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Sifting through genomes with iterative-sequence clustering produces a large, phylogenetically diverse protein-family resource

Overview of attention for article published in BMC Bioinformatics, October 2012
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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 (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

blogs
1 blog
twitter
13 X users

Citations

dimensions_citation
21 Dimensions

Readers on

mendeley
109 Mendeley
citeulike
6 CiteULike
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Title
Sifting through genomes with iterative-sequence clustering produces a large, phylogenetically diverse protein-family resource
Published in
BMC Bioinformatics, October 2012
DOI 10.1186/1471-2105-13-264
Pubmed ID
Authors

Thomas J Sharpton, Guillaume Jospin, Dongying Wu, Morgan GI Langille, Katherine S Pollard, Jonathan A Eisen

Abstract

New computational resources are needed to manage the increasing volume of biological data from genome sequencing projects. One fundamental challenge is the ability to maintain a complete and current catalog of protein diversity. We developed a new approach for the identification of protein families that focuses on the rapid discovery of homologous protein sequences.

X Demographics

X Demographics

The data shown below were collected from the profiles of 13 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 109 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 11 10%
Brazil 2 2%
Netherlands 1 <1%
Sweden 1 <1%
United Kingdom 1 <1%
Australia 1 <1%
Russia 1 <1%
Canada 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 88 81%

Demographic breakdown

Readers by professional status Count As %
Researcher 40 37%
Student > Ph. D. Student 29 27%
Professor 8 7%
Student > Master 7 6%
Student > Bachelor 5 5%
Other 13 12%
Unknown 7 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 74 68%
Biochemistry, Genetics and Molecular Biology 8 7%
Computer Science 6 6%
Immunology and Microbiology 3 3%
Earth and Planetary Sciences 2 2%
Other 7 6%
Unknown 9 8%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 22 March 2019.
All research outputs
#1,779,120
of 22,681,577 outputs
Outputs from BMC Bioinformatics
#428
of 7,252 outputs
Outputs of similar age
#12,302
of 173,661 outputs
Outputs of similar age from BMC Bioinformatics
#4
of 111 outputs
Altmetric has tracked 22,681,577 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,252 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done particularly well, scoring higher than 94% 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 173,661 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 92% of its contemporaries.
We're also able to compare this research output to 111 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.