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Streamlined analysis of duplex sequencing data with Du Novo

Overview of attention for article published in Genome Biology (Online Edition), August 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 (85th percentile)

Mentioned by

twitter
16 tweeters
patent
2 patents

Citations

dimensions_citation
23 Dimensions

Readers on

mendeley
61 Mendeley
citeulike
3 CiteULike
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Title
Streamlined analysis of duplex sequencing data with Du Novo
Published in
Genome Biology (Online Edition), August 2016
DOI 10.1186/s13059-016-1039-4
Pubmed ID
Authors

Nicholas Stoler, Barbara Arbeithuber, Wilfried Guiblet, Kateryna D. Makova, Anton Nekrutenko

Abstract

Duplex sequencing was originally developed to detect rare nucleotide polymorphisms normally obscured by the noise of high-throughput sequencing. Here we describe a new, streamlined, reference-free approach for the analysis of duplex sequencing data. We show the approach performs well on simulated data and precisely reproduces previously published results and apply it to a newly produced dataset, enabling us to type low-frequency variants in human mitochondrial DNA. Finally, we provide all necessary tools as stand-alone components as well as integrate them into the Galaxy platform. All analyses performed in this manuscript can be repeated exactly as described at http://usegalaxy.org/duplex .

Twitter Demographics

The data shown below were collected from the profiles of 16 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 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Unknown 60 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 25%
Student > Bachelor 10 16%
Student > Ph. D. Student 8 13%
Other 6 10%
Student > Doctoral Student 3 5%
Other 11 18%
Unknown 8 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 20 33%
Agricultural and Biological Sciences 14 23%
Computer Science 9 15%
Medicine and Dentistry 4 7%
Engineering 3 5%
Other 2 3%
Unknown 9 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 09 March 2021.
All research outputs
#2,282,997
of 20,439,515 outputs
Outputs from Genome Biology (Online Edition)
#1,918
of 3,954 outputs
Outputs of similar age
#40,478
of 283,788 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 20,439,515 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,954 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.0. This one has gotten more attention than average, scoring higher than 51% 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 283,788 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% 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