ARK: Aggregation of Reads by K-Means for Estimation of Bacterial Community Composition.
Identifieur interne : 001652 ( PubMed/Checkpoint ); précédent : 001651; suivant : 001653ARK: Aggregation of Reads by K-Means for Estimation of Bacterial Community Composition.
Auteurs : David Koslicki [États-Unis] ; Saikat Chatterjee [Suède] ; Damon Shahrivar [Suède] ; Alan W. Walker [Royaume-Uni] ; Suzanna C. Francis [Royaume-Uni] ; Louise J. Fraser [Royaume-Uni] ; Mikko Vehkaper [Royaume-Uni] ; Yueheng Lan [République populaire de Chine] ; Jukka Corander [Finlande]Source :
- PloS one [ 1932-6203 ] ; 2015.
Descripteurs français
- KwdFr :
- ADN bactérien (), ADN bactérien (génétique), ARN ribosomique 16S (génétique), Algorithmes, Analyse de regroupements, Analyse de séquence d'ADN, Bactéries (), Bactéries (génétique), Fèces (microbiologie), Humains, Internet, Microbiote (génétique), Métagénomique (), Reproductibilité des résultats, Réaction de polymérisation en chaîne.
- MESH :
English descriptors
- KwdEn :
- Algorithms, Bacteria (classification), Bacteria (genetics), Cluster Analysis, DNA, Bacterial (chemistry), DNA, Bacterial (genetics), Feces (microbiology), Humans, Internet, Metagenomics (methods), Microbiota (genetics), Polymerase Chain Reaction, RNA, Ribosomal, 16S (genetics), Reproducibility of Results, Sequence Analysis, DNA.
- MESH :
- chemical , chemistry : DNA, Bacterial.
- classification : Bacteria.
- genetics : Bacteria, DNA, Bacterial, Microbiota, RNA, Ribosomal, 16S.
- methods : Metagenomics.
- microbiology : Feces.
- Algorithms, Cluster Analysis, Humans, Internet, Polymerase Chain Reaction, Reproducibility of Results, Sequence Analysis, DNA.
Abstract
Estimation of bacterial community composition from high-throughput sequenced 16S rRNA gene amplicons is a key task in microbial ecology. Since the sequence data from each sample typically consist of a large number of reads and are adversely impacted by different levels of biological and technical noise, accurate analysis of such large datasets is challenging.
DOI: 10.1371/journal.pone.0140644
PubMed: 26496191
Affiliations:
- Finlande, Royaume-Uni, République populaire de Chine, Suède, États-Unis
- Angleterre, Grand Londres, Svealand, Uusimaa
- Helsinki, Londres, Pékin, Stockholm
- Université d'Helsinki
Links toward previous steps (curation, corpus...)
Links to Exploration step
pubmed:26496191Le document en format XML
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<term>Bacteria (classification)</term>
<term>Bacteria (genetics)</term>
<term>Cluster Analysis</term>
<term>DNA, Bacterial (chemistry)</term>
<term>DNA, Bacterial (genetics)</term>
<term>Feces (microbiology)</term>
<term>Humans</term>
<term>Internet</term>
<term>Metagenomics (methods)</term>
<term>Microbiota (genetics)</term>
<term>Polymerase Chain Reaction</term>
<term>RNA, Ribosomal, 16S (genetics)</term>
<term>Reproducibility of Results</term>
<term>Sequence Analysis, DNA</term>
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<term>ADN bactérien (génétique)</term>
<term>ARN ribosomique 16S (génétique)</term>
<term>Algorithmes</term>
<term>Analyse de regroupements</term>
<term>Analyse de séquence d'ADN</term>
<term>Bactéries ()</term>
<term>Bactéries (génétique)</term>
<term>Fèces (microbiologie)</term>
<term>Humains</term>
<term>Internet</term>
<term>Microbiote (génétique)</term>
<term>Métagénomique ()</term>
<term>Reproductibilité des résultats</term>
<term>Réaction de polymérisation en chaîne</term>
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</keywords>
<keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Bacteria</term>
<term>DNA, Bacterial</term>
<term>Microbiota</term>
<term>RNA, Ribosomal, 16S</term>
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<term>ARN ribosomique 16S</term>
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<term>Microbiote</term>
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<term>Cluster Analysis</term>
<term>Humans</term>
