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Gene ontology analysis for RNA-seq: accounting for selection bias

Overview of attention for article published in Genome Biology (Online Edition), January 2010
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (98th percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
twitter
77 tweeters
patent
1 patent
wikipedia
2 Wikipedia pages
googleplus
1 Google+ user
q&a
1 Q&A thread

Citations

dimensions_citation
2923 Dimensions

Readers on

mendeley
1922 Mendeley
citeulike
42 CiteULike
connotea
3 Connotea
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Title
Gene ontology analysis for RNA-seq: accounting for selection bias
Published in
Genome Biology (Online Edition), January 2010
DOI 10.1186/gb-2010-11-2-r14
Pubmed ID
Authors

Matthew D Young, Matthew J Wakefield, Gordon K Smyth, Alicia Oshlack

Abstract

We present GOseq, an application for performing Gene Ontology (GO) analysis on RNA-seq data. GO analysis is widely used to reduce complexity and highlight biological processes in genome-wide expression studies, but standard methods give biased results on RNA-seq data due to over-detection of differential expression for long and highly expressed transcripts. Application of GOseq to a prostate cancer data set shows that GOseq dramatically changes the results, highlighting categories more consistent with the known biology.

Twitter Demographics

The data shown below were collected from the profiles of 77 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 1,922 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 50 3%
United Kingdom 13 <1%
Germany 11 <1%
Brazil 9 <1%
Italy 8 <1%
Mexico 7 <1%
France 5 <1%
Spain 5 <1%
Belgium 4 <1%
Other 44 2%
Unknown 1766 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 544 28%
Researcher 492 26%
Student > Master 240 12%
Student > Bachelor 130 7%
Student > Doctoral Student 103 5%
Other 269 14%
Unknown 144 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 1014 53%
Biochemistry, Genetics and Molecular Biology 351 18%
Medicine and Dentistry 71 4%
Computer Science 70 4%
Environmental Science 35 2%
Other 186 10%
Unknown 195 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 65. 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 05 November 2020.
All research outputs
#375,223
of 16,760,518 outputs
Outputs from Genome Biology (Online Edition)
#315
of 3,528 outputs
Outputs of similar age
#1,636
of 99,635 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 2 outputs
Altmetric has tracked 16,760,518 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 3,528 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.3. This one has done particularly well, scoring higher than 91% 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 99,635 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 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them