MersV1, PubMed, Corpus, bibRecord, 001382

Large-scale machine learning for metagenomics sequence classification.

Identifieur interne : 001382 ( PubMed/Corpus ); précédent : 001381; suivant : 001383

Large-scale machine learning for metagenomics sequence classification.

Auteurs : Kévin Vervier ; Pierre Mahé ; Maud Tournoud ; Jean-Baptiste Veyrieras ; Jean-Philippe Vert

Source :

Bioinformatics (Oxford, England) [ 1367-4811 ] ; 2016.

RBID : pubmed:26589281

English descriptors

KwdEn :
- Algorithms, Machine Learning, Metagenome, Metagenomics, Sequence Analysis, DNA, Software.
MESH :
- Algorithms, Machine Learning, Metagenome, Metagenomics, Sequence Analysis, DNA, Software.

Abstract

Metagenomics characterizes the taxonomic diversity of microbial communities by sequencing DNA directly from an environmental sample. One of the main challenges in metagenomics data analysis is the binning step, where each sequenced read is assigned to a taxonomic clade. Because of the large volume of metagenomics datasets, binning methods need fast and accurate algorithms that can operate with reasonable computing requirements. While standard alignment-based methods provide state-of-the-art performance, compositional approaches that assign a taxonomic class to a DNA read based on the k-mers it contains have the potential to provide faster solutions.

