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Rapid metagenomic identification of viral pathogens in clinical samples by real-time nanopore sequencing analysis

Overview of attention for article published in Genome Medicine, September 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#22 of 1,357)
  • High Attention Score compared to outputs of the same age (99th percentile)

Mentioned by

news
20 news outlets
blogs
2 blogs
twitter
103 tweeters
patent
3 patents
facebook
1 Facebook page
wikipedia
7 Wikipedia pages

Citations

dimensions_citation
387 Dimensions

Readers on

mendeley
892 Mendeley
Title
Rapid metagenomic identification of viral pathogens in clinical samples by real-time nanopore sequencing analysis
Published in
Genome Medicine, September 2015
DOI 10.1186/s13073-015-0220-9
Pubmed ID
Authors

Alexander L. Greninger, Samia N. Naccache, Scot Federman, Guixia Yu, Placide Mbala, Vanessa Bres, Doug Stryke, Jerome Bouquet, Sneha Somasekar, Jeffrey M. Linnen, Roger Dodd, Prime Mulembakani, Bradley S. Schneider, Jean-Jacques Muyembe-Tamfum, Susan L. Stramer, Charles Y. Chiu

Abstract

We report unbiased metagenomic detection of chikungunya virus (CHIKV), Ebola virus (EBOV), and hepatitis C virus (HCV) from four human blood samples by MinION nanopore sequencing coupled to a newly developed, web-based pipeline for real-time bioinformatics analysis on a computational server or laptop (MetaPORE). At titers ranging from 10(7)-10(8) copies per milliliter, reads to EBOV from two patients with acute hemorrhagic fever and CHIKV from an asymptomatic blood donor were detected within 4 to 10 min of data acquisition, while lower titer HCV virus (1 × 10(5) copies per milliliter) was detected within 40 min. Analysis of mapped nanopore reads alone, despite an average individual error rate of 24 % (range 8-49 %), permitted identification of the correct viral strain in all four isolates, and 90 % of the genome of CHIKV was recovered with 97-99 % accuracy. Using nanopore sequencing, metagenomic detection of viral pathogens directly from clinical samples was performed within an unprecedented <6 hr sample-to-answer turnaround time, and in a timeframe amenable to actionable clinical and public health diagnostics.

Twitter Demographics

The data shown below were collected from the profiles of 103 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 892 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 7 <1%
United Kingdom 6 <1%
Germany 5 <1%
United States 5 <1%
Spain 2 <1%
Japan 2 <1%
Korea, Republic of 2 <1%
Sweden 1 <1%
Israel 1 <1%
Other 7 <1%
Unknown 854 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 192 22%
Student > Ph. D. Student 158 18%
Student > Master 132 15%
Student > Bachelor 102 11%
Other 45 5%
Other 144 16%
Unknown 119 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 267 30%
Biochemistry, Genetics and Molecular Biology 212 24%
Medicine and Dentistry 67 8%
Immunology and Microbiology 56 6%
Computer Science 35 4%
Other 107 12%
Unknown 148 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 213. 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 16 December 2021.
All research outputs
#131,091
of 21,346,066 outputs
Outputs from Genome Medicine
#22
of 1,357 outputs
Outputs of similar age
#1,926
of 265,000 outputs
Outputs of similar age from Genome Medicine
#1
of 2 outputs
Altmetric has tracked 21,346,066 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,357 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.9. This one has done particularly well, scoring higher than 98% 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 265,000 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 99% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them