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Bacterial and viral identification and differentiation by amplicon sequencing on the MinION nanopore sequencer

Overview of attention for article published in Giga Science, March 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 (#25 of 1,172)
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

news
4 news outlets
blogs
3 blogs
twitter
112 X users
peer_reviews
1 peer review site
facebook
2 Facebook pages
googleplus
4 Google+ users

Citations

dimensions_citation
149 Dimensions

Readers on

mendeley
465 Mendeley
citeulike
3 CiteULike
Title
Bacterial and viral identification and differentiation by amplicon sequencing on the MinION nanopore sequencer
Published in
Giga Science, March 2015
DOI 10.1186/s13742-015-0051-z
Pubmed ID
Authors

Andy Kilianski, Jamie L Haas, Elizabeth J Corriveau, Alvin T Liem, Kristen L Willis, Dana R Kadavy, C Nicole Rosenzweig, Samuel S Minot

Abstract

The MinION™ nanopore sequencer was recently released to a community of alpha-testers for evaluation using a variety of sequencing applications. Recent reports have tested the ability of the MinION™ to act as a whole genome sequencer and have demonstrated that nanopore sequencing has tremendous potential utility. However, the current nanopore technology still has limitations with respect to error-rate, and this is problematic when attempting to assemble whole genomes without secondary rounds of sequencing to correct errors. In this study, we tested the ability of the MinION™ nanopore sequencer to accurately identify and differentiate bacterial and viral samples via directed sequencing of characteristic genes shared broadly across a target clade. Using a 6 hour sequencing run time, sufficient data were generated to identify an E. coli sample down to the species level from 16S rDNA amplicons. Three poxviruses (cowpox, vaccinia-MVA, and vaccinia-Lister) were identified and differentiated down to the strain level, despite over 98% identity between the vaccinia strains. The ability to differentiate strains by amplicon sequencing on the MinION™ was accomplished despite an observed per-base error rate of approximately 30%. While nanopore sequencing, using the MinION™ platform from Oxford Nanopore in particular, continues to mature into a commercially available technology, practical uses are sought for the current versions of the technology. This study offers evidence of the utility of amplicon sequencing by demonstrating that the current versions of MinION™ technology can accurately identify and differentiate both viral and bacterial species present within biological samples via amplicon sequencing.

X Demographics

X Demographics

The data shown below were collected from the profiles of 112 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 465 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 8 2%
Germany 6 1%
United Kingdom 3 <1%
Sweden 2 <1%
Brazil 2 <1%
Switzerland 1 <1%
Ireland 1 <1%
Chile 1 <1%
Hong Kong 1 <1%
Other 10 2%
Unknown 430 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 109 23%
Student > Ph. D. Student 88 19%
Student > Master 68 15%
Student > Bachelor 50 11%
Student > Postgraduate 20 4%
Other 77 17%
Unknown 53 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 166 36%
Biochemistry, Genetics and Molecular Biology 89 19%
Engineering 23 5%
Medicine and Dentistry 22 5%
Immunology and Microbiology 21 5%
Other 65 14%
Unknown 79 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 117. 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 30 October 2017.
All research outputs
#359,107
of 25,528,120 outputs
Outputs from Giga Science
#25
of 1,172 outputs
Outputs of similar age
#4,080
of 278,118 outputs
Outputs of similar age from Giga Science
#3
of 21 outputs
Altmetric has tracked 25,528,120 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,172 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 21.7. This one has done particularly well, scoring higher than 97% 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 278,118 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 98% of its contemporaries.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.