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Realtime analysis and visualization of MinION sequencing data with npReader

Overview of attention for article published in Bioinformatics, November 2015
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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 (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

news
1 news outlet
twitter
27 X users
googleplus
1 Google+ user

Citations

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24 Dimensions

Readers on

mendeley
97 Mendeley
citeulike
1 CiteULike
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Title
Realtime analysis and visualization of MinION sequencing data with npReader
Published in
Bioinformatics, November 2015
DOI 10.1093/bioinformatics/btv658
Pubmed ID
Authors

Minh Duc Cao, Devika Ganesamoorthy, Matthew A Cooper, Lachlan J M Coin

Abstract

The recently released Oxford Nanopore MinION sequencing platform presents many innovative features opening up potential for a range of applications not previously possible. Among these features, the ability to sequence in real-time provides a unique opportunity for many time-critical applications. While many software packages have been developed to analyse its data, there is still a lack of toolkits that support the streaming and real-time analysis of MinION sequencing data. We developed npReader, an open-source software package to facilitate real-time analysis of MinION sequencing data. npReader can simultaneously extract sequence reads and stream them to downstream analysis pipelines while the samples are being sequenced on the MinION device. It provides a command line interface for easy integration into a bioinformatics work flow, as well as a graphical user interface which concurrently displays the statistics of the run. It also provides an application programming interface for development of streaming algorithms in order to fully utilize the extent of nanopore sequencing potential. npReader is written in Java and is freely available at https://github.com/mdcao/npReader. Minh Duc Cao ([email protected]) and Lachlan J. M. Coin ([email protected]).

X Demographics

X Demographics

The data shown below were collected from the profiles of 27 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 97 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Brazil 2 2%
Portugal 1 1%
Switzerland 1 1%
Italy 1 1%
France 1 1%
Netherlands 1 1%
Czechia 1 1%
Sweden 1 1%
Other 2 2%
Unknown 84 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 38 39%
Student > Master 13 13%
Student > Ph. D. Student 11 11%
Other 9 9%
Student > Bachelor 6 6%
Other 11 11%
Unknown 9 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 35 36%
Biochemistry, Genetics and Molecular Biology 21 22%
Computer Science 13 13%
Medicine and Dentistry 6 6%
Engineering 2 2%
Other 6 6%
Unknown 14 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 23. 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 03 October 2017.
All research outputs
#1,608,744
of 25,377,790 outputs
Outputs from Bioinformatics
#797
of 12,809 outputs
Outputs of similar age
#23,108
of 294,335 outputs
Outputs of similar age from Bioinformatics
#25
of 197 outputs
Altmetric has tracked 25,377,790 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 12,809 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one has done particularly well, scoring higher than 93% 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 294,335 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 92% of its contemporaries.
We're also able to compare this research output to 197 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.