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A comparison of methods for differential expression analysis of RNA-seq data

Overview of attention for article published in BMC Bioinformatics, March 2013
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#24 of 7,701)
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

blogs
4 blogs
twitter
80 X users
facebook
1 Facebook page
wikipedia
2 Wikipedia pages
googleplus
2 Google+ users

Citations

dimensions_citation
757 Dimensions

Readers on

mendeley
2577 Mendeley
citeulike
34 CiteULike
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Title
A comparison of methods for differential expression analysis of RNA-seq data
Published in
BMC Bioinformatics, March 2013
DOI 10.1186/1471-2105-14-91
Pubmed ID
Authors

Charlotte Soneson, Mauro Delorenzi

Abstract

Finding genes that are differentially expressed between conditions is an integral part of understanding the molecular basis of phenotypic variation. In the past decades, DNA microarrays have been used extensively to quantify the abundance of mRNA corresponding to different genes, and more recently high-throughput sequencing of cDNA (RNA-seq) has emerged as a powerful competitor. As the cost of sequencing decreases, it is conceivable that the use of RNA-seq for differential expression analysis will increase rapidly. To exploit the possibilities and address the challenges posed by this relatively new type of data, a number of software packages have been developed especially for differential expression analysis of RNA-seq data.

X Demographics

X Demographics

The data shown below were collected from the profiles of 80 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 2,577 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 60 2%
United Kingdom 24 <1%
Germany 19 <1%
Brazil 16 <1%
Spain 10 <1%
France 9 <1%
Sweden 7 <1%
Italy 6 <1%
Denmark 6 <1%
Other 63 2%
Unknown 2357 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 717 28%
Researcher 590 23%
Student > Master 373 14%
Student > Bachelor 172 7%
Student > Doctoral Student 129 5%
Other 341 13%
Unknown 255 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 1166 45%
Biochemistry, Genetics and Molecular Biology 542 21%
Computer Science 148 6%
Medicine and Dentistry 97 4%
Mathematics 53 2%
Other 252 10%
Unknown 319 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 79. 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 08 June 2021.
All research outputs
#544,143
of 25,408,670 outputs
Outputs from BMC Bioinformatics
#24
of 7,701 outputs
Outputs of similar age
#3,573
of 208,722 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 142 outputs
Altmetric has tracked 25,408,670 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 7,701 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 99% 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 208,722 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 142 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.