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IUTA: a tool for effectively detecting differential isoform usage from RNA-Seq data

Overview of attention for article published in BMC Genomics, January 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 (90th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

blogs
1 blog
twitter
11 tweeters
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

dimensions_citation
18 Dimensions

Readers on

mendeley
59 Mendeley
citeulike
1 CiteULike
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Title
IUTA: a tool for effectively detecting differential isoform usage from RNA-Seq data
Published in
BMC Genomics, January 2014
DOI 10.1186/1471-2164-15-862
Pubmed ID
Authors

Liang Niu, Weichun Huang, David M Umbach, Leping Li

Abstract

Most genes in mammals generate several transcript isoforms that differ in stability and translational efficiency through alternative splicing. Such alternative splicing can be tissue- and developmental stage-specific, and such specificity is sometimes associated with disease. Thus, detecting differential isoform usage for a gene between tissues or cell lines/types (differences in the fraction of total expression of a gene represented by the expression of each of its isoforms) is potentially important for cell and developmental biology.

Twitter Demographics

The data shown below were collected from the profiles of 11 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 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 7%
Canada 1 2%
Brazil 1 2%
Unknown 53 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 36%
Researcher 14 24%
Student > Master 7 12%
Student > Bachelor 5 8%
Professor > Associate Professor 5 8%
Other 6 10%
Unknown 1 2%
Readers by discipline Count As %
Agricultural and Biological Sciences 29 49%
Biochemistry, Genetics and Molecular Biology 18 31%
Computer Science 5 8%
Medicine and Dentistry 4 7%
Decision Sciences 2 3%
Other 0 0%
Unknown 1 2%

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 17 January 2017.
All research outputs
#975,391
of 12,373,620 outputs
Outputs from BMC Genomics
#412
of 7,313 outputs
Outputs of similar age
#20,768
of 216,044 outputs
Outputs of similar age from BMC Genomics
#5
of 34 outputs
Altmetric has tracked 12,373,620 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,313 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done particularly well, scoring higher than 94% 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 216,044 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 90% of its contemporaries.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.