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Streaming algorithms for identification of pathogens and antibiotic resistance potential from real-time MinIONTM sequencing

Overview of attention for article published in Giga Science, July 2016
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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 (95th percentile)

Mentioned by

blogs
3 blogs
twitter
58 tweeters
peer_reviews
1 peer review site
facebook
1 Facebook page
wikipedia
1 Wikipedia page
googleplus
1 Google+ user

Citations

dimensions_citation
49 Dimensions

Readers on

mendeley
167 Mendeley
Title
Streaming algorithms for identification of pathogens and antibiotic resistance potential from real-time MinIONTM sequencing
Published in
Giga Science, July 2016
DOI 10.1186/s13742-016-0137-2
Pubmed ID
Authors

Minh Duc Cao, Devika Ganesamoorthy, Alysha G. Elliott, Huihui Zhang, Matthew A. Cooper, Lachlan J.M. Coin

Abstract

The recently introduced Oxford Nanopore MinION platform generates DNA sequence data in real-time. This has great potential to shorten the sample-to-results time and is likely to have benefits such as rapid diagnosis of bacterial infection and identification of drug resistance. However, there are few tools available for streaming analysis of real-time sequencing data. Here, we present a framework for streaming analysis of MinION real-time sequence data, together with probabilistic streaming algorithms for species typing, strain typing and antibiotic resistance profile identification. Using four culture isolate samples, as well as a mixed-species sample, we demonstrate that bacterial species and strain information can be obtained within 30 min of sequencing and using about 500 reads, initial drug-resistance profiles within two hours, and complete resistance profiles within 10 h. While strain identification with multi-locus sequence typing required more than 15x coverage to generate confident assignments, our novel gene-presence typing could detect the presence of a known strain with 0.5x coverage. We also show that our pipeline can process over 100 times more data than the current throughput of the MinION on a desktop computer.

Twitter Demographics

The data shown below were collected from the profiles of 58 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 167 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 1%
United Kingdom 1 <1%
Sweden 1 <1%
France 1 <1%
Belgium 1 <1%
Japan 1 <1%
Switzerland 1 <1%
Unknown 159 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 45 27%
Student > Bachelor 31 19%
Student > Master 24 14%
Student > Ph. D. Student 21 13%
Professor > Associate Professor 10 6%
Other 30 18%
Unknown 6 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 57 34%
Biochemistry, Genetics and Molecular Biology 44 26%
Computer Science 18 11%
Medicine and Dentistry 11 7%
Engineering 6 4%
Other 17 10%
Unknown 14 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 54. 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 31 January 2020.
All research outputs
#352,520
of 14,280,939 outputs
Outputs from Giga Science
#57
of 675 outputs
Outputs of similar age
#11,252
of 264,269 outputs
Outputs of similar age from Giga Science
#1
of 1 outputs
Altmetric has tracked 14,280,939 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 675 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.8. This one has done particularly well, scoring higher than 91% 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 264,269 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 95% 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