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GC-Content Normalization for RNA-Seq Data

Overview of attention for article published in BMC Bioinformatics, December 2011
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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 (95th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

blogs
1 blog
twitter
18 tweeters
patent
2 patents
wikipedia
1 Wikipedia page

Citations

dimensions_citation
539 Dimensions

Readers on

mendeley
1006 Mendeley
citeulike
28 CiteULike
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Title
GC-Content Normalization for RNA-Seq Data
Published in
BMC Bioinformatics, December 2011
DOI 10.1186/1471-2105-12-480
Pubmed ID
Authors

Davide Risso, Katja Schwartz, Gavin Sherlock, Sandrine Dudoit

Abstract

Transcriptome sequencing (RNA-Seq) has become the assay of choice for high-throughput studies of gene expression. However, as is the case with microarrays, major technology-related artifacts and biases affect the resulting expression measures. Normalization is therefore essential to ensure accurate inference of expression levels and subsequent analyses thereof.

Twitter Demographics

The data shown below were collected from the profiles of 18 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 1,006 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 23 2%
Germany 10 <1%
United Kingdom 9 <1%
Italy 4 <1%
France 4 <1%
Brazil 3 <1%
Japan 3 <1%
Sweden 2 <1%
Portugal 2 <1%
Other 20 2%
Unknown 926 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 301 30%
Researcher 245 24%
Student > Master 99 10%
Student > Bachelor 76 8%
Student > Doctoral Student 50 5%
Other 145 14%
Unknown 90 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 427 42%
Biochemistry, Genetics and Molecular Biology 236 23%
Computer Science 57 6%
Medicine and Dentistry 47 5%
Mathematics 40 4%
Other 91 9%
Unknown 108 11%

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 20 August 2020.
All research outputs
#1,281,507
of 21,298,857 outputs
Outputs from BMC Bioinformatics
#228
of 6,905 outputs
Outputs of similar age
#9,441
of 222,923 outputs
Outputs of similar age from BMC Bioinformatics
#10
of 317 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 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,905 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 222,923 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 95% of its contemporaries.
We're also able to compare this research output to 317 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.