title: Exploiting topic modeling to boost metagenomic reads binning.
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.
authors: Ruichang
authors: R
authors: Zhang
authors: Zhanzhan
authors: Z
authors: Cheng
authors: Jihong
authors: J
authors: Guan
authors: Shuigeng
authors: S
authors: Zhou
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D058893:DNA Barcoding, Taxonomic / Q000379:methods*; D016680:Genome, Bacterial; D059014:High-Throughput Nucleotide Sequencing; D054892:Metagenome; D056186:Metagenomics / Q000379:methods*; D064307:Microbiota / Q000235:genetics*; D058977:Molecular Sequence Annotation / Q000379:methods*; D010802:Phylogeny; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software