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Genotyping 1000 yeast strains by next-generation sequencing

Overview of attention for article published in BMC Genomics, February 2013
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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 (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

twitter
30 tweeters

Citations

dimensions_citation
40 Dimensions

Readers on

mendeley
197 Mendeley
citeulike
4 CiteULike
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Title
Genotyping 1000 yeast strains by next-generation sequencing
Published in
BMC Genomics, February 2013
DOI 10.1186/1471-2164-14-90
Pubmed ID
Authors

Stefan Wilkening, Manu M Tekkedil, Gen Lin, Emilie S Fritsch, Wu Wei, Julien Gagneur, David W Lazinski, Andrew Camilli, Lars M Steinmetz

Abstract

The throughput of next-generation sequencing machines has increased dramatically over the last few years; yet the cost and time for library preparation have not changed proportionally, thus representing the main bottleneck for sequencing large numbers of samples. Here we present an economical, high-throughput library preparation method for the Illumina platform, comprising a 96-well based method for DNA isolation for yeast cells, a low-cost DNA shearing alternative, and adapter ligation using heat inactivation of enzymes instead of bead cleanups.

Twitter Demographics

The data shown below were collected from the profiles of 30 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 197 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
Spain 3 2%
United Kingdom 2 1%
Australia 2 1%
Sweden 1 <1%
Brazil 1 <1%
Italy 1 <1%
France 1 <1%
Singapore 1 <1%
Other 4 2%
Unknown 178 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 61 31%
Student > Ph. D. Student 51 26%
Student > Master 22 11%
Professor > Associate Professor 12 6%
Student > Bachelor 12 6%
Other 31 16%
Unknown 8 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 131 66%
Biochemistry, Genetics and Molecular Biology 39 20%
Computer Science 5 3%
Environmental Science 4 2%
Engineering 2 1%
Other 5 3%
Unknown 11 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 24 March 2013.
All research outputs
#1,168,537
of 15,873,383 outputs
Outputs from BMC Genomics
#362
of 8,841 outputs
Outputs of similar age
#17,685
of 253,327 outputs
Outputs of similar age from BMC Genomics
#35
of 743 outputs
Altmetric has tracked 15,873,383 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 8,841 research outputs from this source. They receive a mean Attention Score of 4.3. 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 253,327 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 92% of its contemporaries.
We're also able to compare this research output to 743 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.