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ngs.plot: Quick mining and visualization of next-generation sequencing data by integrating genomic databases

Overview of attention for article published in BMC Genomics, April 2014
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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 (95th percentile)

Mentioned by

blogs
1 blog
twitter
15 X users
patent
1 patent

Citations

dimensions_citation
786 Dimensions

Readers on

mendeley
472 Mendeley
citeulike
8 CiteULike
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Title
ngs.plot: Quick mining and visualization of next-generation sequencing data by integrating genomic databases
Published in
BMC Genomics, April 2014
DOI 10.1186/1471-2164-15-284
Pubmed ID
Authors

Li Shen, Ningyi Shao, Xiaochuan Liu, Eric Nestler

Abstract

Understanding the relationship between the millions of functional DNA elements and their protein regulators, and how they work in conjunction to manifest diverse phenotypes, is key to advancing our understanding of the mammalian genome. Next-generation sequencing technology is now used widely to probe these protein-DNA interactions and to profile gene expression at a genome-wide scale. As the cost of DNA sequencing continues to fall, the interpretation of the ever increasing amount of data generated represents a considerable challenge.

X Demographics

X Demographics

The data shown below were collected from the profiles of 15 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 472 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 8 2%
United Kingdom 4 <1%
Germany 2 <1%
China 2 <1%
Spain 2 <1%
Norway 1 <1%
Brazil 1 <1%
Portugal 1 <1%
Belgium 1 <1%
Other 6 1%
Unknown 444 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 123 26%
Student > Ph. D. Student 98 21%
Student > Master 44 9%
Student > Bachelor 31 7%
Student > Doctoral Student 25 5%
Other 73 15%
Unknown 78 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 178 38%
Biochemistry, Genetics and Molecular Biology 127 27%
Computer Science 31 7%
Medicine and Dentistry 14 3%
Neuroscience 9 2%
Other 27 6%
Unknown 86 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 19. 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 22 November 2023.
All research outputs
#1,907,683
of 25,371,288 outputs
Outputs from BMC Genomics
#415
of 11,244 outputs
Outputs of similar age
#18,846
of 239,868 outputs
Outputs of similar age from BMC Genomics
#9
of 221 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,244 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 96% 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 239,868 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 221 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.