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Patterns of cross-contamination in a multispecies population genomic project: detection, quantification, impact, and solutions

Overview of attention for article published in BMC Biology, March 2017
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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 (97th percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
twitter
148 tweeters
facebook
2 Facebook pages

Citations

dimensions_citation
82 Dimensions

Readers on

mendeley
115 Mendeley
citeulike
1 CiteULike
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Title
Patterns of cross-contamination in a multispecies population genomic project: detection, quantification, impact, and solutions
Published in
BMC Biology, March 2017
DOI 10.1186/s12915-017-0366-6
Pubmed ID
Authors

Marion Ballenghien, Nicolas Faivre, Nicolas Galtier

Abstract

Contamination is a well-known but often neglected problem in molecular biology. Here, we investigated the prevalence of cross-contamination among 446 samples from 116 distinct species of animals, which were processed in the same laboratory and subjected to subcontracted transcriptome sequencing. Using cytochrome oxidase 1 as a barcode, we identified a minimum of 782 events of between-species contamination, with approximately 80% of our samples being affected. An analysis of laboratory metadata revealed a strong effect of the sequencing center: nearly all the detected events of between-species contamination involved species that were sent the same day to the same company. We introduce new methods to address the amount of within-species, between-individual contamination, and to correct for this problem when calling genotypes from base read counts. We report evidence for pervasive within-species contamination in this data set, and show that classical population genomic statistics, such as synonymous diversity, the ratio of non-synonymous to synonymous diversity, inbreeding coefficient FIT, and Tajima's D, are sensitive to this problem to various extents. Control analyses suggest that our published results are probably robust to the problem of contamination. Recommendations on how to prevent or avoid contamination in large-scale population genomics/molecular ecology are provided based on this analysis.

Twitter Demographics

The data shown below were collected from the profiles of 148 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 115 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 2%
United States 2 2%
Netherlands 1 <1%
Australia 1 <1%
Unknown 109 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 29 25%
Student > Ph. D. Student 18 16%
Student > Bachelor 12 10%
Student > Master 12 10%
Other 9 8%
Other 19 17%
Unknown 16 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 53 46%
Biochemistry, Genetics and Molecular Biology 26 23%
Environmental Science 7 6%
Computer Science 3 3%
Medicine and Dentistry 2 2%
Other 4 3%
Unknown 20 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 96. 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 13 June 2021.
All research outputs
#360,361
of 22,463,866 outputs
Outputs from BMC Biology
#81
of 1,956 outputs
Outputs of similar age
#8,479
of 284,640 outputs
Outputs of similar age from BMC Biology
#1
of 1 outputs
Altmetric has tracked 22,463,866 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 1,956 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 21.6. This one has done particularly well, scoring higher than 95% 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 284,640 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 97% 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