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Efficient oligonucleotide probe selection for pan-genomic tiling arrays

Overview of attention for article published in BMC Bioinformatics, September 2009
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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2 tweeters

Citations

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24 Dimensions

Readers on

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34 Mendeley
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1 Connotea
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Title
Efficient oligonucleotide probe selection for pan-genomic tiling arrays
Published in
BMC Bioinformatics, September 2009
DOI 10.1186/1471-2105-10-293
Pubmed ID
Authors

Adam M Phillippy, Xiangyu Deng, Wei Zhang, Steven L Salzberg

Abstract

Array comparative genomic hybridization is a fast and cost-effective method for detecting, genotyping, and comparing the genomic sequence of unknown bacterial isolates. This method, as with all microarray applications, requires adequate coverage of probes targeting the regions of interest. An unbiased tiling of probes across the entire length of the genome is the most flexible design approach. However, such a whole-genome tiling requires that the genome sequence is known in advance. For the accurate analysis of uncharacterized bacteria, an array must query a fully representative set of sequences from the species' pan-genome. Prior microarrays have included only a single strain per array or the conserved sequences of gene families. These arrays omit potentially important genes and sequence variants from the pan-genome.

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

Geographical breakdown

Country Count As %
United States 2 6%
Denmark 1 3%
Finland 1 3%
Japan 1 3%
Netherlands 1 3%
Unknown 28 82%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 32%
Student > Ph. D. Student 10 29%
Student > Master 3 9%
Student > Bachelor 2 6%
Professor > Associate Professor 2 6%
Other 3 9%
Unknown 3 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 62%
Biochemistry, Genetics and Molecular Biology 3 9%
Computer Science 2 6%
Mathematics 1 3%
Environmental Science 1 3%
Other 2 6%
Unknown 4 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 10 February 2014.
All research outputs
#2,687,201
of 6,230,816 outputs
Outputs from BMC Bioinformatics
#2,022
of 3,133 outputs
Outputs of similar age
#68,572
of 154,900 outputs
Outputs of similar age from BMC Bioinformatics
#69
of 121 outputs
Altmetric has tracked 6,230,816 research outputs across all sources so far. This one has received more attention than most of these and is in the 56th percentile.
So far Altmetric has tracked 3,133 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 34th percentile – i.e., 34% 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 154,900 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.
We're also able to compare this research output to 121 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.