Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis.
Identifieur interne : 001301 ( PubMed/Curation ); précédent : 001300; suivant : 001302Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis.
Auteurs : Veronika B. Dubinkina [Russie] ; Dmitry S. Ischenko [Russie] ; Vladimir I. Ulyantsev [Russie] ; Alexander V. Tyakht [Russie] ; Dmitry G. Alexeev [Russie]Source :
- BMC bioinformatics [ 1471-2105 ] ; 2016.
Descripteurs français
- KwdFr :
- Analyse de regroupements, Analyse de séquence d'ADN, Bases de données génétiques, Biologie informatique, Cartographie chromosomique, Humains, Microbiome gastro-intestinal (génétique), Modèles moléculaires, Métagénome, Métagénomique (), Polymorphisme de nucléotide simple, Simulation numérique, Tube digestif (microbiologie).
- MESH :
- génétique : Microbiome gastro-intestinal.
- microbiologie : Tube digestif.
- Analyse de regroupements, Analyse de séquence d'ADN, Bases de données génétiques, Biologie informatique, Cartographie chromosomique, Humains, Modèles moléculaires, Métagénome, Métagénomique, Polymorphisme de nucléotide simple, Simulation numérique.
English descriptors
- KwdEn :
- Chromosome Mapping, Cluster Analysis, Computational Biology, Computer Simulation, Databases, Genetic, Gastrointestinal Microbiome (genetics), Gastrointestinal Tract (microbiology), Humans, Metagenome, Metagenomics (methods), Models, Molecular, Polymorphism, Single Nucleotide, Sequence Analysis, DNA.
- MESH :
- genetics : Gastrointestinal Microbiome.
- methods : Metagenomics.
- microbiology : Gastrointestinal Tract.
- Chromosome Mapping, Cluster Analysis, Computational Biology, Computer Simulation, Databases, Genetic, Humans, Metagenome, Models, Molecular, Polymorphism, Single Nucleotide, Sequence Analysis, DNA.
Abstract
A rapidly increasing flow of genomic data requires the development of efficient methods for obtaining its compact representation. Feature extraction facilitates classification, clustering and model analysis for testing and refining biological hypotheses. "Shotgun" metagenome is an analytically challenging type of genomic data - containing sequences of all genes from the totality of a complex microbial community. Recently, researchers started to analyze metagenomes using reference-free methods based on the analysis of oligonucleotides (k-mers) frequency spectrum previously applied to isolated genomes. However, little is known about their correlation with the existing approaches for metagenomic feature extraction, as well as the limits of applicability. Here we evaluated a metagenomic pairwise dissimilarity measure based on short k-mer spectrum using the example of human gut microbiota, a biomedically significant object of study.
DOI: 10.1186/s12859-015-0875-7
PubMed: 26774270
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Ischenko</name><affiliation wicri:level="1"><nlm:affiliation>Research Institute of Physico-Chemical Medicine, Malaya Pirogovskaya, Moscow, 119435, Russia. ischenko.dmitry@gmail.com.</nlm:affiliation><country xml:lang="fr">Russie</country><wicri:regionArea>Research Institute of Physico-Chemical Medicine, Malaya Pirogovskaya, Moscow, 119435</wicri:regionArea></affiliation></author><author><name sortKey="Ulyantsev, Vladimir I" sort="Ulyantsev, Vladimir I" uniqKey="Ulyantsev V" first="Vladimir I" last="Ulyantsev">Vladimir I. Ulyantsev</name><affiliation wicri:level="1"><nlm:affiliation>ITMO University, Kronverksky Pr., St. Petersburg, 197101, Russia. vl.ulyantsev@gmail.com.</nlm:affiliation><country xml:lang="fr">Russie</country><wicri:regionArea>ITMO University, Kronverksky Pr., St. Petersburg, 197101</wicri:regionArea></affiliation></author><author><name sortKey="Tyakht, Alexander V" sort="Tyakht, Alexander V" uniqKey="Tyakht A" first="Alexander V" last="Tyakht">Alexander V. 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Alexeev</name><affiliation wicri:level="1"><nlm:affiliation>Research Institute of Physico-Chemical Medicine, Malaya Pirogovskaya, Moscow, 119435, Russia. alexeev@niifhm.ru.</nlm:affiliation><country xml:lang="fr">Russie</country><wicri:regionArea>Research Institute of Physico-Chemical Medicine, Malaya Pirogovskaya, Moscow, 119435</wicri:regionArea></affiliation></author></analytic><series><title level="j">BMC bioinformatics</title><idno type="eISSN">1471-2105</idno><imprint><date when="2016" type="published">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Chromosome Mapping</term><term>Cluster Analysis</term><term>Computational Biology</term><term>Computer Simulation</term><term>Databases, Genetic</term><term>Gastrointestinal Microbiome (genetics)</term><term>Gastrointestinal Tract (microbiology)</term><term>Humans</term><term>Metagenome</term><term>Metagenomics (methods)</term><term>Models, Molecular</term><term>Polymorphism, Single Nucleotide</term><term>Sequence Analysis, DNA</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Analyse de