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Pseudo–Messenger RNA: Phantoms of the Transcriptome

Overview of attention for article published in PLoS Genetics, April 2006
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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 (93rd percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

blogs
1 blog
twitter
4 X users

Citations

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59 Dimensions

Readers on

mendeley
99 Mendeley
citeulike
1 CiteULike
connotea
3 Connotea
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Title
Pseudo–Messenger RNA: Phantoms of the Transcriptome
Published in
PLoS Genetics, April 2006
DOI 10.1371/journal.pgen.0020023
Pubmed ID
Authors

Martin C Frith, Laurens G Wilming, Alistair Forrest, Hideya Kawaji, Sin Lam Tan, Claes Wahlestedt, Vladimir B Bajic, Chikatoshi Kai, Jun Kawai, Piero Carninci, Yoshihide Hayashizaki, Timothy L Bailey, Lukasz Huminiecki

Abstract

The mammalian transcriptome harbours shadowy entities that resist classification and analysis. In analogy with pseudogenes, we define pseudo-messenger RNA to be RNA molecules that resemble protein-coding mRNA, but cannot encode full-length proteins owing to disruptions of the reading frame. Using a rigorous computational pipeline, which rules out sequencing errors, we identify 10,679 pseudo-messenger RNAs (approximately half of which are transposon-associated) among the 102,801 FANTOM3 mouse cDNAs: just over 10% of the FANTOM3 transcriptome. These comprise not only transcribed pseudogenes, but also disrupted splice variants of otherwise protein-coding genes. Some may encode truncated proteins, only a minority of which appear subject to nonsense-mediated decay. The presence of an excess of transcripts whose only disruptions are opal stop codons suggests that there are more selenoproteins than currently estimated. We also describe compensatory frameshifts, where a segment of the gene has changed frame but remains translatable. In summary, we survey a large class of non-standard but potentially functional transcripts that are likely to encode genetic information and effect biological processes in novel ways. Many of these transcripts do not correspond cleanly to any identifiable object in the genome, implying fundamental limits to the goal of annotating all functional elements at the genome sequence level.

X Demographics

X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 99 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sweden 3 3%
Brazil 2 2%
Germany 1 1%
South Africa 1 1%
United Kingdom 1 1%
Canada 1 1%
Spain 1 1%
Japan 1 1%
United States 1 1%
Other 0 0%
Unknown 87 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 30 30%
Student > Ph. D. Student 18 18%
Student > Master 10 10%
Professor 10 10%
Professor > Associate Professor 7 7%
Other 15 15%
Unknown 9 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 54 55%
Biochemistry, Genetics and Molecular Biology 20 20%
Medicine and Dentistry 4 4%
Neuroscience 3 3%
Computer Science 2 2%
Other 3 3%
Unknown 13 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 April 2016.
All research outputs
#2,655,815
of 25,374,647 outputs
Outputs from PLoS Genetics
#2,223
of 8,960 outputs
Outputs of similar age
#5,474
of 84,623 outputs
Outputs of similar age from PLoS Genetics
#9
of 27 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.7. This one has gotten more attention than average, scoring higher than 74% 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 84,623 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 93% of its contemporaries.
We're also able to compare this research output to 27 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.