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Reconstruction of Ribosomal RNA Genes from Metagenomic Data

Overview of attention for article published in PLOS ONE, June 2012
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Title
Reconstruction of Ribosomal RNA Genes from Metagenomic Data
Published in
PLOS ONE, June 2012
DOI 10.1371/journal.pone.0039948
Pubmed ID
Authors

Lu Fan, Kerensa McElroy, Torsten Thomas

Abstract

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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Mendeley readers

The data shown below were compiled from readership statistics for 171 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 10 6%
Brazil 4 2%
Germany 3 2%
France 2 1%
Sweden 2 1%
Norway 1 <1%
Colombia 1 <1%
South Africa 1 <1%
India 1 <1%
Other 9 5%
Unknown 137 80%

Demographic breakdown

Readers by professional status Count As %
Researcher 53 31%
Student > Ph. D. Student 38 22%
Student > Master 26 15%
Student > Doctoral Student 11 6%
Student > Postgraduate 11 6%
Other 26 15%
Unknown 6 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 102 60%
Environmental Science 19 11%
Biochemistry, Genetics and Molecular Biology 19 11%
Immunology and Microbiology 7 4%
Medicine and Dentistry 3 2%
Other 9 5%
Unknown 12 7%