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Comprehensive DNA Signature Discovery and Validation

Overview of attention for article published in PLoS Computational Biology, May 2007
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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 (84th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

Mentioned by

blogs
1 blog
twitter
2 X users

Citations

dimensions_citation
63 Dimensions

Readers on

mendeley
93 Mendeley
citeulike
3 CiteULike
connotea
3 Connotea
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Title
Comprehensive DNA Signature Discovery and Validation
Published in
PLoS Computational Biology, May 2007
DOI 10.1371/journal.pcbi.0030098
Pubmed ID
Authors

Adam M Phillippy, Jacquline A Mason, Kunmi Ayanbule, Daniel D Sommer, Elisa Taviani, Anwar Huq, Rita R Colwell, Ivor T Knight, Steven L Salzberg

Abstract

DNA signatures are nucleotide sequences that can be used to detect the presence of an organism and to distinguish that organism from all other species. Here we describe Insignia, a new, comprehensive system for the rapid identification of signatures in the genomes of bacteria and viruses. With the availability of hundreds of complete bacterial and viral genome sequences, it is now possible to use computational methods to identify signature sequences in all of these species, and to use these signatures as the basis for diagnostic assays to detect and genotype microbes in both environmental and clinical samples. The success of such assays critically depends on the methods used to identify signatures that properly differentiate between the target genomes and the sample background. We have used Insignia to compute accurate signatures for most bacterial genomes and made them available through our Web site. A sample of these signatures has been successfully tested on a set of 46 Vibrio cholerae strains, and the results indicate that the signatures are highly sensitive for detection as well as specific for discrimination between these strains and their near relatives. Our approach, whereby the entire genomic complement of organisms are compared to identify probe targets, is a promising method for diagnostic assay development, and it provides assay designers with the flexibility to choose probes from the most relevant genes or genomic regions. The Insignia system is freely accessible via a Web interface and has been released as open source software at: http://insignia.cbcb.umd.edu.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 93 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 8%
Chile 1 1%
Mexico 1 1%
Sweden 1 1%
Thailand 1 1%
China 1 1%
Unknown 81 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 28 30%
Student > Ph. D. Student 23 25%
Student > Master 8 9%
Student > Bachelor 8 9%
Professor > Associate Professor 6 6%
Other 15 16%
Unknown 5 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 45 48%
Biochemistry, Genetics and Molecular Biology 15 16%
Computer Science 9 10%
Immunology and Microbiology 6 6%
Medicine and Dentistry 5 5%
Other 8 9%
Unknown 5 5%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 2015.
All research outputs
#4,367,137
of 25,371,288 outputs
Outputs from PLoS Computational Biology
#3,579
of 8,958 outputs
Outputs of similar age
#13,469
of 84,366 outputs
Outputs of similar age from PLoS Computational Biology
#12
of 30 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,958 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 60% 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 84,366 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 84% of its contemporaries.
We're also able to compare this research output to 30 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 60% of its contemporaries.