Article: A robust and accurate binning algorithm for metagenomic sequences with arbitrary species abundance ratio

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TitleA robust and accurate binning algorithm for metagenomic sequences with arbitrary species abundance ratio
AuthorsLeung, HCM1
Yiu, SM1
Yang, B1
Peng, Y1
Wang, Y1
Liu, Z1
Chen, J1
Qin, J1
Li, R
Chin, FYL1
Issue Date2011
PublisherOxford University Press. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/
CitationBioinformatics, 2011, v. 27 n. 11, p. 1489-1495 [How to Cite?]
DOI: http://dx.doi.org/10.1093/bioinformatics/btr186
AbstractMotivation: With the rapid development of next-generation sequencing techniques, metagenomics, also known as environmental genomics, has emerged as an exciting research area that enables us to analyze the microbial environment in which we live. An important step for metagenomic data analysis is the identification and taxonomic characterization of DNA fragments (reads or contigs) resulting from sequencing a sample of mixed species. This step is referred to as 'binning'. Binning algorithms that are based on sequence similarity and sequence composition markers rely heavily on the reference genomes of known microorganisms or phylogenetic markers. Due to the limited availability of reference genomes and the bias and low availability of markers, these algorithms may not be applicable in all cases. Unsupervised binning algorithms which can handle fragments from unknown species provide an alternative approach. However, existing unsupervised binning algorithms only work on datasets either with balanced species abundance ratios or rather different abundance ratios, but not both. Results: In this article, we present MetaCluster 3.0, an integrated binning method based on the unsupervised top-down separation and bottom-up merging strategy, which can bin metagenomic fragments of species with very balanced abundance ratios (say 1:1) to very different abundance ratios (e.g. 1:24) with consistently higher accuracy than existing methods. © The Author 2011. Published by Oxford University Press. All rights reserved.
ISSN1367-4803
2011 Impact Factor: 5.468
2011 SCImago Journal Rankings: 1.118
DOIhttp://dx.doi.org/10.1093/bioinformatics/btr186
ReferencesReferences in Scopus
GrantsAlgorithms for Inferring k-articulated Phylogenetic Network
DC Field
Value
dc.contributor.authorLeung, HCM
dc.contributor.authorYiu, SM
dc.contributor.authorYang, B
dc.contributor.authorPeng, Y
dc.contributor.authorWang, Y
dc.contributor.authorLiu, Z
dc.contributor.authorChen, J
dc.contributor.authorQin, J
dc.contributor.authorLi, R
dc.contributor.authorChin, FYL
dc.date.accessioned2011-09-23T06:19:25Z
dc.date.available2011-09-23T06:19:25Z
dc.date.issued2011
dc.description.abstractMotivation: With the rapid development of next-generation sequencing techniques, metagenomics, also known as environmental genomics, has emerged as an exciting research area that enables us to analyze the microbial environment in which we live. An important step for metagenomic data analysis is the identification and taxonomic characterization of DNA fragments (reads or contigs) resulting from sequencing a sample of mixed species. This step is referred to as 'binning'. Binning algorithms that are based on sequence similarity and sequence composition markers rely heavily on the reference genomes of known microorganisms or phylogenetic markers. Due to the limited availability of reference genomes and the bias and low availability of markers, these algorithms may not be applicable in all cases. Unsupervised binning algorithms which can handle fragments from unknown species provide an alternative approach. However, existing unsupervised binning algorithms only work on datasets either with balanced species abundance ratios or rather different abundance ratios, but not both. Results: In this article, we present MetaCluster 3.0, an integrated binning method based on the unsupervised top-down separation and bottom-up merging strategy, which can bin metagenomic fragments of species with very balanced abundance ratios (say 1:1) to very different abundance ratios (e.g. 1:24) with consistently higher accuracy than existing methods. © The Author 2011. Published by Oxford University Press. All rights reserved.
dc.description.grantAlgorithms for Inferring k-articulated Phylogenetic Network
dc.description.grantcode100580
dc.description.naturepostprint
dc.identifier.citationBioinformatics, 2011, v. 27 n. 11, p. 1489-1495 [How to Cite?]
DOI: http://dx.doi.org/10.1093/bioinformatics/btr186
dc.identifier.citeulike9157005
dc.identifier.doihttp://dx.doi.org/10.1093/bioinformatics/btr186
dc.identifier.epage1495
dc.identifier.hkuros192228
dc.identifier.isiWOS:000291062400007
Funding AgencyGrant Number
GRFHKU 719709E
HKU 711611
Funding Information:

GRF grant (HKU 719709E, HKU 711611 and HKU SPACE Research Fund) in part.

dc.identifier.issn1367-4803
2011 Impact Factor: 5.468
2011 SCImago Journal Rankings: 1.118
dc.identifier.issue11
dc.identifier.openurl
dc.identifier.pmid21493653
dc.identifier.scopuseid_2-s2.0-79957877228
dc.identifier.spage1489
dc.identifier.urihttp://hdl.handle.net/10722/140792
dc.identifier.volume27
dc.languageeng
dc.publisherOxford University Press. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/
dc.publisher.placeUnited Kingdom
dc.relation.ispartofBioinformatics
dc.relation.referencesReferences in Scopus
dc.rightsThis is a pre-copy-editing, author-produced PDF of an article accepted for publication in Bioinformatics following peer review. The definitive publisher-authenticated version Bioinformatics, 2011, v. 27 n. 11, p. 1489-1495 is available online at: http://bioinformatics.oxfordjournals.org/content/27/11/1489
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License
dc.subject.meshAlgorithms
dc.subject.meshCluster Analysis
dc.subject.meshMetagenomics - methods
dc.subject.meshSequence Analysis, DNA
dc.titleA robust and accurate binning algorithm for metagenomic sequences with arbitrary species abundance ratio
dc.typeArticle
Author Affiliations
  1. The University of Hong Kong