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Unsupervised discovery of microbial population structure within metagenomes using nucleotide base composition
An approach to infer the unknown microbial population structure within a metagenome is to cluster nucleotide sequences based on common patterns in base composition, otherwise referred to as binning. When functional roles are assigned to the identified populations, a deeper understanding of microbial...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3300000/ https://www.ncbi.nlm.nih.gov/pubmed/22180538 http://dx.doi.org/10.1093/nar/gkr1204 |
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author | Saeed, Isaam Tang, Sen-Lin Halgamuge, Saman K. |
author_facet | Saeed, Isaam Tang, Sen-Lin Halgamuge, Saman K. |
author_sort | Saeed, Isaam |
collection | PubMed |
description | An approach to infer the unknown microbial population structure within a metagenome is to cluster nucleotide sequences based on common patterns in base composition, otherwise referred to as binning. When functional roles are assigned to the identified populations, a deeper understanding of microbial communities can be attained, more so than gene-centric approaches that explore overall functionality. In this study, we propose an unsupervised, model-based binning method with two clustering tiers, which uses a novel transformation of the oligonucleotide frequency-derived error gradient and GC content to generate coarse groups at the first tier of clustering; and tetranucleotide frequency to refine these groups at the secondary clustering tier. The proposed method has a demonstrated improvement over PhyloPythia, S-GSOM, TACOA and TaxSOM on all three benchmarks that were used for evaluation in this study. The proposed method is then applied to a pyrosequenced metagenomic library of mud volcano sediment sampled in southwestern Taiwan, with the inferred population structure validated against complementary sequencing of 16S ribosomal RNA marker genes. Finally, the proposed method was further validated against four publicly available metagenomes, including a highly complex Antarctic whale-fall bone sample, which was previously assumed to be too complex for binning prior to functional analysis. |
format | Online Article Text |
id | pubmed-3300000 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-33000002012-03-13 Unsupervised discovery of microbial population structure within metagenomes using nucleotide base composition Saeed, Isaam Tang, Sen-Lin Halgamuge, Saman K. Nucleic Acids Res Methods Online An approach to infer the unknown microbial population structure within a metagenome is to cluster nucleotide sequences based on common patterns in base composition, otherwise referred to as binning. When functional roles are assigned to the identified populations, a deeper understanding of microbial communities can be attained, more so than gene-centric approaches that explore overall functionality. In this study, we propose an unsupervised, model-based binning method with two clustering tiers, which uses a novel transformation of the oligonucleotide frequency-derived error gradient and GC content to generate coarse groups at the first tier of clustering; and tetranucleotide frequency to refine these groups at the secondary clustering tier. The proposed method has a demonstrated improvement over PhyloPythia, S-GSOM, TACOA and TaxSOM on all three benchmarks that were used for evaluation in this study. The proposed method is then applied to a pyrosequenced metagenomic library of mud volcano sediment sampled in southwestern Taiwan, with the inferred population structure validated against complementary sequencing of 16S ribosomal RNA marker genes. Finally, the proposed method was further validated against four publicly available metagenomes, including a highly complex Antarctic whale-fall bone sample, which was previously assumed to be too complex for binning prior to functional analysis. Oxford University Press 2012-03 2011-12-16 /pmc/articles/PMC3300000/ /pubmed/22180538 http://dx.doi.org/10.1093/nar/gkr1204 Text en © The Author(s) 2011. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methods Online Saeed, Isaam Tang, Sen-Lin Halgamuge, Saman K. Unsupervised discovery of microbial population structure within metagenomes using nucleotide base composition |
title | Unsupervised discovery of microbial population structure within metagenomes using nucleotide base composition |
title_full | Unsupervised discovery of microbial population structure within metagenomes using nucleotide base composition |
title_fullStr | Unsupervised discovery of microbial population structure within metagenomes using nucleotide base composition |
title_full_unstemmed | Unsupervised discovery of microbial population structure within metagenomes using nucleotide base composition |
title_short | Unsupervised discovery of microbial population structure within metagenomes using nucleotide base composition |
title_sort | unsupervised discovery of microbial population structure within metagenomes using nucleotide base composition |
topic | Methods Online |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3300000/ https://www.ncbi.nlm.nih.gov/pubmed/22180538 http://dx.doi.org/10.1093/nar/gkr1204 |
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