GET /metagenomics/api/v1/samples/ERS3886136/studies?format=api
HTTP 200 OK
Allow: GET, HEAD, OPTIONS
Content-Type: application/json
Vary: Accept
{
"links": {
"first": "https://www.ebi.ac.uk/metagenomics/api/v1/samples/ERS3886136/studies?format=api&page=1",
"last": "https://www.ebi.ac.uk/metagenomics/api/v1/samples/ERS3886136/studies?format=api&page=1",
"next": null,
"prev": null
},
"data": [
{
"type": "studies",
"id": "MGYS00005128",
"attributes": {
"bioproject": "PRJEB34871",
"samples-count": 1197,
"accession": "MGYS00005128",
"is-private": false,
"last-update": "2020-02-04T14:43:01",
"secondary-accession": "ERP117839",
"centre-name": "CENTRE FOR ECOLOGY AND HYDROLOGY",
"public-release-date": null,
"study-abstract": "Antimicrobial resistance (AMR) is a global health threat, especially in low-/middle-income countries (LMICs), where there is limited surveillance to inform empiric antibiotic treatment guidelines. Enterobacterales are a particular AMR problem, and a common cause of disease. We developed a novel approach for AMR surveillance in Enterobacterales by profiling metagenomes of pooled human faecal material from three sites (n=563 individuals; Cambodia, Kenya, UK) to derive a taxonomy-adjusted AMR metric which could be used to predict the aggregate burden of resistant Enterobacterales infections within each setting. Samples were sequenced (Illumina); taxonomic and resistance gene profiling was performed using ResPipe (https://gitlab.com/hsgweon/ResPipe). Data on organisms causing bacteraemia and meningitis and susceptibility results from 2010-2017 were collated for each site. Bayesian generalised linear models with a binomial likelihood were fitted to determine the capacity of the metric to predict AMR in Enterobacterales infections in each setting. The most informative model predicted the proportion of resistant infections in the target populations for 14/14 of antibiotics in the UK, 12/12 in Kenya, and 8/12 in Cambodia. Intermittent metagenomics of pooled human samples could be a powerful tool in determining and monitoring the burden of AMR in clinical infections, especially in resource-limited settings.",
"study-name": "Predicting antibiotic resistance in clinical infections caused by Enterobacterales from aggregated population-level taxonomy-adjusted AMR estimates derived from quantitative metagenomic analysis of pooled human faecal samples",
"data-origination": "SUBMITTED"
},
"relationships": {
"downloads": {
"links": {
"related": "https://www.ebi.ac.uk/metagenomics/api/v1/studies/MGYS00005128/downloads?format=api"
}
},
"publications": {
"links": {
"related": "https://www.ebi.ac.uk/metagenomics/api/v1/studies/MGYS00005128/publications?format=api"
}
},
"geocoordinates": {
"links": {
"related": "https://www.ebi.ac.uk/metagenomics/api/v1/studies/MGYS00005128/geocoordinates?format=api"
}
},
"analyses": {
"links": {
"related": "https://www.ebi.ac.uk/metagenomics/api/v1/studies/MGYS00005128/analyses?format=api"
}
},
"samples": {
"links": {
"related": "https://www.ebi.ac.uk/metagenomics/api/v1/studies/MGYS00005128/samples?format=api"
}
},
"biomes": {
"links": {
"related": "https://www.ebi.ac.uk/metagenomics/api/v1/studies/MGYS00005128/biomes?format=api"
},
"data": [
{
"type": "biomes",
"id": "root:Host-associated:Human:Digestive system:Large intestine:Fecal",
"links": {
"self": "https://www.ebi.ac.uk/metagenomics/api/v1/biomes/root:Host-associated:Human:Digestive%20system:Large%20intestine:Fecal?format=api"
}
}
]
}
},
"links": {
"self": "https://www.ebi.ac.uk/metagenomics/api/v1/studies/MGYS00005128?format=api"
}
}
],
"meta": {
"pagination": {
"page": 1,
"pages": 1,
"count": 1
}
}
}