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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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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

blogs
1 blog
policy
1 policy source
twitter
60 tweeters
googleplus
1 Google+ user

Citations

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67 Dimensions

Readers on

mendeley
319 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.

Twitter Demographics

The data shown below were collected from the profiles of 60 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 319 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 317 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 74 23%
Student > Ph. D. Student 70 22%
Student > Master 37 12%
Student > Bachelor 28 9%
Other 19 6%
Other 42 13%
Unknown 49 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 75 24%
Biochemistry, Genetics and Molecular Biology 70 22%
Immunology and Microbiology 31 10%
Environmental Science 13 4%
Medicine and Dentistry 12 4%
Other 46 14%
Unknown 72 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 45. 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 01 January 2020.
All research outputs
#631,237
of 19,163,209 outputs
Outputs from Microbiome
#182
of 1,157 outputs
Outputs of similar age
#24,224
of 480,854 outputs
Outputs of similar age from Microbiome
#37
of 146 outputs
Altmetric has tracked 19,163,209 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 1,157 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 40.1. 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 480,854 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 94% of its contemporaries.
We're also able to compare this research output to 146 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.