DOI: 10.1093/bioinformatics/btv683
PubMed: 26589281

Links to Exploration step

pubmed:26589281

Le document en format XML

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<author><name sortKey="Vervier, Kevin" sort="Vervier, Kevin" uniqKey="Vervier K" first="Kévin" last="Vervier">Kévin Vervier</name>
<affiliation><nlm:affiliation>Bioinformatics Research Departement, bioMérieux, 69280 Marcy-l'Étoile, MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, 77300 Fontainebleau, Institut Curie, 75248 Paris Cedex and INSERM U900, 75248 Paris Cedex, France.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Mahe, Pierre" sort="Mahe, Pierre" uniqKey="Mahe P" first="Pierre" last="Mahé">Pierre Mahé</name>
<affiliation><nlm:affiliation>Bioinformatics Research Departement, bioMérieux, 69280 Marcy-l'Étoile.</nlm:affiliation>
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</author>
<author><name sortKey="Tournoud, Maud" sort="Tournoud, Maud" uniqKey="Tournoud M" first="Maud" last="Tournoud">Maud Tournoud</name>
<affiliation><nlm:affiliation>Bioinformatics Research Departement, bioMérieux, 69280 Marcy-l'Étoile.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Veyrieras, Jean Baptiste" sort="Veyrieras, Jean Baptiste" uniqKey="Veyrieras J" first="Jean-Baptiste" last="Veyrieras">Jean-Baptiste Veyrieras</name>
<affiliation><nlm:affiliation>Bioinformatics Research Departement, bioMérieux, 69280 Marcy-l'Étoile.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Vert, Jean Philippe" sort="Vert, Jean Philippe" uniqKey="Vert J" first="Jean-Philippe" last="Vert">Jean-Philippe Vert</name>
<affiliation><nlm:affiliation>MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, 77300 Fontainebleau, Institut Curie, 75248 Paris Cedex and INSERM U900, 75248 Paris Cedex, France.</nlm:affiliation>
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<author><name sortKey="Vervier, Kevin" sort="Vervier, Kevin" uniqKey="Vervier K" first="Kévin" last="Vervier">Kévin Vervier</name>
<affiliation><nlm:affiliation>Bioinformatics Research Departement, bioMérieux, 69280 Marcy-l'Étoile, MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, 77300 Fontainebleau, Institut Curie, 75248 Paris Cedex and INSERM U900, 75248 Paris Cedex, France.</nlm:affiliation>
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<author><name sortKey="Mahe, Pierre" sort="Mahe, Pierre" uniqKey="Mahe P" first="Pierre" last="Mahé">Pierre Mahé</name>
<affiliation><nlm:affiliation>Bioinformatics Research Departement, bioMérieux, 69280 Marcy-l'Étoile.</nlm:affiliation>
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<author><name sortKey="Tournoud, Maud" sort="Tournoud, Maud" uniqKey="Tournoud M" first="Maud" last="Tournoud">Maud Tournoud</name>
<affiliation><nlm:affiliation>Bioinformatics Research Departement, bioMérieux, 69280 Marcy-l'Étoile.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Veyrieras, Jean Baptiste" sort="Veyrieras, Jean Baptiste" uniqKey="Veyrieras J" first="Jean-Baptiste" last="Veyrieras">Jean-Baptiste Veyrieras</name>
<affiliation><nlm:affiliation>Bioinformatics Research Departement, bioMérieux, 69280 Marcy-l'Étoile.</nlm:affiliation>
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<author><name sortKey="Vert, Jean Philippe" sort="Vert, Jean Philippe" uniqKey="Vert J" first="Jean-Philippe" last="Vert">Jean-Philippe Vert</name>
<affiliation><nlm:affiliation>MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, 77300 Fontainebleau, Institut Curie, 75248 Paris Cedex and INSERM U900, 75248 Paris Cedex, France.</nlm:affiliation>
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<series><title level="j">Bioinformatics (Oxford, England)</title>
<idno type="eISSN">1367-4811</idno>
<imprint><date when="2016" type="published">2016</date>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term>
<term>Machine Learning</term>
<term>Metagenome</term>
<term>Metagenomics</term>
<term>Sequence Analysis, DNA</term>
<term>Software</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Algorithms</term>
<term>Machine Learning</term>
<term>Metagenome</term>
<term>Metagenomics</term>
<term>Sequence Analysis, DNA</term>
<term>Software</term>
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<front><div type="abstract" xml:lang="en">Metagenomics characterizes the taxonomic diversity of microbial communities by sequencing DNA directly from an environmental sample. One of the main challenges in metagenomics data analysis is the binning step, where each sequenced read is assigned to a taxonomic clade. Because of the large volume of metagenomics datasets, binning methods need fast and accurate algorithms that can operate with reasonable computing requirements. While standard alignment-based methods provide state-of-the-art performance, compositional approaches that assign a taxonomic class to a DNA read based on the k-mers it contains have the potential to provide faster solutions.</div>
</front>
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<pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">26589281</PMID>
<DateCompleted><Year>2017</Year>
<Month>08</Month>
<Day>18</Day>
</DateCompleted>
<DateRevised><Year>2018</Year>
<Month>12</Month>
<Day>02</Day>
</DateRevised>
<Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1367-4811</ISSN>
<JournalIssue CitedMedium="Internet"><Volume>32</Volume>
<Issue>7</Issue>
<PubDate><Year>2016</Year>
<Month>04</Month>
<Day>01</Day>
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<Title>Bioinformatics (Oxford, England)</Title>
<ISOAbbreviation>Bioinformatics</ISOAbbreviation>
