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Centroid based clustering of high throughput sequencing reads based on n-mer counts

Overview of attention for article published in BMC Bioinformatics, September 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Good Attention Score compared to outputs of the same age and source (69th percentile)

Mentioned by

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5 X users
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2 Google+ users
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1 research highlight platform

Citations

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15 Dimensions

Readers on

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59 Mendeley
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5 CiteULike
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Title
Centroid based clustering of high throughput sequencing reads based on n-mer counts
Published in
BMC Bioinformatics, September 2013
DOI 10.1186/1471-2105-14-268
Pubmed ID
Authors

Alexander Solovyov, W Ian Lipkin

Abstract

Many problems in computational biology require alignment-free sequence comparisons. One of the common tasks involving sequence comparison is sequence clustering. Here we apply methods of alignment-free comparison (in particular, comparison using sequence composition) to the challenge of sequence clustering.

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 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 7%
Brazil 2 3%
Japan 2 3%
Sweden 1 2%
Russia 1 2%
Unknown 49 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 29%
Student > Ph. D. Student 16 27%
Student > Master 8 14%
Professor > Associate Professor 4 7%
Student > Bachelor 3 5%
Other 8 14%
Unknown 3 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 24 41%
Computer Science 15 25%
Biochemistry, Genetics and Molecular Biology 9 15%
Immunology and Microbiology 2 3%
Medicine and Dentistry 2 3%
Other 3 5%
Unknown 4 7%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 12 March 2015.
All research outputs
#5,628,603
of 22,721,584 outputs
Outputs from BMC Bioinformatics
#2,083
of 7,260 outputs
Outputs of similar age
#47,745
of 197,573 outputs
Outputs of similar age from BMC Bioinformatics
#31
of 102 outputs
Altmetric has tracked 22,721,584 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,260 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 gotten more attention than average, scoring higher than 71% 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 197,573 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 75% of its contemporaries.
We're also able to compare this research output to 102 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 69% of its contemporaries.