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Blind spots of quantitative RNA-seq: the limits for assessing abundance, differential expression, and isoform switching

Overview of attention for article published in BMC Bioinformatics, December 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 (94th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

blogs
1 blog
twitter
19 tweeters
q&a
2 Q&A threads

Citations

dimensions_citation
39 Dimensions

Readers on

mendeley
189 Mendeley
citeulike
4 CiteULike
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Title
Blind spots of quantitative RNA-seq: the limits for assessing abundance, differential expression, and isoform switching
Published in
BMC Bioinformatics, December 2013
DOI 10.1186/1471-2105-14-370
Pubmed ID
Authors

Hubert Rehrauer, Lennart Opitz, Ge Tan, Lina Sieverling, Ralph Schlapbach

Abstract

RNA-seq is now widely used to quantitatively assess gene expression, expression differences and isoform switching, and promises to deliver results for the entire transcriptome. However, whether the transcriptional state of a gene can be captured accurately depends critically on library preparation, read alignment, expression estimation and the tests for differential expression and isoform switching. There are comparisons available for the individual steps but there is not yet a systematic investigation which specific genes are impacted by biases throughout the entire analysis workflow. It is especially unclear whether for a given gene, with current methods and protocols, expression changes and isoform switches can be detected.

Twitter Demographics

The data shown below were collected from the profiles of 19 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 189 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 16 8%
United Kingdom 4 2%
Germany 3 2%
Norway 3 2%
Brazil 3 2%
Spain 2 1%
Sweden 2 1%
Finland 1 <1%
Czechia 1 <1%
Other 5 3%
Unknown 149 79%

Demographic breakdown

Readers by professional status Count As %
Researcher 63 33%
Student > Ph. D. Student 55 29%
Student > Master 16 8%
Student > Bachelor 12 6%
Professor > Associate Professor 10 5%
Other 26 14%
Unknown 7 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 120 63%
Biochemistry, Genetics and Molecular Biology 32 17%
Computer Science 10 5%
Neuroscience 5 3%
Engineering 4 2%
Other 8 4%
Unknown 10 5%

Attention Score in Context

This research output has an Altmetric Attention Score of 22. 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 27 October 2020.
All research outputs
#1,471,051
of 22,738,543 outputs
Outputs from BMC Bioinformatics
#285
of 7,266 outputs
Outputs of similar age
#17,999
of 306,712 outputs
Outputs of similar age from BMC Bioinformatics
#8
of 114 outputs
Altmetric has tracked 22,738,543 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 7,266 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 306,712 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 114 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 92% of its contemporaries.