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In-depth resistome analysis by targeted metagenomics

Overview of attention for article published in Microbiome, January 2018
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

blogs
1 blog
policy
1 policy source
twitter
55 X users
googleplus
1 Google+ user

Citations

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117 Dimensions

Readers on

mendeley
415 Mendeley
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Title
In-depth resistome analysis by targeted metagenomics
Published in
Microbiome, January 2018
DOI 10.1186/s40168-017-0387-y
Pubmed ID
Authors

Val F. Lanza, Fernando Baquero, José Luís Martínez, Ricardo Ramos-Ruíz, Bruno González-Zorn, Antoine Andremont, Antonio Sánchez-Valenzuela, Stanislav Dusko Ehrlich, Sean Kennedy, Etienne Ruppé, Willem van Schaik, Rob J. Willems, Fernando de la Cruz, Teresa M. Coque

Abstract

Antimicrobial resistance is a major global health challenge. Metagenomics allows analyzing the presence and dynamics of "resistomes" (the ensemble of genes encoding antimicrobial resistance in a given microbiome) in disparate microbial ecosystems. However, the low sensitivity and specificity of available metagenomic methods preclude the detection of minority populations (often present below their detection threshold) and/or the identification of allelic variants that differ in the resulting phenotype. Here, we describe a novel strategy that combines targeted metagenomics using last generation in-solution capture platforms, with novel bioinformatics tools to establish a standardized framework that allows both quantitative and qualitative analyses of resistomes. We developed ResCap, a targeted sequence capture platform based on SeqCapEZ (NimbleGene) technology, which includes probes for 8667 canonical resistance genes (7963 antibiotic resistance genes and 704 genes conferring resistance to metals or biocides), and 2517 relaxase genes (plasmid markers) and 78,600 genes homologous to the previous identified targets (47,806 for antibiotics and 30,794 for biocides or metals). Its performance was compared with metagenomic shotgun sequencing (MSS) for 17 fecal samples (9 humans, 8 swine). ResCap significantly improves MSS to detect "gene abundance" (from 2.0 to 83.2%) and "gene diversity" (26 versus 14.9 genes unequivocally detected per sample per million of reads; the number of reads unequivocally mapped increasing up to 300-fold by using ResCap), which were calculated using novel bioinformatic tools. ResCap also facilitated the analysis of novel genes potentially involved in the resistance to antibiotics, metals, biocides, or any combination thereof. ResCap, the first targeted sequence capture, specifically developed to analyze resistomes, greatly enhances the sensitivity and specificity of available metagenomic methods and offers the possibility to analyze genes related to the selection and transfer of antimicrobial resistance (biocides, heavy metals, plasmids). The model opens the possibility to study other complex microbial systems in which minority populations play a relevant role.

X Demographics

X Demographics

The data shown below were collected from the profiles of 55 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 415 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sweden 1 <1%
China 1 <1%
Unknown 413 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 90 22%
Researcher 88 21%
Student > Master 43 10%
Student > Bachelor 31 7%
Other 27 7%
Other 51 12%
Unknown 85 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 92 22%
Agricultural and Biological Sciences 85 20%
Immunology and Microbiology 39 9%
Medicine and Dentistry 17 4%
Environmental Science 15 4%
Other 53 13%
Unknown 114 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 43. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 September 2020.
All research outputs
#973,408
of 25,711,518 outputs
Outputs from Microbiome
#270
of 1,790 outputs
Outputs of similar age
#23,219
of 471,475 outputs
Outputs of similar age from Microbiome
#10
of 54 outputs
Altmetric has tracked 25,711,518 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,790 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 37.9. This one has done well, scoring higher than 84% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 471,475 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 54 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.