Title |
Metagenomics for pathogen detection in public health
|
---|---|
Published in |
Genome Medicine, September 2013
|
DOI | 10.1186/gm485 |
Pubmed ID | |
Authors |
Ruth R Miller, Vincent Montoya, Jennifer L Gardy, David M Patrick, Patrick Tang |
Abstract |
Traditional pathogen detection methods in public health infectious disease surveillance rely upon the identification of agents that are already known to be associated with a particular clinical syndrome. The emerging field of metagenomics has the potential to revolutionize pathogen detection in public health laboratories by allowing the simultaneous detection of all microorganisms in a clinical sample, without a priori knowledge of their identities, through the use of next-generation DNA sequencing. A single metagenomics analysis has the potential to detect rare and novel pathogens, and to uncover the role of dysbiotic microbiomes in infectious and chronic human disease. Making use of advances in sequencing platforms and bioinformatics tools, recent studies have shown that metagenomics can even determine the whole-genome sequences of pathogens, allowing inferences about antibiotic resistance, virulence, evolution and transmission to be made. We are entering an era in which more novel infectious diseases will be identified through metagenomics-based methods than through traditional laboratory methods. The impetus is now on public health laboratories to integrate metagenomics techniques into their diagnostic arsenals. |
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Geographical breakdown
Country | Count | As % |
---|---|---|
Saudi Arabia | 1 | 9% |
United States | 1 | 9% |
Unknown | 9 | 82% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 8 | 73% |
Scientists | 3 | 27% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Brazil | 5 | <1% |
United States | 4 | <1% |
United Kingdom | 2 | <1% |
Turkey | 1 | <1% |
South Africa | 1 | <1% |
Portugal | 1 | <1% |
Spain | 1 | <1% |
Sweden | 1 | <1% |
Unknown | 525 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 109 | 20% |
Student > Ph. D. Student | 84 | 16% |
Student > Master | 68 | 13% |
Student > Bachelor | 68 | 13% |
Student > Doctoral Student | 30 | 6% |
Other | 84 | 16% |
Unknown | 98 | 18% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 158 | 29% |
Biochemistry, Genetics and Molecular Biology | 103 | 19% |
Medicine and Dentistry | 42 | 8% |
Immunology and Microbiology | 30 | 6% |
Veterinary Science and Veterinary Medicine | 15 | 3% |
Other | 67 | 12% |
Unknown | 126 | 23% |