A two-phase binning algorithm using l-mer frequency on groups of non-overlapping reads.
Identifieur interne : 001709 ( PubMed/Corpus ); précédent : 001708; suivant : 001710A two-phase binning algorithm using l-mer frequency on groups of non-overlapping reads.
Auteurs : Le Van Vinh ; Tran Van Lang ; Le Thanh Binh ; Tran Van HoaiSource :
- Algorithms for molecular biology : AMB [ 1748-7188 ] ; 2015.
Abstract
Metagenomics is the study of genetic materials derived directly from complex microbial samples, instead of from culture. One of the crucial steps in metagenomic analysis, referred to as "binning", is to separate reads into clusters that represent genomes from closely related organisms. Among the existing binning methods, unsupervised methods base the classification on features extracted from reads, and especially taking advantage in case of the limitation of reference database availability. However, their performance, under various aspects, is still being investigated by recent theoretical and empirical studies. The one addressed in this paper is among those efforts to enhance the accuracy of the classification.
DOI: 10.1186/s13015-014-0030-4
PubMed: 25648210
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<author><name sortKey="Vinh, Le Van" sort="Vinh, Le Van" uniqKey="Vinh L" first="Le Van" last="Vinh">Le Van Vinh</name>
<affiliation><nlm:affiliation>Faculty of Computer Science and Engineering, HCMC University of Technology, 268 Ly Thuong Kiet, Q10, Ho Chi Minh City, Vietnam.</nlm:affiliation>
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<author><name sortKey="Lang, Tran Van" sort="Lang, Tran Van" uniqKey="Lang T" first="Tran Van" last="Lang">Tran Van Lang</name>
<affiliation><nlm:affiliation>Institute of Applied Mechanics and Informatics, Vietnam Academy of Science and Technology (VAST), 01 Mac Dinh Chi, Q1, Ho Chi Minh City, Vietnam ; Faculty of Information Technology, Lac Hong University, 10 Huynh Van Nghe, Bien Hoa, Dong Nai Vietnam.</nlm:affiliation>
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<front><div type="abstract" xml:lang="en">Metagenomics is the study of genetic materials derived directly from complex microbial samples, instead of from culture. One of the crucial steps in metagenomic analysis, referred to as "binning", is to separate reads into clusters that represent genomes from closely related organisms. Among the existing binning methods, unsupervised methods base the classification on features extracted from reads, and especially taking advantage in case of the limitation of reference database availability. However, their performance, under various aspects, is still being investigated by recent theoretical and empirical studies. The one addressed in this paper is among those efforts to enhance the accuracy of the classification.</div>
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<DateCompleted><Year>2015</Year>
<Month>02</Month>
<Day>04</Day>
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<DateRevised><Year>2018</Year>
<Month>11</Month>
<Day>13</Day>
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<JournalIssue CitedMedium="Print"><Volume>10</Volume>
<Issue>1</Issue>
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<Title>Algorithms for molecular biology : AMB</Title>
<ISOAbbreviation>Algorithms Mol Biol</ISOAbbreviation>
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<ArticleTitle>A two-phase binning algorithm using l-mer frequency on groups of non-overlapping reads.</ArticleTitle>
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<Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Metagenomics is the study of genetic materials derived directly from complex microbial samples, instead of from culture. One of the crucial steps in metagenomic analysis, referred to as "binning", is to separate reads into clusters that represent genomes from closely related organisms. Among the existing binning methods, unsupervised methods base the classification on features extracted from reads, and especially taking advantage in case of the limitation of reference database availability. However, their performance, under various aspects, is still being investigated by recent theoretical and empirical studies. The one addressed in this paper is among those efforts to enhance the accuracy of the classification.</AbstractText>
<AbstractText Label="RESULTS" NlmCategory="RESULTS">This paper presents an unsupervised algorithm, called BiMeta, for binning of reads from different species in a metagenomic dataset. The algorithm consists of two phases. In the first phase of the algorithm, reads are grouped into groups based on overlap information between the reads. The second phase merges the groups by using an observation on l-mer frequency distribution of sets of non-overlapping reads. The experimental results on simulated and real datasets showed that BiMeta outperforms three state-of-the-art binning algorithms for both short and long reads (≥700 b p) datasets.</AbstractText>
<AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">This paper developed a novel and efficient algorithm for binning of metagenomic reads, which does not require any reference database. The software implementing the algorithm and all test datasets mentioned in this paper can be downloaded at http://it.hcmute.edu.vn/bioinfo/bimeta/index.htm.</AbstractText>
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<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Vinh</LastName>
<ForeName>Le Van</ForeName>
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<AffiliationInfo><Affiliation>Faculty of Computer Science and Engineering, HCMC University of Technology, 268 Ly Thuong Kiet, Q10, Ho Chi Minh City, Vietnam.</Affiliation>
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<AffiliationInfo><Affiliation>Institute of Applied Mechanics and Informatics, Vietnam Academy of Science and Technology (VAST), 01 Mac Dinh Chi, Q1, Ho Chi Minh City, Vietnam ; Faculty of Information Technology, Lac Hong University, 10 Huynh Van Nghe, Bien Hoa, Dong Nai Vietnam.</Affiliation>
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<KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Algorithm</Keyword>
<Keyword MajorTopicYN="N">Binning</Keyword>
<Keyword MajorTopicYN="N">Metagenomics</Keyword>
<Keyword MajorTopicYN="N">Next-generation sequencing</Keyword>
<Keyword MajorTopicYN="N">l-mers frequency</Keyword>
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