Exploiting topic modeling to boost metagenomic reads binning.
Identifieur interne : 001552 ( PubMed/Checkpoint ); précédent : 001551; suivant : 001553Exploiting topic modeling to boost metagenomic reads binning.
Auteurs : Ruichang Zhang ; Zhanzhan Cheng ; Jihong Guan ; Shuigeng ZhouSource :
- BMC bioinformatics [ 1471-2105 ] ; 2015.
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Abstract
With the rapid development of high-throughput technologies, researchers can sequence the whole metagenome of a microbial community sampled directly from the environment. The assignment of these metagenomic reads into different species or taxonomical classes is a vital step for metagenomic analysis, which is referred to as binning of metagenomic data.
DOI: 10.1186/1471-2105-16-S5-S2
PubMed: 25859745
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<author><name sortKey="Guan, Jihong" sort="Guan, Jihong" uniqKey="Guan J" first="Jihong" last="Guan">Jihong Guan</name>
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<author><name sortKey="Zhou, Shuigeng" sort="Zhou, Shuigeng" uniqKey="Zhou S" first="Shuigeng" last="Zhou">Shuigeng Zhou</name>
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<term>Cluster Analysis</term>
<term>DNA Barcoding, Taxonomic (methods)</term>
<term>Genome, Bacterial</term>
<term>High-Throughput Nucleotide Sequencing</term>
<term>Metagenome</term>
<term>Metagenomics (methods)</term>
<term>Microbiota (genetics)</term>
<term>Molecular Sequence Annotation (methods)</term>
<term>Phylogeny</term>
<term>Sequence Analysis, DNA (methods)</term>
<term>Software</term>
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<keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term>
<term>Analyse de regroupements</term>
<term>Analyse de séquence d'ADN ()</term>
<term>Annotation de séquence moléculaire ()</term>
<term>Codage à barres de l'ADN pour la taxonomie ()</term>
<term>Génome bactérien</term>
<term>Logiciel</term>
<term>Microbiote (génétique)</term>
<term>Métagénome</term>
<term>Métagénomique ()</term>
<term>Phylogénie</term>
<term>Séquençage nucléotidique à haut débit</term>
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<keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Microbiota</term>
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<keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Microbiote</term>
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<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>DNA Barcoding, Taxonomic</term>
<term>Metagenomics</term>
<term>Molecular Sequence Annotation</term>
<term>Sequence Analysis, DNA</term>
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<keywords scheme="MESH" xml:lang="en"><term>Algorithms</term>
<term>Cluster Analysis</term>
<term>Genome, Bacterial</term>
<term>High-Throughput Nucleotide Sequencing</term>
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<term>Analyse de regroupements</term>
<term>Analyse de séquence d'ADN</term>
<term>Annotation de séquence moléculaire</term>
<term>Codage à barres de l'ADN pour la taxonomie</term>
<term>Génome bactérien</term>
<term>Logiciel</term>
<term>Métagénome</term>
<term>Métagénomique</term>
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<front><div type="abstract" xml:lang="en">With the rapid development of high-throughput technologies, researchers can sequence the whole metagenome of a microbial community sampled directly from the environment. The assignment of these metagenomic reads into different species or taxonomical classes is a vital step for metagenomic analysis, which is referred to as binning of metagenomic data.</div>
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<Title>BMC bioinformatics</Title>
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<ArticleTitle>Exploiting topic modeling to boost metagenomic reads binning.</ArticleTitle>
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<Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">With the rapid development of high-throughput technologies, researchers can sequence the whole metagenome of a microbial community sampled directly from the environment. The assignment of these metagenomic reads into different species or taxonomical classes is a vital step for metagenomic analysis, which is referred to as binning of metagenomic data.</AbstractText>
<AbstractText Label="RESULTS" NlmCategory="RESULTS">In this paper, we propose a new method TM-MCluster for binning metagenomic reads. First, we represent each metagenomic read as a set of "k-mers" with their frequencies occurring in the read. Then, we employ a probabilistic topic model -- the Latent Dirichlet Allocation (LDA) model to the reads, which generates a number of hidden "topics" such that each read can be represented by a distribution vector of the generated topics. Finally, as in the MCluster method, we apply SKWIC -- a variant of the classical K-means algorithm with automatic feature weighting mechanism to cluster these reads represented by topic distributions.</AbstractText>
<AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">Experiments show that the new method TM-MCluster outperforms major existing methods, including AbundanceBin, MetaCluster 3.0/5.0 and MCluster. This result indicates that the exploitation of topic modeling can effectively improve the binning performance of metagenomic reads.</AbstractText>
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