↓ Skip to main content

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

Overview of attention for article published in Scientific Reports, November 2015
Altmetric Badge

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 (94th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

news
1 news outlet
twitter
38 tweeters

Citations

dimensions_citation
82 Dimensions

Readers on

mendeley
223 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
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 38 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 223 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 1%
Brazil 2 <1%
Netherlands 1 <1%
France 1 <1%
Malaysia 1 <1%
United Kingdom 1 <1%
Canada 1 <1%
Denmark 1 <1%
Germany 1 <1%
Other 0 0%
Unknown 211 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 52 23%
Researcher 41 18%
Student > Bachelor 29 13%
Student > Master 28 13%
Student > Doctoral Student 10 4%
Other 36 16%
Unknown 27 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 79 35%
Biochemistry, Genetics and Molecular Biology 52 23%
Environmental Science 12 5%
Social Sciences 11 5%
Medicine and Dentistry 11 5%
Other 29 13%
Unknown 29 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 32. 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 07 August 2019.
All research outputs
#779,238
of 17,709,629 outputs
Outputs from Scientific Reports
#8,132
of 95,403 outputs
Outputs of similar age
#17,060
of 291,050 outputs
Outputs of similar age from Scientific Reports
#370
of 4,197 outputs
Altmetric has tracked 17,709,629 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 95,403 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 16.7. This one has done particularly well, scoring higher than 91% 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 291,050 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 94% of its contemporaries.
We're also able to compare this research output to 4,197 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.