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AbsIDconvert: An absolute approach for converting genetic identifiers at different granularities

Overview of attention for article published in BMC Bioinformatics, January 2012
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  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
2 tweeters

Readers on

mendeley
25 Mendeley
citeulike
3 CiteULike
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Title
AbsIDconvert: An absolute approach for converting genetic identifiers at different granularities
Published in
BMC Bioinformatics, January 2012
DOI 10.1186/1471-2105-13-229
Pubmed ID
Authors

Fahim Mohammad, Robert M Flight, Benjamin J Harrison, Jeffrey C Petruska, Eric C Rouchka

Abstract

High-throughput molecular biology techniques yield vast amounts of data, often by detecting small portions of ribonucleotides corresponding to specific identifiers. Existing bioinformatic methodologies categorize and compare these elements using inferred descriptive annotation given this sequence information irrespective of the fact that it may not be representative of the identifier as a whole.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 4%
Luxembourg 1 4%
Unknown 23 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 20%
Other 4 16%
Student > Ph. D. Student 4 16%
Professor > Associate Professor 3 12%
Student > Bachelor 3 12%
Other 4 16%
Unknown 2 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 36%
Computer Science 5 20%
Biochemistry, Genetics and Molecular Biology 3 12%
Engineering 2 8%
Medicine and Dentistry 2 8%
Other 2 8%
Unknown 2 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 19 September 2012.
All research outputs
#7,762,556
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#3,175
of 4,588 outputs
Outputs of similar age
#70,352
of 126,134 outputs
Outputs of similar age from BMC Bioinformatics
#22
of 32 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,588 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 21st percentile – i.e., 21% of its peers scored the same or lower than it.
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 126,134 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 32 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.