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<ArticleTitle>Large-scale machine learning for metagenomics sequence classification.</ArticleTitle>
<Pagination><MedlinePgn>1023-32</MedlinePgn>
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<ELocationID EIdType="doi" ValidYN="Y">10.1093/bioinformatics/btv683</ELocationID>
<Abstract><AbstractText Label="MOTIVATION">Metagenomics characterizes the taxonomic diversity of microbial communities by sequencing DNA directly from an environmental sample. One of the main challenges in metagenomics data analysis is the binning step, where each sequenced read is assigned to a taxonomic clade. Because of the large volume of metagenomics datasets, binning methods need fast and accurate algorithms that can operate with reasonable computing requirements. While standard alignment-based methods provide state-of-the-art performance, compositional approaches that assign a taxonomic class to a DNA read based on the k-mers it contains have the potential to provide faster solutions.</AbstractText>
<AbstractText Label="RESULTS">We propose a new rank-flexible machine learning-based compositional approach for taxonomic assignment of metagenomics reads and show that it benefits from increasing the number of fragments sampled from reference genome to tune its parameters, up to a coverage of about 10, and from increasing the k-mer size to about 12. Tuning the method involves training machine learning models on about 10(8) samples in 10(7) dimensions, which is out of reach of standard softwares but can be done efficiently with modern implementations for large-scale machine learning. The resulting method is competitive in terms of accuracy with well-established alignment and composition-based tools for problems involving a small to moderate number of candidate species and for reasonable amounts of sequencing errors. We show, however, that machine learning-based compositional approaches are still limited in their ability to deal with problems involving a greater number of species and more sensitive to sequencing errors. We finally show that the new method outperforms the state-of-the-art in its ability to classify reads from species of lineage absent from the reference database and confirm that compositional approaches achieve faster prediction times, with a gain of 2-17 times with respect to the BWA-MEM short read mapper, depending on the number of candidate species and the level of sequencing noise.</AbstractText>
<AbstractText Label="AVAILABILITY AND IMPLEMENTATION">Data and codes are available at http://cbio.ensmp.fr/largescalemetagenomics</AbstractText>
<AbstractText Label="CONTACT">pierre.mahe@biomerieux.com</AbstractText>
<AbstractText Label="SUPPLEMENTARY INFORMATION">Supplementary data are available at Bioinformatics online.</AbstractText>
<CopyrightInformation>© The Author 2015. Published by Oxford University Press.</CopyrightInformation>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Vervier</LastName>
<ForeName>Kévin</ForeName>
<Initials>K</Initials>
<AffiliationInfo><Affiliation>Bioinformatics Research Departement, bioMérieux, 69280 Marcy-l'Étoile, MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, 77300 Fontainebleau, Institut Curie, 75248 Paris Cedex and INSERM U900, 75248 Paris Cedex, France.</Affiliation>
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<Author ValidYN="Y"><LastName>Mahé</LastName>
<ForeName>Pierre</ForeName>
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<AffiliationInfo><Affiliation>Bioinformatics Research Departement, bioMérieux, 69280 Marcy-l'Étoile.</Affiliation>
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<Author ValidYN="Y"><LastName>Tournoud</LastName>
<ForeName>Maud</ForeName>
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<AffiliationInfo><Affiliation>Bioinformatics Research Departement, bioMérieux, 69280 Marcy-l'Étoile.</Affiliation>
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</Author>
<Author ValidYN="Y"><LastName>Veyrieras</LastName>
<ForeName>Jean-Baptiste</ForeName>
<Initials>JB</Initials>
<AffiliationInfo><Affiliation>Bioinformatics Research Departement, bioMérieux, 69280 Marcy-l'Étoile.</Affiliation>
</AffiliationInfo>
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<Author ValidYN="Y"><LastName>Vert</LastName>
<ForeName>Jean-Philippe</ForeName>
<Initials>JP</Initials>
<AffiliationInfo><Affiliation>MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, 77300 Fontainebleau, Institut Curie, 75248 Paris Cedex and INSERM U900, 75248 Paris Cedex, France.</Affiliation>
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<Language>eng</Language>
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<MeshHeading><DescriptorName UI="D000069550" MajorTopicYN="Y">Machine Learning</DescriptorName>
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<MeshHeading><DescriptorName UI="D054892" MajorTopicYN="N">Metagenome</DescriptorName>
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<MeshHeading><DescriptorName UI="D056186" MajorTopicYN="Y">Metagenomics</DescriptorName>
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<MeshHeading><DescriptorName UI="D017422" MajorTopicYN="Y">Sequence Analysis, DNA</DescriptorName>
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Large-scale machine learning for metagenomics sequence classification.

Large-scale machine learning for metagenomics sequence classification.

Source :

English descriptors

Abstract

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

Pour générer des pages wiki