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

TitleA robust and accurate binning algorithm for metagenomic sequences with arbitrary species abundance ratio
Authors
Issue Date2011
PublisherOxford University Press. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/
Citation
Bioinformatics, 2011, v. 27 n. 11, p. 1489-1495 How to Cite?
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.
Persistent Identifierhttp://hdl.handle.net/10722/140792
ISSN
2014 Impact Factor: 4.981
ISI Accession Number ID
Funding AgencyGrant Number
GRFHKU 719709E
HKU 711611
Funding Information:

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

References
Grants

 

DC FieldValueLanguage
dc.contributor.authorLeung, HCMen_HK
dc.contributor.authorYiu, SMen_HK
dc.contributor.authorYang, Ben_HK
dc.contributor.authorPeng, Yen_HK
dc.contributor.authorWang, Yen_HK
dc.contributor.authorLiu, Zen_HK
dc.contributor.authorChen, Jen_HK
dc.contributor.authorQin, Jen_HK
dc.contributor.authorLi, Ren_HK
dc.contributor.authorChin, FYLen_HK
dc.date.accessioned2011-09-23T06:19:25Z-
dc.date.available2011-09-23T06:19:25Z-
dc.date.issued2011en_HK
dc.identifier.citationBioinformatics, 2011, v. 27 n. 11, p. 1489-1495en_HK
dc.identifier.issn1367-4803en_HK
dc.identifier.urihttp://hdl.handle.net/10722/140792-
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.en_HK
dc.languageengen_US
dc.publisherOxford University Press. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/en_HK
dc.relation.ispartofBioinformaticsen_HK
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 ratioen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1367-4803&volume=27&issue=11&spage=1489&epage=1495&date=2011&atitle=A+robust+and+accurate+binning+algorithm+for+metagenomic+sequences+with+arbitrary+species+abundance+ratio-
dc.identifier.emailLeung, HCM:cmleung2@cs.hku.hken_HK
dc.identifier.emailYiu, SM:smyiu@cs.hku.hken_HK
dc.identifier.emailChin, FYL:chin@cs.hku.hken_HK
dc.identifier.authorityLeung, HCM=rp00144en_HK
dc.identifier.authorityYiu, SM=rp00207en_HK
dc.identifier.authorityChin, FYL=rp00105en_HK
dc.description.naturepostprint-
dc.identifier.doi10.1093/bioinformatics/btr186en_HK
dc.identifier.pmid21493653-
dc.identifier.scopuseid_2-s2.0-79957877228en_HK
dc.identifier.hkuros192228en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79957877228&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume27en_HK
dc.identifier.issue11en_HK
dc.identifier.spage1489en_HK
dc.identifier.epage1495en_HK
dc.identifier.eissn1460-2059-
dc.identifier.isiWOS:000291062400007-
dc.publisher.placeUnited Kingdomen_HK
dc.relation.projectAlgorithms for Inferring k-articulated Phylogenetic Network-
dc.identifier.scopusauthoridLeung, HCM=35233742700en_HK
dc.identifier.scopusauthoridYiu, SM=7003282240en_HK
dc.identifier.scopusauthoridYang, B=54394737300en_HK
dc.identifier.scopusauthoridPeng, Y=54393903900en_HK
dc.identifier.scopusauthoridWang, Y=54394522700en_HK
dc.identifier.scopusauthoridLiu, Z=54393630900en_HK
dc.identifier.scopusauthoridChen, J=54392639400en_HK
dc.identifier.scopusauthoridQin, J=14039564900en_HK
dc.identifier.scopusauthoridLi, R=34975581600en_HK
dc.identifier.scopusauthoridChin, FYL=7005101915en_HK
dc.identifier.citeulike9157005-

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