regroupements</term><term>Analyse de séquence d'ADN</term><term>Bases de données génétiques</term><term>Biologie informatique</term><term>Cartographie chromosomique</term><term>Humains</term><term>Microbiome gastro-intestinal (génétique)</term><term>Modèles moléculaires</term><term>Métagénome</term><term>Métagénomique ()</term><term>Polymorphisme de nucléotide simple</term><term>Simulation numérique</term><term>Tube digestif (microbiologie)</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Gastrointestinal Microbiome</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Microbiome gastro-intestinal</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Metagenomics</term></keywords><keywords scheme="MESH" qualifier="microbiologie" xml:lang="fr"><term>Tube digestif</term></keywords><keywords scheme="MESH" qualifier="microbiology" xml:lang="en"><term>Gastrointestinal Tract</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Chromosome Mapping</term><term>Cluster Analysis</term><term>Computational Biology</term><term>Computer Simulation</term><term>Databases, Genetic</term><term>Humans</term><term>Metagenome</term><term>Models, Molecular</term><term>Polymorphism, Single Nucleotide</term><term>Sequence Analysis, DNA</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Analyse de regroupements</term><term>Analyse de séquence d'ADN</term><term>Bases de données génétiques</term><term>Biologie informatique</term><term>Cartographie chromosomique</term><term>Humains</term><term>Modèles moléculaires</term><term>Métagénome</term><term>Métagénomique</term><term>Polymorphisme de nucléotide simple</term><term>Simulation numérique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">A rapidly increasing flow of genomic data requires the development of efficient methods for obtaining its compact representation. Feature extraction facilitates classification, clustering and model analysis for testing and refining biological hypotheses. "Shotgun" metagenome is an analytically challenging type of genomic data - containing sequences of all genes from the totality of a complex microbial community. Recently, researchers started to analyze metagenomes using reference-free methods based on the analysis of oligonucleotides (k-mers) frequency spectrum previously applied to isolated genomes. However, little is known about their correlation with the existing approaches for metagenomic feature extraction, as well as the limits of applicability. Here we evaluated a metagenomic pairwise dissimilarity measure based on short k-mer spectrum using the example of human gut microbiota, a biomedically significant object of study.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">26774270</PMID><DateCompleted><Year>2016</Year><Month>08</Month><Day>18</Day></DateCompleted><DateRevised><Year>2018</Year><Month>12</Month><Day>02</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2105</ISSN><JournalIssue CitedMedium="Internet"><Volume>17</Volume><PubDate><Year>2016</Year><Month>Jan</Month><Day>16</Day></PubDate></JournalIssue><Title>BMC bioinformatics</Title><ISOAbbreviation>BMC Bioinformatics</ISOAbbreviation></Journal><ArticleTitle>Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis.</ArticleTitle><Pagination><MedlinePgn>38</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s12859-015-0875-7</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">A rapidly increasing flow of genomic data requires the development of efficient methods for obtaining its compact representation. Feature extraction facilitates classification, clustering and model analysis for testing and refining biological hypotheses. "Shotgun" metagenome is an analytically challenging type of genomic data - containing sequences of all genes from the totality of a complex microbial community. Recently, researchers started to analyze metagenomes using reference-free methods based on the analysis of oligonucleotides (k-mers) frequency spectrum previously applied to isolated genomes. However, little is known about their correlation with the existing approaches for metagenomic feature extraction, as well as the limits of applicability. Here we evaluated a metagenomic pairwise dissimilarity measure based on short k-mer spectrum using the example of human gut microbiota, a biomedically significant object of study.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">We developed a method for calculating pairwise dissimilarity (beta-diversity) of "shotgun" metagenomes based on short k-mer spectra (5 ≤ k ≤ 11). The method was validated on simulated metagenomes and further applied to a large collection of human gut metagenomes from the populations of the world (n=281). The k-mer spectrum-based measure was found to behave similarly to one based on mapping to a reference gene catalog, but different from one using a genome catalog. This difference turned out to be associated with a significant presence of viral reads in a number of metagenomes. Simulations showed limited impact of bacterial genetic variability as well as sequencing errors on k-mer spectra. Specific differences between the datasets from individual populations were identified.