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Reconstruction of Ribosomal RNA Genes from Metagenomic Data

Direct sequencing of environmental DNA (metagenomics) has a great potential for describing the 16S rRNA gene diversity of microbial communities. However current approaches using this 16S rRNA gene information to describe community diversity suffer from low taxonomic resolution or chimera problems. H...

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Detalles Bibliográficos
Autores principales: Fan, Lu, McElroy, Kerensa, Thomas, Torsten
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3384625/
https://www.ncbi.nlm.nih.gov/pubmed/22761935
http://dx.doi.org/10.1371/journal.pone.0039948
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author Fan, Lu
McElroy, Kerensa
Thomas, Torsten
author_facet Fan, Lu
McElroy, Kerensa
Thomas, Torsten
author_sort Fan, Lu
collection PubMed
description Direct sequencing of environmental DNA (metagenomics) has a great potential for describing the 16S rRNA gene diversity of microbial communities. However current approaches using this 16S rRNA gene information to describe community diversity suffer from low taxonomic resolution or chimera problems. Here we describe a new strategy that involves stringent assembly and data filtering to reconstruct full-length 16S rRNA genes from metagenomicpyrosequencing data. Simulations showed that reconstructed 16S rRNA genes provided a true picture of the community diversity, had minimal rates of chimera formation and gave taxonomic resolution down to genus level. The strategy was furthermore compared to PCR-based methods to determine the microbial diversity in two marine sponges. This showed that about 30% of the abundant phylotypes reconstructed from metagenomic data failed to be amplified by PCR. Our approach is readily applicable to existing metagenomic datasets and is expected to lead to the discovery of new microbial phylotypes.
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spelling pubmed-33846252012-07-03 Reconstruction of Ribosomal RNA Genes from Metagenomic Data Fan, Lu McElroy, Kerensa Thomas, Torsten PLoS One Research Article Direct sequencing of environmental DNA (metagenomics) has a great potential for describing the 16S rRNA gene diversity of microbial communities. However current approaches using this 16S rRNA gene information to describe community diversity suffer from low taxonomic resolution or chimera problems. Here we describe a new strategy that involves stringent assembly and data filtering to reconstruct full-length 16S rRNA genes from metagenomicpyrosequencing data. Simulations showed that reconstructed 16S rRNA genes provided a true picture of the community diversity, had minimal rates of chimera formation and gave taxonomic resolution down to genus level. The strategy was furthermore compared to PCR-based methods to determine the microbial diversity in two marine sponges. This showed that about 30% of the abundant phylotypes reconstructed from metagenomic data failed to be amplified by PCR. Our approach is readily applicable to existing metagenomic datasets and is expected to lead to the discovery of new microbial phylotypes. Public Library of Science 2012-06-27 /pmc/articles/PMC3384625/ /pubmed/22761935 http://dx.doi.org/10.1371/journal.pone.0039948 Text en Fan et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Fan, Lu
McElroy, Kerensa
Thomas, Torsten
Reconstruction of Ribosomal RNA Genes from Metagenomic Data
title Reconstruction of Ribosomal RNA Genes from Metagenomic Data
title_full Reconstruction of Ribosomal RNA Genes from Metagenomic Data
title_fullStr Reconstruction of Ribosomal RNA Genes from Metagenomic Data
title_full_unstemmed Reconstruction of Ribosomal RNA Genes from Metagenomic Data
title_short Reconstruction of Ribosomal RNA Genes from Metagenomic Data
title_sort reconstruction of ribosomal rna genes from metagenomic data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3384625/
https://www.ncbi.nlm.nih.gov/pubmed/22761935
http://dx.doi.org/10.1371/journal.pone.0039948
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