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Unraveling the outcome of 16S rDNA-based taxonomy analysis through mock data and simulations

Overview of attention for article published in Bioinformatics, February 2014
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  • Good Attention Score compared to outputs of the same age (76th percentile)
  • Average Attention Score compared to outputs of the same age and source

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Citations

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109 Mendeley
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1 CiteULike
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Title
Unraveling the outcome of 16S rDNA-based taxonomy analysis through mock data and simulations
Published in
Bioinformatics, February 2014
DOI 10.1093/bioinformatics/btu085
Pubmed ID
Authors

Ali May, Sanne Abeln, Wim Crielaard, Jaap Heringa, Bernd W Brandt

Abstract

16S rDNA pyrosequencing is a powerful approach that requires extensive usage of computational methods for delineating microbial compositions. Previously, it was shown that outcomes of studies relying on this approach vastly depend on the choice of pre-processing and clustering algorithms used. However, obtaining insights into the effects and accuracy of these algorithms is challenging due to difficulties in generating samples of known composition with high enough diversity. Here, we use in silico microbial datasets to better understand how the experimental data are transformed into taxonomic clusters by computational methods.

X Demographics

X Demographics

The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 109 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 6%
Netherlands 4 4%
Switzerland 2 2%
Brazil 1 <1%
Sweden 1 <1%
Norway 1 <1%
Denmark 1 <1%
Canada 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 90 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 32 29%
Student > Ph. D. Student 29 27%
Student > Master 19 17%
Student > Bachelor 6 6%
Professor > Associate Professor 5 5%
Other 13 12%
Unknown 5 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 54 50%
Biochemistry, Genetics and Molecular Biology 15 14%
Computer Science 9 8%
Environmental Science 9 8%
Medicine and Dentistry 5 5%
Other 11 10%
Unknown 6 6%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 11 October 2018.
All research outputs
#6,874,583
of 25,371,288 outputs
Outputs from Bioinformatics
#5,640
of 12,808 outputs
Outputs of similar age
#76,893
of 327,774 outputs
Outputs of similar age from Bioinformatics
#92
of 187 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 12,808 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one has gotten more attention than average, scoring higher than 55% 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 327,774 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 187 others from the same source and published within six weeks on either side of this one. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.