<term>Internet</term>
<term>Polymerase Chain Reaction</term>
<term>Reproducibility of Results</term>
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<term>Analyse de séquence d'ADN</term>
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<term>Internet</term>
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<front><div type="abstract" xml:lang="en">Estimation of bacterial community composition from high-throughput sequenced 16S rRNA gene amplicons is a key task in microbial ecology. Since the sequence data from each sample typically consist of a large number of reads and are adversely impacted by different levels of biological and technical noise, accurate analysis of such large datasets is challenging.</div>
</front>
</TEI>
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<DateCompleted><Year>2016</Year>
<Month>06</Month>
<Day>15</Day>
</DateCompleted>
<DateRevised><Year>2018</Year>
<Month>11</Month>
<Day>13</Day>
</DateRevised>
<Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN>
<JournalIssue CitedMedium="Internet"><Volume>10</Volume>
<Issue>10</Issue>
<PubDate><Year>2015</Year>
</PubDate>
</JournalIssue>
<Title>PloS one</Title>
<ISOAbbreviation>PLoS ONE</ISOAbbreviation>
</Journal>
<ArticleTitle>ARK: Aggregation of Reads by K-Means for Estimation of Bacterial Community Composition.</ArticleTitle>
<Pagination><MedlinePgn>e0140644</MedlinePgn>
</Pagination>
<ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0140644</ELocationID>
<Abstract><AbstractText Label="MOTIVATION" NlmCategory="BACKGROUND">Estimation of bacterial community composition from high-throughput sequenced 16S rRNA gene amplicons is a key task in microbial ecology. Since the sequence data from each sample typically consist of a large number of reads and are adversely impacted by different levels of biological and technical noise, accurate analysis of such large datasets is challenging.</AbstractText>
<AbstractText Label="RESULTS" NlmCategory="RESULTS">There has been a recent surge of interest in using compressed sensing inspired and convex-optimization based methods to solve the estimation problem for bacterial community composition. These methods typically rely on summarizing the sequence data by frequencies of low-order k-mers and matching this information statistically with a taxonomically structured database. Here we show that the accuracy of the resulting community composition estimates can be substantially improved by aggregating the reads from a sample with an unsupervised machine learning approach prior to the estimation phase. The aggregation of reads is a pre-processing approach where we use a standard K-means clustering algorithm that partitions a large set of reads into subsets with reasonable computational cost to provide several vectors of first order statistics instead of only single statistical summarization in terms of k-mer frequencies. The output of the clustering is then processed further to obtain the final estimate for each sample. The resulting method is called Aggregation of Reads by K-means (ARK), and it is based on a statistical argument via mixture density formulation. ARK is found to improve the fidelity and robustness of several recently introduced methods, with only a modest increase in computational complexity.</AbstractText>
<AbstractText Label="AVAILABILITY" NlmCategory="BACKGROUND">An open source, platform-independent implementation of the method in the Julia programming language is freely available at https://github.com/dkoslicki/ARK. A Matlab implementation is available at http://www.ee.kth.se/ctsoftware.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Koslicki</LastName>
<ForeName>David</ForeName>
<Initials>D</Initials>
<AffiliationInfo><Affiliation>Dept of Mathematics, Oregon State University, Corvallis, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Chatterjee</LastName>
<ForeName>Saikat</ForeName>
<Initials>S</Initials>
