A two-phase binning algorithm using l-mer frequency on groups of non-overlapping reads.
Identifieur interne : 001914 ( Main/Curation ); précédent : 001913; suivant : 001915A two-phase binning algorithm using l-mer frequency on groups of non-overlapping reads.
Auteurs : Le Van Vinh [Viêt Nam] ; Tran Van Lang ; Le Thanh Binh ; Tran Van Hoai [Viêt Nam]Source :
- 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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Tran Van Lang<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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<affiliation><nlm:affiliation>Institute of Biotechnology, Vietnam Academy of Science and Technology (VAST), 18 Hoang Quoc Viet, Cau Giay, Ha Noi 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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