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IVT-seq reveals extreme bias in RNA sequencing

Overview of attention for article published in Genome Biology, June 2014
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

blogs
3 blogs
twitter
111 X users
weibo
1 weibo user
wikipedia
5 Wikipedia pages
googleplus
1 Google+ user
f1000
1 research highlight platform

Citations

dimensions_citation
136 Dimensions

Readers on

mendeley
505 Mendeley
citeulike
15 CiteULike
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Title
IVT-seq reveals extreme bias in RNA sequencing
Published in
Genome Biology, June 2014
DOI 10.1186/gb-2014-15-6-r86
Pubmed ID
Authors

Nicholas F Lahens, Ibrahim Halil Kavakli, Ray Zhang, Katharina Hayer, Michael B Black, Hannah Dueck, Angel Pizarro, Junhyong Kim, Rafael Irizarry, Russell S Thomas, Gregory R Grant, John B Hogenesch

Abstract

RNA-seq is a powerful technique for identifying and quantifying transcription and splicing events, both known and novel. However, given its recent development and the proliferation of library construction methods, understanding the bias it introduces is incomplete but critical to realizing its value.

X Demographics

X Demographics

The data shown below were collected from the profiles of 111 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 505 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 17 3%
United Kingdom 6 1%
France 3 <1%
Spain 3 <1%
Germany 3 <1%
Brazil 3 <1%
Australia 2 <1%
New Zealand 2 <1%
Norway 1 <1%
Other 11 2%
Unknown 454 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 135 27%
Student > Ph. D. Student 130 26%
Student > Master 44 9%
Student > Bachelor 30 6%
Professor 25 5%
Other 92 18%
Unknown 49 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 244 48%
Biochemistry, Genetics and Molecular Biology 119 24%
Computer Science 22 4%
Medicine and Dentistry 18 4%
Immunology and Microbiology 13 3%
Other 34 7%
Unknown 55 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 84. 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 04 May 2023.
All research outputs
#516,894
of 25,721,020 outputs
Outputs from Genome Biology
#290
of 4,507 outputs
Outputs of similar age
#4,512
of 242,655 outputs
Outputs of similar age from Genome Biology
#4
of 58 outputs
Altmetric has tracked 25,721,020 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,507 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has done particularly well, scoring higher than 93% 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 242,655 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 98% of its contemporaries.
We're also able to compare this research output to 58 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 93% of its contemporaries.