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From squiggle to basepair: computational approaches for improving nanopore sequencing read accuracy

Overview of attention for article published in Genome Biology (Online Edition), July 2018
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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 (98th percentile)

Mentioned by

blogs
1 blog
twitter
257 tweeters

Citations

dimensions_citation
132 Dimensions

Readers on

mendeley
466 Mendeley
citeulike
2 CiteULike
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Title
From squiggle to basepair: computational approaches for improving nanopore sequencing read accuracy
Published in
Genome Biology (Online Edition), July 2018
DOI 10.1186/s13059-018-1462-9
Pubmed ID
Authors

Franka J. Rang, Wigard P. Kloosterman, Jeroen de Ridder

Abstract

Nanopore sequencing is a rapidly maturing technology delivering long reads in real time on a portable instrument at low cost. Not surprisingly, the community has rapidly taken up this new way of sequencing and has used it successfully for a variety of research applications. A major limitation of nanopore sequencing is its high error rate, which despite recent improvements to the nanopore chemistry and computational tools still ranges between 5% and 15%. Here, we review computational approaches determining the nanopore sequencing error rate. Furthermore, we outline strategies for translation of raw sequencing data into base calls for detection of base modifications and for obtaining consensus sequences.

Twitter Demographics

The data shown below were collected from the profiles of 257 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 466 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 466 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 94 20%
Researcher 87 19%
Student > Bachelor 67 14%
Student > Master 67 14%
Other 20 4%
Other 56 12%
Unknown 75 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 159 34%
Agricultural and Biological Sciences 92 20%
Computer Science 28 6%
Engineering 21 5%
Immunology and Microbiology 18 4%
Other 58 12%
Unknown 90 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 141. 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 11 September 2020.
All research outputs
#143,671
of 15,922,938 outputs
Outputs from Genome Biology (Online Edition)
#89
of 3,414 outputs
Outputs of similar age
#5,012
of 276,329 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 15,922,938 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 3,414 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.7. 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 276,329 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