MetaVW: Large-Scale Machine Learning for Metagenomics Sequence Classification.
Identifieur interne : 000035 ( France/Analysis ); précédent : 000034; suivant : 000036MetaVW: Large-Scale Machine Learning for Metagenomics Sequence Classification.
Auteurs : Kévin Vervier [États-Unis] ; Pierre Mahé [France] ; Jean-Philippe Vert [France]Source :
- Methods in molecular biology (Clifton, N.J.) [ 1940-6029 ] ; 2018.
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Abstract
Metagenomics is the study of microbial community diversity, especially the uncultured microorganisms by shotgun sequencing environmental samples. As the sequencers throughput and the data volume increase, it becomes challenging to develop scalable bioinformatics tools that reconstruct microbiome structure by binning sequencing reads to reference genomes. Standard alignment-based methods, such as BWA-MEM, provide state-of-the-art performance, but we demonstrate in Vervier et al. (2016) that compositional approaches using nucleotides motifs have faster analysis time, for comparable accuracy. In this work, we describe how to use MetaVW, a scalable machine learning implementation for short sequencing reads binning, based on their k-mers profile. We provide a step-by-step guideline on how we trained the classification models and how it can easily generalize to user-defined reference genomes and specific applications. We also give additional details on what effect parameters in the algorithm have on performances.
DOI: 10.1007/978-1-4939-8561-6_2
PubMed: 30030800
Affiliations:
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pubmed:30030800Le document en format XML
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Jean-Philippe.Vert@mines-paristech.fr.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Fontainebleau</wicri:regionArea><placeName><region type="region">Île-de-France</region><region type="old region">Île-de-France</region><settlement type="city">Fontainebleau</settlement></placeName></affiliation></author></analytic><series><title level="j">Methods in molecular biology (Clifton, N.J.)</title><idno type="eISSN">1940-6029</idno><imprint><date when="2018" type="published">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Base Sequence</term><term>Calibration</term><term>Genome, Bacterial</term><term>Machine Learning</term><term>Metagenomics (methods)</term><term>Reproducibility of Results</term><term>Sequence Analysis, DNA</term><term>Software</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Analyse de séquence d'ADN</term><term>Apprentissage machine</term><term>Calibrage</term><term>Génome bactérien</term><term>Logiciel</term><term>Métagénomique ()</term><term>Reproductibilité des résultats</term><term>Séquence nucléotidique</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Metagenomics</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Base Sequence</term><term>Calibration</term><term>Genome, Bacterial</term><term>Machine Learning</term><term>Reproducibility of Results</term><term>Sequence Analysis, DNA</term><term>Software</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Analyse de séquence d'ADN</term><term>Apprentissage machine</term><term>Calibrage</term><term>Génome bactérien</term><term>Logiciel</term><term>Métagénomique</term><term>Reproductibilité des résultats</term><term>Séquence nucléotidique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Metagenomics is the study of microbial community diversity, especially the uncultured microorganisms by shotgun sequencing environmental samples. As the sequencers throughput and the data volume increase, it becomes challenging to develop scalable bioinformatics tools that reconstruct microbiome structure by binning sequencing reads to reference genomes. Standard alignment-based methods, such as BWA-MEM, provide state-of-the-art performance, but we demonstrate in Vervier et al. (2016) that compositional approaches using nucleotides motifs have faster analysis time, for comparable accuracy. In this work, we describe how to use MetaVW, a scalable machine learning implementation for short sequencing reads binning, based on their k-mers profile. We provide a step-by-step guideline on how we trained the classification models and how it can easily generalize to user-defined reference genomes and specific applications. We also give additional details on what effect parameters in the algorithm have on performances.</div></front></TEI><affiliations><list><country><li>France</li><li>États-Unis</li></country><region><li>Iowa</li><li>Île-de-France</li></region><settlement><li>Fontainebleau</li></settlement></list><tree><country name="États-Unis"><region name="Iowa"><name sortKey="Vervier, Kevin" sort="Vervier, Kevin" uniqKey="Vervier K" first="Kévin" last="Vervier">Kévin Vervier</name></region></country><country name="France"><noRegion><name sortKey="Mahe, Pierre" sort="Mahe, Pierre" uniqKey="Mahe P" first="Pierre" last="Mahé">Pierre Mahé</name></noRegion><name sortKey="Vert, Jean Philippe" sort="Vert, Jean Philippe" uniqKey="Vert J" first="Jean-Philippe" last="Vert">Jean-Philippe Vert</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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