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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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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.

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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%