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NanoMod: a computational tool to detect DNA modifications using Nanopore long-read sequencing data

Overview of attention for article published in BMC Genomics, February 2019
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Mentioned by

twitter
1 tweeter

Citations

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2 Dimensions

Readers on

mendeley
77 Mendeley
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Title
NanoMod: a computational tool to detect DNA modifications using Nanopore long-read sequencing data
Published in
BMC Genomics, February 2019
DOI 10.1186/s12864-018-5372-8
Authors

Qian Liu, Daniela C. Georgieva, Dieter Egli, Kai Wang

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 77 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 25%
Researcher 19 25%
Unspecified 13 17%
Student > Master 9 12%
Student > Bachelor 7 9%
Other 10 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 24 31%
Biochemistry, Genetics and Molecular Biology 24 31%
Unspecified 14 18%
Computer Science 7 9%
Medicine and Dentistry 2 3%
Other 6 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 05 February 2019.
All research outputs
#11,824,074
of 13,327,609 outputs
Outputs from BMC Genomics
#6,855
of 7,832 outputs
Outputs of similar age
#240,392
of 284,500 outputs
Outputs of similar age from BMC Genomics
#4
of 5 outputs
Altmetric has tracked 13,327,609 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,832 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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 284,500 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one.