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Virtual terminator nucleotides for next-generation DNA sequencing

Overview of attention for article published in Nature Methods, July 2009
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

30 patents
1 Wikipedia page


85 Dimensions

Readers on

173 Mendeley
4 CiteULike
2 Connotea
Virtual terminator nucleotides for next-generation DNA sequencing
Published in
Nature Methods, July 2009
DOI 10.1038/nmeth.1354
Pubmed ID

Jayson Bowers, Judith Mitchell, Eric Beer, Philip R Buzby, Marie Causey, J William Efcavitch, Mirna Jarosz, Edyta Krzymanska-Olejnik, Li Kung, Doron Lipson, Geoffrey M Lowman, Subramanian Marappan, Peter McInerney, Adam Platt, Atanu Roy, Suhaib M Siddiqi, Kathleen Steinmann, John F Thompson


We synthesized reversible terminators with tethered inhibitors for next-generation sequencing. These were efficiently incorporated with high fidelity while preventing incorporation of additional nucleotides, and we used them to sequence canine bacterial artificial chromosomes in a single-molecule system that provided even coverage for over 99% of the region sequenced. This single-molecule approach generated high-quality sequence data without the need for target amplification and thus avoided concomitant biases.

Mendeley readers

The data shown below were compiled from readership statistics for 173 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
United Kingdom 2 1%
Brazil 2 1%
India 2 1%
Italy 2 1%
Indonesia 1 <1%
Chile 1 <1%
Mexico 1 <1%
Finland 1 <1%
Other 7 4%
Unknown 151 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 51 29%
Researcher 40 23%
Student > Master 19 11%
Student > Bachelor 18 10%
Unspecified 10 6%
Other 35 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 84 49%
Biochemistry, Genetics and Molecular Biology 23 13%
Chemistry 12 7%
Medicine and Dentistry 12 7%
Unspecified 11 6%
Other 31 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 2018.
All research outputs
of 12,452,184 outputs
Outputs from Nature Methods
of 3,599 outputs
Outputs of similar age
of 270,876 outputs
Outputs of similar age from Nature Methods
of 99 outputs
Altmetric has tracked 12,452,184 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,599 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 24.1. This one has gotten more attention than average, scoring higher than 52% 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 270,876 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 85% of its contemporaries.
We're also able to compare this research output to 99 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.