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A comparison across non-model animals suggests an optimal sequencing depth for de novo transcriptome assembly

Overview of attention for article published in BMC Genomics, January 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 (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

twitter
36 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
73 Dimensions

Readers on

mendeley
250 Mendeley
citeulike
1 CiteULike
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Title
A comparison across non-model animals suggests an optimal sequencing depth for de novo transcriptome assembly
Published in
BMC Genomics, January 2013
DOI 10.1186/1471-2164-14-167
Pubmed ID
Authors

Warren R Francis, Lynne M Christianson, Rainer Kiko, Meghan L Powers, Nathan C Shaner, Steven H D Haddock

Abstract

The lack of genomic resources can present challenges for studies of non-model organisms. Transcriptome sequencing offers an attractive method to gather information about genes and gene expression without the need for a reference genome. However, it is unclear what sequencing depth is adequate to assemble the transcriptome de novo for these purposes.

Twitter Demographics

The data shown below were collected from the profiles of 36 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 250 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 8 3%
Germany 5 2%
France 4 2%
Japan 3 1%
United Kingdom 2 <1%
Canada 2 <1%
Australia 2 <1%
Spain 2 <1%
Brazil 2 <1%
Other 6 2%
Unknown 214 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 72 29%
Researcher 60 24%
Student > Master 30 12%
Student > Doctoral Student 15 6%
Professor > Associate Professor 14 6%
Other 50 20%
Unknown 9 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 165 66%
Biochemistry, Genetics and Molecular Biology 41 16%
Computer Science 8 3%
Neuroscience 4 2%
Immunology and Microbiology 4 2%
Other 16 6%
Unknown 12 5%

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 01 December 2015.
All research outputs
#714,586
of 12,378,087 outputs
Outputs from BMC Genomics
#246
of 7,251 outputs
Outputs of similar age
#9,232
of 144,644 outputs
Outputs of similar age from BMC Genomics
#4
of 179 outputs
Altmetric has tracked 12,378,087 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,251 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done particularly well, scoring higher than 96% 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 144,644 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 93% of its contemporaries.
We're also able to compare this research output to 179 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 97% of its contemporaries.