<AffiliationInfo><Affiliation>Dept of Communication Theory, KTH Royal Institute of Technology, Stockholm, Sweden.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Shahrivar</LastName>
<ForeName>Damon</ForeName>
<Initials>D</Initials>
<AffiliationInfo><Affiliation>Dept of Communication Theory, KTH Royal Institute of Technology, Stockholm, Sweden.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Walker</LastName>
<ForeName>Alan W</ForeName>
<Initials>AW</Initials>
<AffiliationInfo><Affiliation>Microbiology Group, Rowett Institute of Nutrition and Health, University of Aberdeen, Aberdeen, United Kingdom.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Francis</LastName>
<ForeName>Suzanna C</ForeName>
<Initials>SC</Initials>
<AffiliationInfo><Affiliation>MRC Tropical Epidemiology Group, London School of Hygiene and Tropical Medicine, London, United Kingdom.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Fraser</LastName>
<ForeName>Louise J</ForeName>
<Initials>LJ</Initials>
<AffiliationInfo><Affiliation>Illumina Cambridge Ltd., Chesterford Research Park, Essex, United Kingdom.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Vehkaperä</LastName>
<ForeName>Mikko</ForeName>
<Initials>M</Initials>
<AffiliationInfo><Affiliation>Dept of Electronic and Electrical Engineering, University of Sheffield, Sheffield, United Kingdom.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Lan</LastName>
<ForeName>Yueheng</ForeName>
<Initials>Y</Initials>
<AffiliationInfo><Affiliation>Dept of Physics, Tsinghua University, Beijing, China.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Corander</LastName>
<ForeName>Jukka</ForeName>
<Initials>J</Initials>
<AffiliationInfo><Affiliation>Dept of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.</Affiliation>
</AffiliationInfo>
</Author>
</AuthorList>
<Language>eng</Language>
<GrantList CompleteYN="Y"><Grant><Agency>Wellcome Trust</Agency>
<Country>United Kingdom</Country>
</Grant>
<Grant><GrantID>G0701039</GrantID>
<Agency>Medical Research Council</Agency>
<Country>United Kingdom</Country>
</Grant>
<Grant><GrantID>MR/K012126/1</GrantID>
<Agency>Medical Research Council</Agency>
<Country>United Kingdom</Country>
</Grant>
<Grant><GrantID>G1002369</GrantID>
<Agency>Medical Research Council</Agency>
<Country>United Kingdom</Country>
</Grant>
</GrantList>
<PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType>
<PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType>
</PublicationTypeList>
<ArticleDate DateType="Electronic"><Year>2015</Year>
<Month>10</Month>
<Day>23</Day>
</ArticleDate>
</Article>
<MedlineJournalInfo><Country>United States</Country>
<MedlineTA>PLoS One</MedlineTA>
<NlmUniqueID>101285081</NlmUniqueID>
<ISSNLinking>1932-6203</ISSNLinking>
</MedlineJournalInfo>
<ChemicalList><Chemical><RegistryNumber>0</RegistryNumber>
<NameOfSubstance UI="D004269">DNA, Bacterial</NameOfSubstance>
</Chemical>
<Chemical><RegistryNumber>0</RegistryNumber>
<NameOfSubstance UI="D012336">RNA, Ribosomal, 16S</NameOfSubstance>
</Chemical>
</ChemicalList>
<CitationSubset>IM</CitationSubset>
<MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="Y">Algorithms</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D001419" MajorTopicYN="N">Bacteria</DescriptorName>
<QualifierName UI="Q000145" MajorTopicYN="N">classification</QualifierName>
<QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D016000" MajorTopicYN="N">Cluster Analysis</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D004269" MajorTopicYN="N">DNA, Bacterial</DescriptorName>
<QualifierName UI="Q000737" MajorTopicYN="N">chemistry</QualifierName>
<QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D005243" MajorTopicYN="N">Feces</DescriptorName>
<QualifierName UI="Q000382" MajorTopicYN="N">microbiology</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D020407" MajorTopicYN="N">Internet</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D056186" MajorTopicYN="N">Metagenomics</DescriptorName>
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