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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

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1 X user

Citations

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58 Dimensions

Readers on

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175 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
Pubmed ID
Authors

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

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 175 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 175 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 34 19%
Student > Ph. D. Student 24 14%
Student > Bachelor 20 11%
Student > Master 18 10%
Student > Doctoral Student 7 4%
Other 26 15%
Unknown 46 26%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 49 28%
Agricultural and Biological Sciences 33 19%
Computer Science 13 7%
Chemistry 7 4%
Environmental Science 4 2%
Other 18 10%
Unknown 51 29%
Attention Score in Context

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
#20,552,296
of 23,128,387 outputs
Outputs from BMC Genomics
#9,329
of 10,708 outputs
Outputs of similar age
#370,593
of 438,009 outputs
Outputs of similar age from BMC Genomics
#190
of 228 outputs
Altmetric has tracked 23,128,387 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 10,708 research outputs from this source. They receive a mean Attention Score of 4.7. 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 438,009 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 228 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.