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Seq2Ref: a web server to facilitate functional interpretation

Overview of attention for article published in BMC Bioinformatics, January 2013
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Mentioned by

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5 X users
facebook
1 Facebook page

Citations

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8 Dimensions

Readers on

mendeley
43 Mendeley
citeulike
2 CiteULike
Title
Seq2Ref: a web server to facilitate functional interpretation
Published in
BMC Bioinformatics, January 2013
DOI 10.1186/1471-2105-14-30
Pubmed ID
Authors

Wenlin Li, Qian Cong, Lisa N Kinch, Nick V Grishin

Abstract

The size of the protein sequence database has been exponentially increasing due to advances in genome sequencing. However, experimentally characterized proteins only constitute a small portion of the database, such that the majority of sequences have been annotated by computational approaches. Current automatic annotation pipelines inevitably introduce errors, making the annotations unreliable. Instead of such error-prone automatic annotations, functional interpretation should rely on annotations of 'reference proteins' that have been experimentally characterized or manually curated.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 2%
Brazil 1 2%
India 1 2%
Czechia 1 2%
United Kingdom 1 2%
Unknown 38 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 35%
Professor > Associate Professor 7 16%
Student > Ph. D. Student 6 14%
Professor 4 9%
Student > Bachelor 3 7%
Other 7 16%
Unknown 1 2%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 58%
Biochemistry, Genetics and Molecular Biology 7 16%
Computer Science 3 7%
Medicine and Dentistry 3 7%
Engineering 3 7%
Other 1 2%
Unknown 1 2%
Attention Score in Context

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 17 April 2013.
All research outputs
#12,676,336
of 22,694,633 outputs
Outputs from BMC Bioinformatics
#3,619
of 7,254 outputs
Outputs of similar age
#150,528
of 282,151 outputs
Outputs of similar age from BMC Bioinformatics
#70
of 137 outputs
Altmetric has tracked 22,694,633 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,254 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 48th percentile – i.e., 48% 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 282,151 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
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 is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.