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

Overview of attention for article published in BMC Genomics, November 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 (91st percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

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25 X users
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1 patent
googleplus
1 Google+ user

Citations

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202 Dimensions

Readers on

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

Traver Hart, H Kiyomi 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 25 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 349 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 1%
Spain 5 1%
Germany 2 <1%
Mexico 2 <1%
Italy 1 <1%
Sweden 1 <1%
Canada 1 <1%
Korea, Republic of 1 <1%
United Kingdom 1 <1%
Other 1 <1%
Unknown 329 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 87 25%
Student > Ph. D. Student 82 23%
Student > Master 34 10%
Student > Bachelor 25 7%
Professor > Associate Professor 23 7%
Other 51 15%
Unknown 47 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 150 43%
Biochemistry, Genetics and Molecular Biology 93 27%
Computer Science 10 3%
Medicine and Dentistry 9 3%
Immunology and Microbiology 8 2%
Other 23 7%
Unknown 56 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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
#2,138,077
of 25,373,627 outputs
Outputs from BMC Genomics
#518
of 11,244 outputs
Outputs of similar age
#19,382
of 225,254 outputs
Outputs of similar age from BMC Genomics
#12
of 222 outputs
Altmetric has tracked 25,373,627 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 11,244 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 95% 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 225,254 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 91% of its contemporaries.
We're also able to compare this research output to 222 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.