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Improving RNA-Seq expression estimates by correcting for fragment bias

Overview of attention for article published in Genome Biology, March 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)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

1 blog
9 X users
6 patents
1 Q&A thread


1221 Dimensions

Readers on

1741 Mendeley
43 CiteULike
2 Connotea
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Improving RNA-Seq expression estimates by correcting for fragment bias
Published in
Genome Biology, March 2011
DOI 10.1186/gb-2011-12-3-r22
Pubmed ID

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


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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1,741 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 70 4%
United Kingdom 18 1%
Germany 13 <1%
Brazil 11 <1%
France 7 <1%
China 6 <1%
Italy 5 <1%
Spain 5 <1%
Mexico 5 <1%
Other 53 3%
Unknown 1548 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 505 29%
Researcher 450 26%
Student > Master 167 10%
Student > Bachelor 111 6%
Student > Doctoral Student 86 5%
Other 288 17%
Unknown 134 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 951 55%
Biochemistry, Genetics and Molecular Biology 327 19%
Computer Science 97 6%
Medicine and Dentistry 42 2%
Mathematics 33 2%
Other 132 8%
Unknown 159 9%
Attention Score in Context

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 14 January 2023.
All research outputs
of 25,371,288 outputs
Outputs from Genome Biology
of 4,467 outputs
Outputs of similar age
of 119,248 outputs
Outputs of similar age from Genome Biology
of 33 outputs
Altmetric has tracked 25,371,288 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 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. 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 119,248 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 33 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.