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Decontamination of 16S rRNA gene amplicon sequence datasets based on bacterial load assessment by qPCR

Overview of attention for article published in BMC Microbiology, April 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)

Mentioned by

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14 tweeters

Citations

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27 Dimensions

Readers on

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78 Mendeley
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Title
Decontamination of 16S rRNA gene amplicon sequence datasets based on bacterial load assessment by qPCR
Published in
BMC Microbiology, April 2016
DOI 10.1186/s12866-016-0689-4
Pubmed ID
Authors

Vladimir Lazarevic, Nadia Gaïa, Myriam Girard, Jacques Schrenzel

Abstract

Identification of unexpected taxa in 16S rRNA surveys of low-density microbiota, diluted mock communities and cultures demonstrated that a variable fraction of sequence reads originated from exogenous DNA. The sources of these contaminants are reagents used in DNA extraction, PCR, and next-generation sequencing library preparation, and human (skin, oral and respiratory) microbiota from the investigators. For in silico removal of reagent contaminants, a pipeline was used which combines the relative abundance of operational taxonomic units (OTUs) in V3-4 16S rRNA gene amplicon datasets with bacterial DNA quantification based on qPCR targeting of the V3 segment of the 16S rRNA gene. Serially diluted cultures of Escherichia coli and Staphylococcus aureus were used for 16S rDNA profiling, and DNA from each of these species was used as a qPCR standard. OTUs assigned to Escherichia or Staphylococcus were virtually unaffected by the decontamination procedure, whereas OTUs from Pseudomonas, which is a major reagent contaminant, were completely or nearly completely removed. The decontamination procedure also attenuated the trend of increase in OTU richness in serially diluted cultures. Removal of contaminant sequences derived from reagents based on use of qPCR data may improve taxonomic representation in samples with low DNA concentration. Using the described pipeline, OTUs derived from cross-contamination of negative extraction controls were not recognized as contaminants and not removed from the sample dataset.

Twitter Demographics

The data shown below were collected from the profiles of 14 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 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 4%
Netherlands 1 1%
Germany 1 1%
Canada 1 1%
Unknown 72 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 24%
Student > Ph. D. Student 16 21%
Student > Master 12 15%
Student > Bachelor 8 10%
Student > Postgraduate 6 8%
Other 12 15%
Unknown 5 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 33 42%
Medicine and Dentistry 12 15%
Biochemistry, Genetics and Molecular Biology 8 10%
Immunology and Microbiology 7 9%
Environmental Science 4 5%
Other 6 8%
Unknown 8 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 26 April 2016.
All research outputs
#3,273,388
of 14,579,947 outputs
Outputs from BMC Microbiology
#347
of 2,189 outputs
Outputs of similar age
#62,848
of 261,197 outputs
Outputs of similar age from BMC Microbiology
#1
of 1 outputs
Altmetric has tracked 14,579,947 research outputs across all sources so far. Compared to these this one has done well and is in the 77th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,189 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done well, scoring higher than 84% 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 261,197 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 75% 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