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Sample descriptors linked to metagenomic sequencing data from human and animal enteric samples from Vietnam.

ABSTRACT: There is still limited information on the diversity of viruses co-circulating in humans and animals. Here, we report data obtained from a large field collection of enteric samples taken from humans, pigs, rodents and other mammal hosts in Vietnam between 2012 and 2016. Each of 2100 stool or rectal swab samples was subjected to virally-enriched agnostic metagenomic sequencing; the short read sequence data are accessible from the European Nucleotide Archive (ENA). We link the sequence data to metadata on host type and demography and geographic location, distinguishing hospital patients, members of a cohort identified as a high risk of zoonotic infections (e.g. abattoir workers, rat traders) and animals. These data are suitable for further studies of virus diversity and virus discovery in humans and animals from Vietnam and to identify viruses found in multiple hosts that are potentially zoonotic.

SUBMITTER: Woolhouse M

PROVIDER: S-EPMC6794271 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Sample descriptors linked to metagenomic sequencing data from human and animal enteric samples from Vietnam.

Woolhouse Mark M Ashworth Jordan J Bogaardt Carlijn C Tue Ngo Tri NT Baker Steve S Thwaites Guy G Phuc Tran My TM

Scientific data 20191015 1

There is still limited information on the diversity of viruses co-circulating in humans and animals. Here, we report data obtained from a large field collection of enteric samples taken from humans, pigs, rodents and other mammal hosts in Vietnam between 2012 and 2016. Each of 2100 stool or rectal swab samples was subjected to virally-enriched agnostic metagenomic sequencing; the short read sequence data are accessible from the European Nucleotide Archive (ENA). We link the sequence data to meta ...[more]

PMID: 31615980

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Project description:BackgroundThe advent of metagenomic sequencing provides microbial abundance patterns that can be leveraged for sample origin prediction. Supervised machine learning classification approaches have been reported to predict sample origin accurately when the origin has been previously sampled. Using metagenomic datasets provided by the 2019 CAMDA challenge, we evaluated the influence of variable technical, analytical and machine learning approaches for result interpretation and novel source prediction.ResultsComparison between 16S rRNA amplicon and shotgun sequencing approaches as well as metagenomic analytical tools showed differences in normalized microbial abundance, especially for organisms present at low abundance. Shotgun sequence data analyzed using Kraken2 and Bracken, for taxonomic annotation, had higher detection sensitivity. As classification models are limited to labeling pre-trained origins, we took an alternative approach using Lasso-regularized multivariate regression to predict geographic coordinates for comparison. In both models, the prediction errors were much higher in Leave-1-city-out than in 10-fold cross validation, of which the former realistically forecasted the increased difficulty in accurately predicting samples from new origins. This challenge was further confirmed when applying the model to a set of samples obtained from new origins. Overall, the prediction performance of the regression and classification models, as measured by mean squared error, were comparable on mystery samples. Due to higher prediction error rates for samples from new origins, we provided an additional strategy based on prediction ambiguity to infer whether a sample is from a new origin. Lastly, we report increased prediction error when data from different sequencing protocols were included as training data.ConclusionsHerein, we highlight the capacity of predicting sample origin accurately with pre-trained origins and the challenge of predicting new origins through both regression and classification models. Overall, this work provides a summary of the impact of sequencing technique, protocol, taxonomic analytical approaches, and machine learning approaches on the use of metagenomics for prediction of sample origin.

Project description:Xylella fastidiosa (Xf) is a globally distributed plant-pathogenic bacterium. The primary control strategy for Xf diseases is eradicating infected plants; therefore, timely and accurate detection is necessary to prevent crop losses and further pathogen dispersal. Conventional Xf diagnostics primarily relies on quantitative PCR (qPCR) assays. However, these methods do not consider new or emerging variants due to pathogen genetic recombination and sensitivity limitations. We developed and tested a metagenomics pipeline using in-house short-read sequencing as a complementary approach for affordable, fast, and highly accurate Xf detection. We used metagenomics to identify Xf to the strain level in single- and mixed-infected plant samples at concentrations as low as 1 pg of bacterial DNA per gram of tissue. We also tested naturally infected samples from various plant species originating from Europe and the United States. We identified Xf subspecies in samples previously considered inconclusive with real-time PCR (quantification cycle [Cq], >35). Overall, we showed the versatility of the pipeline by using different plant hosts and DNA extraction methods. Our pipeline provides taxonomic and functional information for Xf diagnostics without extensive knowledge of the disease. This pipeline demonstrates that metagenomics can be used for early detection of Xf and incorporated as a tool to inform disease management strategies. IMPORTANCE Destructive Xylella fastidiosa (Xf) outbreaks in Europe highlight this pathogen's capacity to expand its host range and geographical distribution. The current disease diagnostic approaches are limited by a multiple-step process, biases to known sequences, and detection limits. We developed a low-cost, user-friendly metagenomic sequencing tool for Xf detection. In less than 3 days, we were able to identify Xf subspecies and strains in field-collected samples. Overall, our pipeline is a diagnostics tool that could be easily extended to other plant-pathogen interactions and implemented for emerging plant threat surveillance.

Dataset Information

Sample descriptors linked to metagenomic sequencing data from human and animal enteric samples from Vietnam.

Publications

Sample descriptors linked to metagenomic sequencing data from human and animal enteric samples from Vietnam.

Similar Datasets

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