Exploiting topic modeling to boost metagenomic reads binning
Identifieur interne : 001797 ( Main/Exploration ); précédent : 001796; suivant : 001798Exploiting topic modeling to boost metagenomic reads binning
Auteurs : Ruichang Zhang [République populaire de Chine] ; Zhanzhan Cheng [République populaire de Chine] ; Jihong Guan [République populaire de Chine] ; Shuigeng Zhou [République populaire de Chine]Source :
- BMC Bioinformatics [ 1471-2105 ] ; 2015.
Descripteurs français
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English descriptors
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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
In this paper, we propose a new method
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.
Url:
DOI: 10.1186/1471-2105-16-S5-S2
PubMed: 25859745
PubMed Central: 4402587
Affiliations:
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Le document en format XML
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<term>High-Throughput Nucleotide Sequencing</term>
<term>Metagenome</term>
<term>Metagenomics (methods)</term>
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<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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<term>Metagenomics</term>
<term>Molecular Sequence Annotation</term>
<term>Sequence Analysis, DNA</term>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>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 <italic>binning </italic>
of metagenomic data.</p>
</sec>
<sec><title>Results</title>
<p>In this paper, we propose a new method <italic>TM-MCluster </italic>
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.</p>
</sec>
<sec><title>Conclusions</title>
<p>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.</p>
</sec>
</div>
</front>
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