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Improving RNA-Seq expression estimates by correcting for fragment bias

Overview of attention for article published in Genome Biology (Online Edition), January 2011
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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)

Mentioned by

blogs
1 blog
twitter
8 tweeters
patent
6 patents
q&a
1 Q&A thread

Citations

dimensions_citation
911 Dimensions

Readers on

mendeley
1684 Mendeley
citeulike
43 CiteULike
connotea
2 Connotea
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Title
Improving RNA-Seq expression estimates by correcting for fragment bias
Published in
Genome Biology (Online Edition), January 2011
DOI 10.1186/gb-2011-12-3-r22
Pubmed ID
Authors

Adam Roberts, Cole Trapnell, Julie Donaghey, John L Rinn, Lior Pachter

Abstract

The biochemistry of RNA-Seq library preparation results in cDNA fragments that are not uniformly distributed within the transcripts they represent. This non-uniformity must be accounted for when estimating expression levels, and we show how to perform the needed corrections using a likelihood based approach. We find improvements in expression estimates as measured by correlation with independently performed qRT-PCR and show that correction of bias leads to improved replicability of results across libraries and sequencing technologies.

Twitter Demographics

The data shown below were collected from the profiles of 8 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 1,684 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 71 4%
United Kingdom 18 1%
Germany 13 <1%
Brazil 11 <1%
France 7 <1%
Italy 6 <1%
China 6 <1%
Spain 5 <1%
Mexico 5 <1%
Other 53 3%
Unknown 1489 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 499 30%
Researcher 445 26%
Student > Master 163 10%
Student > Bachelor 109 6%
Student > Doctoral Student 81 5%
Other 290 17%
Unknown 97 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 949 56%
Biochemistry, Genetics and Molecular Biology 312 19%
Computer Science 95 6%
Medicine and Dentistry 42 2%
Mathematics 33 2%
Other 132 8%
Unknown 121 7%

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 22 April 2021.
All research outputs
#1,413,183
of 21,322,319 outputs
Outputs from Genome Biology (Online Edition)
#1,325
of 4,021 outputs
Outputs of similar age
#6,386
of 107,273 outputs
Outputs of similar age from Genome Biology (Online Edition)
#2
of 4 outputs
Altmetric has tracked 21,322,319 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 4,021 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.2. This one has gotten more attention than average, scoring higher than 67% 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 107,273 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 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.