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Intrinsic challenges in ancient microbiome reconstruction using 16S rRNA gene amplification

Overview of attention for article published in Scientific Reports, November 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

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37 tweeters

Citations

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38 Dimensions

Readers on

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156 Mendeley
Title
Intrinsic challenges in ancient microbiome reconstruction using 16S rRNA gene amplification
Published in
Scientific Reports, November 2015
DOI 10.1038/srep16498
Pubmed ID
Authors

Kirsten A. Ziesemer, Allison E. Mann, Krithivasan Sankaranarayanan, Hannes Schroeder, Andrew T. Ozga, Bernd W. Brandt, Egija Zaura, Andrea Waters-Rist, Menno Hoogland, Domingo C. Salazar-García, Mark Aldenderfer, Camilla Speller, Jessica Hendy, Darlene A. Weston, Sandy J. MacDonald, Gavin H. Thomas, Matthew J. Collins, Cecil M. Lewis, Corinne Hofman, Christina Warinner

Abstract

To date, characterization of ancient oral (dental calculus) and gut (coprolite) microbiota has been primarily accomplished through a metataxonomic approach involving targeted amplification of one or more variable regions in the 16S rRNA gene. Specifically, the V3 region (E. coli 341-534) of this gene has been suggested as an excellent candidate for ancient DNA amplification and microbial community reconstruction. However, in practice this metataxonomic approach often produces highly skewed taxonomic frequency data. In this study, we use non-targeted (shotgun metagenomics) sequencing methods to better understand skewed microbial profiles observed in four ancient dental calculus specimens previously analyzed by amplicon sequencing. Through comparisons of microbial taxonomic counts from paired amplicon (V3 U341F/534R) and shotgun sequencing datasets, we demonstrate that extensive length polymorphisms in the V3 region are a consistent and major cause of differential amplification leading to taxonomic bias in ancient microbiome reconstructions based on amplicon sequencing. We conclude that systematic amplification bias confounds attempts to accurately reconstruct microbiome taxonomic profiles from 16S rRNA V3 amplicon data generated using universal primers. Because in silico analysis indicates that alternative 16S rRNA hypervariable regions will present similar challenges, we advocate for the use of a shotgun metagenomics approach in ancient microbiome reconstructions.

Twitter Demographics

The data shown below were collected from the profiles of 37 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 156 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
Brazil 2 1%
Germany 1 <1%
United Kingdom 1 <1%
Malaysia 1 <1%
Denmark 1 <1%
France 1 <1%
Netherlands 1 <1%
Canada 1 <1%
Other 0 0%
Unknown 144 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 40 26%
Researcher 29 19%
Student > Master 23 15%
Student > Bachelor 18 12%
Unspecified 10 6%
Other 36 23%
Readers by discipline Count As %
Agricultural and Biological Sciences 69 44%
Biochemistry, Genetics and Molecular Biology 33 21%
Unspecified 14 9%
Social Sciences 9 6%
Medicine and Dentistry 8 5%
Other 23 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 24. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 April 2017.
All research outputs
#626,250
of 12,830,601 outputs
Outputs from Scientific Reports
#6,349
of 60,422 outputs
Outputs of similar age
#19,532
of 277,629 outputs
Outputs of similar age from Scientific Reports
#393
of 3,755 outputs
Altmetric has tracked 12,830,601 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 60,422 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.4. This one has done well, scoring higher than 89% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 277,629 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 3,755 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.