Title |
A Universal Method for Species Identification of Mammals Utilizing Next Generation Sequencing for the Analysis of DNA Mixtures
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Published in |
PLOS ONE, December 2013
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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. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 3 | 27% |
United Kingdom | 1 | 9% |
Ecuador | 1 | 9% |
France | 1 | 9% |
Mexico | 1 | 9% |
Unknown | 4 | 36% |
Demographic breakdown
Type | Count | As % |
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Scientists | 7 | 64% |
Members of the public | 4 | 36% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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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% |