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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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></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">25859745</PMID><DateCompleted><Year>2015</Year><Month>07</Month><Day>28</Day></DateCompleted><DateRevised><Year>2018</Year><Month>12</Month><Day>02</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1471-2105</ISSN><JournalIssue CitedMedium="Internet"><Volume>16 Suppl 5</Volume><PubDate><Year>2015</Year></PubDate></JournalIssue><Title>BMC bioinformatics</Title><ISOAbbreviation>BMC Bioinformatics</ISOAbbreviation></Journal><ArticleTitle>Exploiting topic modeling to boost metagenomic reads binning.</ArticleTitle><Pagination><MedlinePgn>S2</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/1471-2105-16-S5-S2</ELocationID><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></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Ruichang</ForeName><Initials>R</Initials></Author><Author ValidYN="Y"><LastName>Cheng</LastName><ForeName>Zhanzhan</ForeName><Initials>Z</Initials></Author><Author ValidYN="Y"><LastName>Guan</LastName><ForeName>Jihong</ForeName><Initials>J</Initials></Author><Author ValidYN="Y"><LastName>Zhou</LastName><ForeName>Shuigeng</ForeName><Initials>S</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2015</Year><Month>03</Month><Day>18</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMC Bioinformatics</MedlineTA><NlmUniqueID>100965194</NlmUniqueID><ISSNLinking>1471-2105</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="Y">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016000" MajorTopicYN="N">Cluster Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058893" MajorTopicYN="N">DNA Barcoding, Taxonomic</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D016680" MajorTopicYN="N">Genome, Bacterial</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D059014" MajorTopicYN="N">High-Throughput Nucleotide Sequencing</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D054892" MajorTopicYN="Y">Metagenome</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D056186" MajorTopicYN="N">Metagenomics</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D064307" MajorTopicYN="N">Microbiota</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D058977" MajorTopicYN="N">Molecular Sequence Annotation</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D010802" MajorTopicYN="N">Phylogeny</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017422" MajorTopicYN="N">Sequence Analysis, DNA</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012984" MajorTopicYN="N">Software</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2015</Year><Month>4</Month><Day>11</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate 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