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Optimization and validation of sample preparation for metagenomic sequencing of viruses in clinical samples

Overview of attention for article published in Microbiome, August 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

Mentioned by

blogs
1 blog
twitter
33 tweeters
patent
1 patent

Citations

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41 Dimensions

Readers on

mendeley
168 Mendeley
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Title
Optimization and validation of sample preparation for metagenomic sequencing of viruses in clinical samples
Published in
Microbiome, August 2017
DOI 10.1186/s40168-017-0317-z
Pubmed ID
Authors

Dagmara W. Lewandowska, Osvaldo Zagordi, Fabienne-Desirée Geissberger, Verena Kufner, Stefan Schmutz, Jürg Böni, Karin J. Metzner, Alexandra Trkola, Michael Huber

Abstract

Sequence-specific PCR is the most common approach for virus identification in diagnostic laboratories. However, as specific PCR only detects pre-defined targets, novel virus strains or viruses not included in routine test panels will be missed. Recently, advances in high-throughput sequencing allow for virus-sequence-independent identification of entire virus populations in clinical samples, yet standardized protocols are needed to allow broad application in clinical diagnostics. Here, we describe a comprehensive sample preparation protocol for high-throughput metagenomic virus sequencing using random amplification of total nucleic acids from clinical samples. In order to optimize metagenomic sequencing for application in virus diagnostics, we tested different enrichment and amplification procedures on plasma samples spiked with RNA and DNA viruses. A protocol including filtration, nuclease digestion, and random amplification of RNA and DNA in separate reactions provided the best results, allowing reliable recovery of viral genomes and a good correlation of the relative number of sequencing reads with the virus input. We further validated our method by sequencing a multiplexed viral pathogen reagent containing a range of human viruses from different virus families. Our method proved successful in detecting the majority of the included viruses with high read numbers and compared well to other protocols in the field validated against the same reference reagent. Our sequencing protocol does work not only with plasma but also with other clinical samples such as urine and throat swabs. The workflow for virus metagenomic sequencing that we established proved successful in detecting a variety of viruses in different clinical samples. Our protocol supplements existing virus-specific detection strategies providing opportunities to identify atypical and novel viruses commonly not accounted for in routine diagnostic panels.

Twitter Demographics

Twitter Demographics

The data shown below were collected from the profiles of 33 tweeters 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 168 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 168 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 34 20%
Student > Ph. D. Student 32 19%
Student > Master 19 11%
Student > Bachelor 15 9%
Student > Postgraduate 9 5%
Other 22 13%
Unknown 37 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 38 23%
Biochemistry, Genetics and Molecular Biology 37 22%
Immunology and Microbiology 15 9%
Medicine and Dentistry 13 8%
Engineering 6 4%
Other 13 8%
Unknown 46 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 28. 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 17 January 2023.
All research outputs
#1,318,547
of 24,417,958 outputs
Outputs from Microbiome
#449
of 1,642 outputs
Outputs of similar age
#26,919
of 321,624 outputs
Outputs of similar age from Microbiome
#25
of 64 outputs
Altmetric has tracked 24,417,958 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,642 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 38.9. This one has gotten more attention than average, scoring higher than 72% 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 321,624 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 91% of its contemporaries.
We're also able to compare this research output to 64 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.