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A Universal Method for Species Identification of Mammals Utilizing Next Generation Sequencing for the Analysis of DNA Mixtures

Overview of attention for article published in PLOS ONE, December 2013
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Title
A Universal Method for Species Identification of Mammals Utilizing Next Generation Sequencing for the Analysis of DNA Mixtures
Published in
PLOS ONE, December 2013
DOI 10.1371/journal.pone.0083761
Pubmed ID
Authors

Andreas O. Tillmar, Barbara Dell'Amico, Jenny Welander, Gunilla Holmlund

Abstract

Species identification can be interesting in a wide range of areas, for example, in forensic applications, food monitoring and in archeology. The vast majority of existing DNA typing methods developed for species determination, mainly focuses on a single species source. There are, however, many instances where all species from mixed sources need to be determined, even when the species in minority constitutes less than 1 % of the sample. The introduction of next generation sequencing opens new possibilities for such challenging samples. In this study we present a universal deep sequencing method using 454 GS Junior sequencing of a target on the mitochondrial gene 16S rRNA. The method was designed through phylogenetic analyses of DNA reference sequences from more than 300 mammal species. Experiments were performed on artificial species-species mixture samples in order to verify the method's robustness and its ability to detect all species within a mixture. The method was also tested on samples from authentic forensic casework. The results showed to be promising, discriminating over 99.9 % of mammal species and the ability to detect multiple donors within a mixture and also to detect minor components as low as 1 % of a mixed sample.

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Geographical breakdown

Country Count As %
United States 4 2%
South Africa 2 <1%
Portugal 1 <1%
Germany 1 <1%
Uruguay 1 <1%
Hungary 1 <1%
Spain 1 <1%
Malaysia 1 <1%
Unknown 199 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 44 21%
Researcher 35 17%
Student > Master 31 15%
Student > Bachelor 21 10%
Other 14 7%
Other 32 15%
Unknown 34 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 82 39%
Biochemistry, Genetics and Molecular Biology 46 22%
Environmental Science 13 6%
Chemistry 5 2%
Computer Science 3 1%
Other 18 9%
Unknown 44 21%