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Exploiting topic modeling to boost metagenomic reads binning

Identifieur interne : 001802 ( Main/Merge ); précédent : 001801; suivant : 001803

Exploiting 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 :

RBID : PMC:4402587

Descripteurs français

English descriptors

Abstract

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.

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.

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.


Url:
DOI: 10.1186/1471-2105-16-S5-S2
PubMed: 25859745
PubMed Central: 4402587

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Links to Exploration step

PMC:4402587

Le document en format XML

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<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
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<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>
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<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>
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