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Differential meta-analysis of RNA-seq data from multiple studies

Overview of attention for article published in BMC Bioinformatics, March 2014
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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 (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

blogs
1 blog
policy
1 policy source
twitter
26 tweeters
googleplus
1 Google+ user

Citations

dimensions_citation
64 Dimensions

Readers on

mendeley
280 Mendeley
citeulike
5 CiteULike
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Title
Differential meta-analysis of RNA-seq data from multiple studies
Published in
BMC Bioinformatics, March 2014
DOI 10.1186/1471-2105-15-91
Pubmed ID
Authors

Andrea Rau, Guillemette Marot, Florence Jaffrézic

Abstract

High-throughput sequencing is now regularly used for studies of the transcriptome (RNA-seq), particularly for comparisons among experimental conditions. For the time being, a limited number of biological replicates are typically considered in such experiments, leading to low detection power for differential expression. As their cost continues to decrease, it is likely that additional follow-up studies will be conducted to re-address the same biological question.

Twitter Demographics

The data shown below were collected from the profiles of 26 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 280 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 1%
United Kingdom 3 1%
Germany 2 <1%
France 2 <1%
Italy 2 <1%
Ireland 1 <1%
Switzerland 1 <1%
Netherlands 1 <1%
Finland 1 <1%
Other 2 <1%
Unknown 262 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 85 30%
Student > Ph. D. Student 82 29%
Student > Master 37 13%
Student > Bachelor 16 6%
Student > Doctoral Student 13 5%
Other 30 11%
Unknown 17 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 121 43%
Biochemistry, Genetics and Molecular Biology 72 26%
Computer Science 16 6%
Medicine and Dentistry 13 5%
Neuroscience 10 4%
Other 24 9%
Unknown 24 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 26. 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 02 October 2021.
All research outputs
#1,124,346
of 20,618,952 outputs
Outputs from BMC Bioinformatics
#171
of 6,807 outputs
Outputs of similar age
#12,628
of 204,682 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 21 outputs
Altmetric has tracked 20,618,952 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,807 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 97% 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 204,682 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 93% of its contemporaries.
We're also able to compare this research output to 21 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 99% of its contemporaries.