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SequencErr: measuring and suppressing sequencer errors in next-generation sequencing data

Overview of attention for article published in Genome Biology, January 2021
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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 (94th percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
twitter
44 X users
patent
1 patent

Citations

dimensions_citation
18 Dimensions

Readers on

mendeley
43 Mendeley
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Title
SequencErr: measuring and suppressing sequencer errors in next-generation sequencing data
Published in
Genome Biology, January 2021
DOI 10.1186/s13059-020-02254-2
Pubmed ID
Authors

Eric M. Davis, Yu Sun, Yanling Liu, Pandurang Kolekar, Ying Shao, Karol Szlachta, Heather L. Mulder, Dongren Ren, Stephen V. Rice, Zhaoming Wang, Joy Nakitandwe, Alexander M. Gout, Bridget Shaner, Salina Hall, Leslie L. Robison, Stanley Pounds, Jeffery M. Klco, John Easton, Xiaotu Ma

X Demographics

X Demographics

The data shown below were collected from the profiles of 44 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 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 43 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 30%
Student > Ph. D. Student 8 19%
Student > Doctoral Student 3 7%
Student > Bachelor 3 7%
Student > Master 2 5%
Other 4 9%
Unknown 10 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 33%
Agricultural and Biological Sciences 6 14%
Computer Science 3 7%
Medicine and Dentistry 2 5%
Nursing and Health Professions 1 2%
Other 3 7%
Unknown 14 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 38. 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 August 2023.
All research outputs
#1,062,546
of 25,387,668 outputs
Outputs from Genome Biology
#762
of 4,470 outputs
Outputs of similar age
#30,294
of 525,858 outputs
Outputs of similar age from Genome Biology
#24
of 89 outputs
Altmetric has tracked 25,387,668 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,470 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has done well, scoring higher than 82% 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 525,858 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 94% of its contemporaries.
We're also able to compare this research output to 89 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.