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Mendeley readers
Attention Score in Context
Title |
Improving RNA-Seq expression estimates by correcting for fragment bias
|
---|---|
Published in |
Genome Biology, 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. |
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.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 4 | 44% |
Australia | 1 | 11% |
Unknown | 4 | 44% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 5 | 56% |
Members of the public | 4 | 44% |
Mendeley readers
The data shown below were compiled from readership statistics for 1,723 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 | 1528 | 89% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 503 | 29% |
Researcher | 448 | 26% |
Student > Master | 166 | 10% |
Student > Bachelor | 110 | 6% |
Student > Doctoral Student | 86 | 5% |
Other | 293 | 17% |
Unknown | 117 | 7% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 950 | 55% |
Biochemistry, Genetics and Molecular Biology | 323 | 19% |
Computer Science | 96 | 6% |
Medicine and Dentistry | 42 | 2% |
Mathematics | 33 | 2% |
Other | 136 | 8% |
Unknown | 143 | 8% |
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
#1,647,827
of 24,071,812 outputs
Outputs from Genome Biology
#1,420
of 4,290 outputs
Outputs of similar age
#8,969
of 187,111 outputs
Outputs of similar age from Genome Biology
#28
of 140 outputs
Altmetric has tracked 24,071,812 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 4,290 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.9. This one has gotten more attention than average, scoring higher than 66% 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 187,111 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 95% of its contemporaries.
We're also able to compare this research output to 140 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.