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Psiscan: a computational approach to identify H/ACA-like and AGA-like non-coding RNA in trypanosomatid genomes

Overview of attention for article published in BMC Bioinformatics, January 2008
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  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

Mentioned by

wikipedia
13 Wikipedia pages

Citations

dimensions_citation
14 Dimensions

Readers on

mendeley
27 Mendeley
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Title
Psiscan: a computational approach to identify H/ACA-like and AGA-like non-coding RNA in trypanosomatid genomes
Published in
BMC Bioinformatics, January 2008
DOI 10.1186/1471-2105-9-471
Pubmed ID
Authors

Inna Myslyuk, Tirza Doniger, Yair Horesh, Avraham Hury, Ran Hoffer, Yaara Ziporen, Shulamit Michaeli, Ron Unger

Abstract

Detection of non coding RNA (ncRNA) molecules is a major bioinformatics challenge. This challenge is particularly difficult when attempting to detect H/ACA molecules which are involved in converting uridine to pseudouridine on rRNA in trypanosomes, because these organisms have unique H/ACA molecules (termed H/ACA-like) that lack several of the features that characterize H/ACA molecules in most other organisms.

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 4%
Unknown 26 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 22%
Researcher 5 19%
Professor > Associate Professor 4 15%
Student > Master 4 15%
Other 2 7%
Other 5 19%
Unknown 1 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 37%
Biochemistry, Genetics and Molecular Biology 6 22%
Computer Science 3 11%
Medicine and Dentistry 2 7%
Immunology and Microbiology 2 7%
Other 2 7%
Unknown 2 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 23 August 2011.
All research outputs
#816,273
of 3,629,910 outputs
Outputs from BMC Bioinformatics
#787
of 2,289 outputs
Outputs of similar age
#24,858
of 95,478 outputs
Outputs of similar age from BMC Bioinformatics
#45
of 137 outputs
Altmetric has tracked 3,629,910 research outputs across all sources so far. This one has received more attention than most of these and is in the 63rd percentile.
So far Altmetric has tracked 2,289 research outputs from this source. They receive a mean Attention Score of 4.3. This one has gotten more attention than average, scoring higher than 58% 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 95,478 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 137 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 64% of its contemporaries.