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Analysis of error profiles in deep next-generation sequencing data

Overview of attention for article published in Genome Biology, March 2019
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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 (95th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

news
2 news outlets
blogs
3 blogs
twitter
53 X users
patent
4 patents
f1000
1 research highlight platform

Citations

dimensions_citation
210 Dimensions

Readers on

mendeley
376 Mendeley
citeulike
1 CiteULike
Title
Analysis of error profiles in deep next-generation sequencing data
Published in
Genome Biology, March 2019
DOI 10.1186/s13059-019-1659-6
Pubmed ID
Authors

Xiaotu Ma, Ying Shao, Liqing Tian, Diane A. Flasch, Heather L. Mulder, Michael N. Edmonson, Yu Liu, Xiang Chen, Scott Newman, Joy Nakitandwe, Yongjin Li, Benshang Li, Shuhong Shen, Zhaoming Wang, Sheila Shurtleff, Leslie L. Robison, Shawn Levy, John Easton, Jinghui Zhang

X Demographics

X Demographics

The data shown below were collected from the profiles of 53 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 376 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 376 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 69 18%
Researcher 59 16%
Student > Master 51 14%
Student > Bachelor 24 6%
Student > Doctoral Student 18 5%
Other 48 13%
Unknown 107 28%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 124 33%
Agricultural and Biological Sciences 67 18%
Medicine and Dentistry 19 5%
Computer Science 10 3%
Pharmacology, Toxicology and Pharmaceutical Science 5 1%
Other 30 8%
Unknown 121 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 63. 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 18 June 2023.
All research outputs
#690,537
of 25,837,817 outputs
Outputs from Genome Biology
#435
of 4,506 outputs
Outputs of similar age
#15,930
of 366,914 outputs
Outputs of similar age from Genome Biology
#14
of 59 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,506 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 particularly well, scoring higher than 90% 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 366,914 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 95% of its contemporaries.
We're also able to compare this research output to 59 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.