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SNPselector: a web tool for selecting SNPs for genetic association studies

Overview of attention for article published in Bioinformatics, September 2005
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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 (89th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

blogs
1 blog
patent
3 patents

Readers on

mendeley
84 Mendeley
citeulike
1 CiteULike
connotea
1 Connotea
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Article details
Title
SNPselector: a web tool for selecting SNPs for genetic association studies
Published in
Bioinformatics, September 2005
DOI 10.1093/bioinformatics/bti682
Pubmed ID
Authors
Abstract

Single nucleotide polymorphisms (SNPs) are commonly used for association studies to find genes responsible for complex genetic diseases. With the recent advance of SNP technology, researchers are able to assay thousands of SNPs in a single experiment. But the process of manually choosing thousands of genotyping SNPs for tens or hundreds of genes is time consuming. We have developed a web-based program, SNPselector, to automate the process. SNPselector takes a list of gene names or a list of genomic regions as input and searches the Ensembl genes or genomic regions for available SNPs. It prioritizes these SNPs on their tagging for linkage disequilibrium, SNP allele frequencies and source, function, regulatory potential and repeat status. SNPselector outputs result in compressed Excel spreadsheet files for review by the user.

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United States 2 2%
Greece 1 1%
United Kingdom 1 1%
France 1 1%
Finland 1 1%
Germany 1 1%
Switzerland 1 1%
Unknown 76 90%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 22 26%
Professor > Associate Professor 15 18%
Student > Ph. D. Student 11 13%
Student > Master 6 7%
Other 4 5%
Other 16 19%
Unknown 10 12%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 37 44%
Biochemistry, Genetics and Molecular Biology 7 8%
Computer Science 7 8%
Medicine and Dentistry 7 8%
Neuroscience 4 5%
Other 6 7%
Unknown 16 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 07 April 2026.
All research outputs
#3,520,654
of 28,841,988 outputs
Outputs from Bioinformatics
#2,616
of 13,313 outputs
Outputs of similar age
#8,230
of 80,602 outputs
Outputs of similar age from Bioinformatics
#6
of 74 outputs
Altmetric has tracked 28,841,988 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,313 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one has done well, scoring higher than 79% 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 80,602 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 89% of its contemporaries.
We're also able to compare this research output to 74 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 91% of its contemporaries.