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Ligation-free ribosome profiling of cell type-specific translation in the brain

Overview of attention for article published in Genome Biology, July 2016
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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 (87th percentile)
  • Average Attention Score compared to outputs of the same age and source

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Citations

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Title
Ligation-free ribosome profiling of cell type-specific translation in the brain
Published in
Genome Biology, July 2016
DOI 10.1186/s13059-016-1005-1
Pubmed ID
Authors

Nicholas Hornstein, Daniela Torres, Sohani Das Sharma, Guomei Tang, Peter Canoll, Peter A. Sims

Abstract

Ribosome profiling has emerged as a powerful tool for genome-wide measurements of translation, but library construction requires multiple ligation steps and remains cumbersome relative to more conventional deep-sequencing experiments. We report a new, ligation-free approach to ribosome profiling that does not require ligation. Library construction for ligation-free ribosome profiling can be completed in one day with as little as 1 ng of purified RNA footprints. We apply ligation-free ribosome profiling to mouse brain tissue to identify new patterns of cell type-specific translation and test its ability to identify translational targets of mTOR signaling in the brain.

X Demographics

X Demographics

The data shown below were collected from the profiles of 24 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 167 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 2%
Spain 1 <1%
Unknown 162 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 48 29%
Researcher 43 26%
Other 10 6%
Student > Master 8 5%
Student > Doctoral Student 7 4%
Other 31 19%
Unknown 20 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 70 42%
Agricultural and Biological Sciences 40 24%
Neuroscience 13 8%
Medicine and Dentistry 6 4%
Computer Science 3 2%
Other 9 5%
Unknown 26 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 16 March 2022.
All research outputs
#2,571,281
of 25,374,647 outputs
Outputs from Genome Biology
#2,063
of 4,467 outputs
Outputs of similar age
#45,082
of 370,454 outputs
Outputs of similar age from Genome Biology
#33
of 65 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 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 53% 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 370,454 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 65 others from the same source and published within six weeks on either side of this one. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.