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">Our approach allows rapid estimation of pairwise dissimilarity between metagenomes. Though we applied this technique to gut microbiota, it should be useful for arbitrary metagenomes, even metagenomes with novel microbiota. Dissimilarity measure based on k-mer spectrum provides a wider perspective in comparison with the ones based on the alignment against reference sequence sets. It helps not to miss possible outstanding features of metagenomic composition, particularly related to the presence of an unknown bacteria, virus or eukaryote, as well as to technical artifacts (sample contamination, reads of non-biological origin, etc.) at the early stages of bioinformatic analysis. Our method is complementary to reference-based approaches and can be easily integrated into metagenomic analysis pipelines.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Dubinkina</LastName><ForeName>Veronika B</ForeName><Initials>VB</Initials><AffiliationInfo><Affiliation>Research Institute of Physico-Chemical Medicine, Malaya Pirogovskaya, Moscow, 119435, Russia. dubinkina@phystech.edu.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Moscow Institute of Physics and Technology (State University), Institutskiy per., Dolgoprudny, 141700, Russia. dubinkina@phystech.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ischenko</LastName><ForeName>Dmitry S</ForeName><Initials>DS</Initials><AffiliationInfo><Affiliation>Research Institute of Physico-Chemical Medicine, Malaya Pirogovskaya, Moscow, 119435, Russia. ischenko.dmitry@gmail.com.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Moscow Institute of Physics and Technology (State University), Institutskiy per., Dolgoprudny, 141700, Russia. ischenko.dmitry@gmail.com.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ulyantsev</LastName><ForeName>Vladimir I</ForeName><Initials>VI</Initials><AffiliationInfo><Affiliation>ITMO University, Kronverksky Pr., St. Petersburg, 197101, Russia. vl.ulyantsev@gmail.com.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Tyakht</LastName><ForeName>Alexander V</ForeName><Initials>AV</Initials><AffiliationInfo><Affiliation>Research Institute of Physico-Chemical Medicine, Malaya Pirogovskaya, Moscow, 119435, Russia. at@niifhm.ru.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Moscow Institute of Physics and Technology (State University), Institutskiy per., Dolgoprudny, 141700, Russia. at@niifhm.ru.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Alexeev</LastName><ForeName>Dmitry G</ForeName><Initials>DG</Initials><AffiliationInfo><Affiliation>Research Institute of Physico-Chemical Medicine, Malaya Pirogovskaya, Moscow, 119435, Russia. alexeev@niifhm.ru.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Moscow Institute of Physics and Technology (State University), Institutskiy per., Dolgoprudny, 141700, Russia. alexeev@niifhm.ru.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2016</Year><Month>01</Month><Day>16</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMC Bioinformatics</MedlineTA><NlmUniqueID>100965194</NlmUniqueID><ISSNLinking>1471-2105</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D002874" MajorTopicYN="N">Chromosome Mapping</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016000" MajorTopicYN="N">Cluster Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D019295" MajorTopicYN="N">Computational Biology</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003198" MajorTopicYN="N">Computer Simulation</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D030541" MajorTopicYN="N">Databases, Genetic</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000069196" MajorTopicYN="N">Gastrointestinal Microbiome</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D041981" MajorTopicYN="N">Gastrointestinal Tract</DescriptorName><QualifierName UI="Q000382" MajorTopicYN="N">microbiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D054892" MajorTopicYN="Y">Metagenome</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D056186" MajorTopicYN="N">Metagenomics</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D008958" MajorTopicYN="N">Models, Molecular</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D020641" MajorTopicYN="N">Polymorphism, Single Nucleotide</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017422" MajorTopicYN="N">Sequence Analysis, DNA</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2015</Year><Month>09</Month><Day>07</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2015</Year><Month>12</Month><Day>14</Day></PubMedPubDate><PubMedPubDate 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