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HPG pore: an efficient and scalable framework for nanopore sequencing data

Overview of attention for article published in BMC Bioinformatics, February 2016
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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 (99th percentile)

Mentioned by

blogs
2 blogs
twitter
28 tweeters
peer_reviews
1 peer review site
facebook
1 Facebook page

Citations

dimensions_citation
8 Dimensions

Readers on

mendeley
89 Mendeley
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Title
HPG pore: an efficient and scalable framework for nanopore sequencing data
Published in
BMC Bioinformatics, February 2016
DOI 10.1186/s12859-016-0966-0
Pubmed ID
Authors

Joaquin Tarraga, Asunción Gallego, Vicente Arnau, Ignacio Medina, Joaquin Dopazo

Abstract

The use of nanopore technologies is expected to spread in the future because they are portable and can sequence long fragments of DNA molecules without prior amplification. The first nanopore sequencer available, the MinION™ from Oxford Nanopore Technologies, is a USB-connected, portable device that allows real-time DNA analysis. In addition, other new instruments are expected to be released soon, which promise to outperform the current short-read technologies in terms of throughput. Despite the flood of data expected from this technology, the data analysis solutions currently available are only designed to manage small projects and are not scalable. Here we present HPG Pore, a toolkit for exploring and analysing nanopore sequencing data. HPG Pore can run on both individual computers and in the Hadoop distributed computing framework, which allows easy scale-up to manage the large amounts of data expected to result from extensive use of nanopore technologies in the future. HPG Pore allows for virtually unlimited sequencing data scalability, thus guaranteeing its continued management in near future scenarios. HPG Pore is available in GitHub at http://github.com/opencb/hpg-pore .

Twitter Demographics

The data shown below were collected from the profiles of 28 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 89 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 2 2%
Czechia 2 2%
Germany 1 1%
Italy 1 1%
Sweden 1 1%
Brazil 1 1%
United Kingdom 1 1%
Canada 1 1%
Estonia 1 1%
Other 2 2%
Unknown 76 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 30 34%
Student > Master 17 19%
Student > Ph. D. Student 10 11%
Student > Bachelor 8 9%
Professor > Associate Professor 6 7%
Other 11 12%
Unknown 7 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 36 40%
Biochemistry, Genetics and Molecular Biology 22 25%
Computer Science 12 13%
Medicine and Dentistry 3 3%
Chemistry 3 3%
Other 4 4%
Unknown 9 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 27. 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 March 2016.
All research outputs
#919,689
of 18,005,056 outputs
Outputs from BMC Bioinformatics
#126
of 6,335 outputs
Outputs of similar age
#19,385
of 272,312 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 7 outputs
Altmetric has tracked 18,005,056 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,335 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. 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 272,312 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 7 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