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Correcting for 16S rRNA gene copy numbers in microbiome surveys remains an unsolved problem

Overview of attention for article published in Microbiome, February 2018
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (96th percentile)

Mentioned by

blogs
1 blog
twitter
150 tweeters

Citations

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206 Dimensions

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425 Mendeley
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2 CiteULike
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Title
Correcting for 16S rRNA gene copy numbers in microbiome surveys remains an unsolved problem
Published in
Microbiome, February 2018
DOI 10.1186/s40168-018-0420-9
Pubmed ID
Authors

Stilianos Louca, Michael Doebeli, Laura Wegener Parfrey

Abstract

The 16S ribosomal RNA gene is the most widely used marker gene in microbial ecology. Counts of 16S sequence variants, often in PCR amplicons, are used to estimate proportions of bacterial and archaeal taxa in microbial communities. Because different organisms contain different 16S gene copy numbers (GCNs), sequence variant counts are biased towards clades with greater GCNs. Several tools have recently been developed for predicting GCNs using phylogenetic methods and based on sequenced genomes, in order to correct for these biases. However, the accuracy of those predictions has not been independently assessed. Here, we systematically evaluate the predictability of 16S GCNs across bacterial and archaeal clades, based on ∼ 6,800 public sequenced genomes and using several phylogenetic methods. Further, we assess the accuracy of GCNs predicted by three recently published tools (PICRUSt, CopyRighter, and PAPRICA) over a wide range of taxa and for 635 microbial communities from varied environments. We find that regardless of the phylogenetic method tested, 16S GCNs could only be accurately predicted for a limited fraction of taxa, namely taxa with closely to moderately related representatives (≲15% divergence in the 16S rRNA gene). Consistent with this observation, we find that all considered tools exhibit low predictive accuracy when evaluated against completely sequenced genomes, in some cases explaining less than 10% of the variance. Substantial disagreement was also observed between tools (R2<0.5) for the majority of tested microbial communities. The nearest sequenced taxon index (NSTI) of microbial communities, i.e., the average distance to a sequenced genome, was a strong predictor for the agreement between GCN prediction tools on non-animal-associated samples, but only a moderate predictor for animal-associated samples. We recommend against correcting for 16S GCNs in microbiome surveys by default, unless OTUs are sufficiently closely related to sequenced genomes or unless a need for true OTU proportions warrants the additional noise introduced, so that community profiles remain interpretable and comparable between studies.

Twitter Demographics

The data shown below were collected from the profiles of 150 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 425 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 425 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 114 27%
Researcher 77 18%
Student > Master 66 16%
Student > Bachelor 34 8%
Student > Postgraduate 22 5%
Other 50 12%
Unknown 62 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 135 32%
Biochemistry, Genetics and Molecular Biology 89 21%
Environmental Science 41 10%
Immunology and Microbiology 26 6%
Engineering 12 3%
Other 38 9%
Unknown 84 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 90. 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 23 October 2019.
All research outputs
#318,461
of 19,541,023 outputs
Outputs from Microbiome
#80
of 1,186 outputs
Outputs of similar age
#9,235
of 289,641 outputs
Outputs of similar age from Microbiome
#1
of 1 outputs
Altmetric has tracked 19,541,023 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,186 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 40.1. This one has done particularly well, scoring higher than 93% 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 289,641 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 96% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them