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The bench scientist's guide to statistical analysis of RNA-Seq data

Overview of attention for article published in BMC Research Notes, September 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 (91st percentile)

Mentioned by

blogs
1 blog
twitter
24 X users

Citations

dimensions_citation
29 Dimensions

Readers on

mendeley
467 Mendeley
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10 CiteULike
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Title
The bench scientist's guide to statistical analysis of RNA-Seq data
Published in
BMC Research Notes, September 2012
DOI 10.1186/1756-0500-5-506
Pubmed ID
Authors

Craig R Yendrek, Elizabeth A Ainsworth, Jyothi Thimmapuram

Abstract

RNA sequencing (RNA-Seq) is emerging as a highly accurate method to quantify transcript abundance. However, analyses of the large data sets obtained by sequencing the entire transcriptome of organisms have generally been performed by bioinformatics specialists. Here we provide a step-by-step guide and outline a strategy using currently available statistical tools that results in a conservative list of differentially expressed genes. We also discuss potential sources of error in RNA-Seq analysis that could alter interpretation of global changes in gene expression.

X Demographics

X Demographics

The data shown below were collected from the profiles of 24 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 467 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 18 4%
Germany 7 1%
Italy 5 1%
Brazil 5 1%
Spain 4 <1%
France 3 <1%
Sweden 3 <1%
United Kingdom 3 <1%
Japan 2 <1%
Other 17 4%
Unknown 400 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 151 32%
Researcher 146 31%
Student > Master 46 10%
Student > Bachelor 20 4%
Professor > Associate Professor 19 4%
Other 55 12%
Unknown 30 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 289 62%
Biochemistry, Genetics and Molecular Biology 62 13%
Medicine and Dentistry 22 5%
Computer Science 15 3%
Neuroscience 13 3%
Other 31 7%
Unknown 35 7%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 21. 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 25 June 2021.
All research outputs
#1,754,360
of 25,301,208 outputs
Outputs from BMC Research Notes
#198
of 4,504 outputs
Outputs of similar age
#10,470
of 176,877 outputs
Outputs of similar age from BMC Research Notes
#9
of 96 outputs
Altmetric has tracked 25,301,208 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 4,504 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one has done particularly well, scoring higher than 95% 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 176,877 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 96 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 91% of its contemporaries.