16S rRNA Amplicon Sequencing for Epidemiological Surveys of Bacteria in Wildlife
0301 basic medicine
Biodiversité et Ecologie
http://aims.fao.org/aos/agrovoc/c_13948
bat
L73 - Maladies des animaux
molecular epidemiology
630
http://aims.fao.org/aos/agrovoc/c_16411
bacteria;emerging infectious diseases;high-throughput sequencing;metabarcoding;molecular epidemiology;rodents;West Africa;zoonoses
Chiroptera
maladie infectieuse
http://aims.fao.org/aos/agrovoc/c_8355
bacteria
Chordata
surveillance épidémiologique
http://aims.fao.org/aos/agrovoc/c_6970
0303 health sciences
http://aims.fao.org/aos/agrovoc/c_2329
high-throughput sequencing
Biodiversity
QR1-502
3. Good health
http://aims.fao.org/aos/agrovoc/c_5630
http://aims.fao.org/aos/agrovoc/c_0a9daef1
rodents
Mammalia
bactérie pathogène
http://aims.fao.org/aos/agrovoc/c_34317
L72 - Organismes nuisibles des animaux
agent pathogène
Research Article
zoonose
570
http://aims.fao.org/aos/agrovoc/c_28665
http://aims.fao.org/aos/agrovoc/c_10b87fa7
relation homme-faune
bats
http://aims.fao.org/aos/agrovoc/c_27813
Microbiology
emerging infectious diseases
Biodiversity and Ecology
03 medical and health sciences
http://aims.fao.org/aos/agrovoc/c_15663
http://aims.fao.org/aos/agrovoc/c_34024
West Africa
séquençage à haut débit
Animalia
http://aims.fao.org/aos/agrovoc/c_8530
rongeur
Rattus
L10 - Génétique et amélioration des animaux
zoonoses
transmission des maladies
[SDE.BE] Environmental Sciences/Biodiversity and Ecology
Enquête pathologique
séquence d'arn
metabarcoding
next-generation sequencing
[SDE.BE]Environmental Sciences/Biodiversity and Ecology
DOI:
10.1128/msystems.00032-16
Publication Date:
2016-07-20T02:04:09Z
AUTHORS (13)
ABSTRACT
Several recent public health crises have shown that the surveillance of zoonotic agents in wildlife is important to prevent pandemic risks. High-throughput sequencing (HTS) technologies are potentially useful for this surveillance, but rigorous experimental processes are required for the use of these effective tools in such epidemiological contexts. In particular, HTS introduces biases into the raw data set that might lead to incorrect interpretations. We describe here a procedure for cleaning data before estimating reliable biological parameters, such as positivity, prevalence, and coinfection, using 16S rRNA amplicon sequencing on an Illumina MiSeq platform. This procedure, applied to 711 rodents collected in West Africa, detected several zoonotic bacterial species, including some at high prevalence, despite their never before having been reported for West Africa. In the future, this approach could be adapted for the monitoring of other microbes such as protists, fungi, and even viruses.
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CITATIONS (97)
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