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country><region><li>Virginie</li></region></list><tree><country name="États-Unis"><region name="Virginie"><name sortKey="Koparde, Vishal N" sort="Koparde, Vishal N" uniqKey="Koparde V" first="Vishal N" last="Koparde">Vishal N. Koparde</name></region><name sortKey="Adkins, Ricky S" sort="Adkins, Ricky S" uniqKey="Adkins R" first="Ricky S" last="Adkins">Ricky S. Adkins</name><name sortKey="Buck, Gregory A A" sort="Buck, Gregory A A" uniqKey="Buck G" first="Gregory A A" last="Buck">Gregory A A. Buck</name><name sortKey="Fettweis, Jennifer M" sort="Fettweis, Jennifer M" uniqKey="Fettweis J" first="Jennifer M" last="Fettweis">Jennifer M. Fettweis</name><name sortKey="Reimers, Mark A" sort="Reimers, Mark A" uniqKey="Reimers M" first="Mark A" last="Reimers">Mark A. Reimers</name><name sortKey="Serrano, Myrna G" sort="Serrano, Myrna G" uniqKey="Serrano M" first="Myrna G" last="Serrano">Myrna G. Serrano</name><name sortKey="Sheth, Nihar U" sort="Sheth, Nihar U" uniqKey="Sheth N" first="Nihar U" last="Sheth">Nihar U. Sheth</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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