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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 (#14 of 740)
  • High Attention Score compared to outputs of the same age (98th percentile)

Mentioned by

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

Citations

dimensions_citation
109 Dimensions

Readers on

mendeley
382 Mendeley
citeulike
1 CiteULike
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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.

Twitter Demographics

The data shown below were collected from the profiles of 124 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 382 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 2%
United Kingdom 3 <1%
Brazil 2 <1%
Sweden 2 <1%
Portugal 1 <1%
Ireland 1 <1%
Italy 1 <1%
Hong Kong 1 <1%
Other 10 3%
Unknown 347 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 100 26%
Student > Ph. D. Student 83 22%
Student > Master 56 15%
Student > Bachelor 43 11%
Student > Postgraduate 19 5%
Other 61 16%
Unknown 20 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 157 41%
Biochemistry, Genetics and Molecular Biology 74 19%
Medicine and Dentistry 20 5%
Engineering 19 5%
Computer Science 18 5%
Other 52 14%
Unknown 42 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 126. 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
#152,797
of 15,354,969 outputs
Outputs from Giga Science
#14
of 740 outputs
Outputs of similar age
#2,883
of 225,260 outputs
Outputs of similar age from Giga Science
#1
of 1 outputs
Altmetric has tracked 15,354,969 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 740 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 22.8. 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 225,260 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 1 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