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Accuracy of RNA-Seq and its dependence on sequencing depth

Overview of attention for article published in BMC Bioinformatics, August 2012
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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 (94th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

blogs
2 blogs
twitter
19 X users

Readers on

mendeley
186 Mendeley
citeulike
6 CiteULike
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Title
Accuracy of RNA-Seq and its dependence on sequencing depth
Published in
BMC Bioinformatics, August 2012
DOI 10.1186/1471-2105-13-s13-s5
Pubmed ID
Authors

Guoshuai Cai, Hua Li, Yue Lu, Xuelin Huang, Juhee Lee, Peter Müller, Yuan Ji, Shoudan Liang

Abstract

The cost of DNA sequencing has undergone a dramatical reduction in the past decade. As a result, sequencing technologies have been increasingly applied to genomic research. RNA-Seq is becoming a common technique for surveying gene expression based on DNA sequencing. As it is not clear how increased sequencing capacity has affected measurement accuracy of mRNA, we sought to investigate that relationship.

X Demographics

X Demographics

The data shown below were collected from the profiles of 19 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 186 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 10 5%
Denmark 3 2%
France 3 2%
Germany 2 1%
Italy 2 1%
India 2 1%
United Kingdom 2 1%
Canada 2 1%
Norway 1 <1%
Other 9 5%
Unknown 150 81%

Demographic breakdown

Readers by professional status Count As %
Researcher 57 31%
Student > Ph. D. Student 43 23%
Student > Master 26 14%
Professor 10 5%
Professor > Associate Professor 10 5%
Other 31 17%
Unknown 9 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 117 63%
Biochemistry, Genetics and Molecular Biology 20 11%
Medicine and Dentistry 10 5%
Computer Science 10 5%
Mathematics 4 2%
Other 12 6%
Unknown 13 7%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 23. 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 15 November 2013.
All research outputs
#1,390,550
of 22,862,742 outputs
Outputs from BMC Bioinformatics
#236
of 7,294 outputs
Outputs of similar age
#8,719
of 169,561 outputs
Outputs of similar age from BMC Bioinformatics
#4
of 99 outputs
Altmetric has tracked 22,862,742 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,294 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. 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 169,561 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 94% 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 has done particularly well, scoring higher than 96% of its contemporaries.