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SNES: single nucleus exome sequencing

Overview of attention for article published in Genome Biology (Online Edition), March 2015
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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 (90th percentile)

Mentioned by

33 tweeters
2 Facebook pages
1 Google+ user


82 Dimensions

Readers on

176 Mendeley
5 CiteULike
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SNES: single nucleus exome sequencing
Published in
Genome Biology (Online Edition), March 2015
DOI 10.1186/s13059-015-0616-2
Pubmed ID

Marco L Leung, Yong Wang, Jill Waters, Nicholas E Navin


Single-cell genome sequencing methods are challenged by poor physical coverage and high error rates, making it difficult to distinguish real biological variants from technical artifacts. To address this problem, we developed a method called SNES that combines flow-sorting of single G1/0 or G2/M nuclei, time-limited multiple-displacement-amplification, exome capture, and next-generation sequencing to generate high coverage (96%) data from single human cells. We validated our method in a fibroblast cell line, and show low allelic dropout and false-positive error rates, resulting in high detection efficiencies for single nucleotide variants (92%) and indels (85%) in single cells.

Twitter Demographics

The data shown below were collected from the profiles of 33 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 176 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 2 1%
United States 2 1%
Germany 1 <1%
Korea, Republic of 1 <1%
Czechia 1 <1%
South Africa 1 <1%
China 1 <1%
United Kingdom 1 <1%
Unknown 166 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 49 28%
Researcher 44 25%
Student > Bachelor 17 10%
Student > Master 16 9%
Student > Doctoral Student 14 8%
Other 21 12%
Unknown 15 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 66 38%
Biochemistry, Genetics and Molecular Biology 51 29%
Computer Science 10 6%
Medicine and Dentistry 9 5%
Neuroscience 6 3%
Other 13 7%
Unknown 21 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 19. 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 28 August 2015.
All research outputs
of 21,298,857 outputs
Outputs from Genome Biology (Online Edition)
of 4,017 outputs
Outputs of similar age
of 239,467 outputs
Outputs of similar age from Genome Biology (Online Edition)
of 1 outputs
Altmetric has tracked 21,298,857 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,017 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.1. This one has gotten more attention than average, scoring higher than 63% 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 239,467 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 90% 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