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Finding the active genes in deep RNA-seq gene expression studies

Overview of attention for article published in BMC Genomics, January 2013
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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 (94th percentile)

Mentioned by

twitter
27 tweeters
googleplus
1 Google+ user

Citations

dimensions_citation
162 Dimensions

Readers on

mendeley
329 Mendeley
citeulike
1 CiteULike
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Title
Finding the active genes in deep RNA-seq gene expression studies
Published in
BMC Genomics, January 2013
DOI 10.1186/1471-2164-14-778
Pubmed ID
Authors

Traver Hart, H Komori, Sarah LaMere, Katie Podshivalova, Daniel R Salomon

Abstract

Early application of second-generation sequencing technologies to transcript quantitation (RNA-seq) has hinted at a vast mammalian transcriptome, including transcripts from nearly all known genes, which might be fully measured only by ultradeep sequencing. Subsequent studies suggested that low-abundance transcripts might be the result of technical or biological noise rather than active transcripts; moreover, most RNA-seq experiments did not provide enough read depth to generate high-confidence estimates of gene expression for low-abundance transcripts. As a result, the community adopted several heuristics for RNA-seq analysis, most notably an arbitrary expression threshold of 0.3 - 1 FPKM for downstream analysis. However, advances in RNA-seq library preparation, sequencing technology, and informatic analysis have addressed many of the systemic sources of uncertainty and undermined the assumptions that drove the adoption of these heuristics. We provide an updated view of the accuracy and efficiency of RNA-seq experiments, using genomic data from large-scale studies like the ENCODE project to provide orthogonal information against which to validate our conclusions.

Twitter Demographics

The data shown below were collected from the profiles of 27 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 329 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 2%
Spain 5 2%
Germany 3 <1%
Mexico 2 <1%
United Kingdom 2 <1%
Italy 1 <1%
Canada 1 <1%
Sweden 1 <1%
Korea, Republic of 1 <1%
Other 1 <1%
Unknown 307 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 86 26%
Student > Ph. D. Student 80 24%
Student > Master 30 9%
Professor > Associate Professor 23 7%
Student > Bachelor 22 7%
Other 48 15%
Unknown 40 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 148 45%
Biochemistry, Genetics and Molecular Biology 85 26%
Computer Science 9 3%
Medicine and Dentistry 9 3%
Immunology and Microbiology 7 2%
Other 22 7%
Unknown 49 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 06 November 2019.
All research outputs
#1,938,982
of 21,742,867 outputs
Outputs from BMC Genomics
#559
of 10,390 outputs
Outputs of similar age
#20,023
of 209,651 outputs
Outputs of similar age from BMC Genomics
#31
of 508 outputs
Altmetric has tracked 21,742,867 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,390 research outputs from this source. They receive a mean Attention Score of 4.6. 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 209,651 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 508 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.