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A new approach for detecting low-level mutations in next-generation sequence data

Overview of attention for article published in Genome Biology, May 2012
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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 (94th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

blogs
1 blog
twitter
16 X users
patent
3 patents

Citations

dimensions_citation
87 Dimensions

Readers on

mendeley
241 Mendeley
citeulike
5 CiteULike
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Title
A new approach for detecting low-level mutations in next-generation sequence data
Published in
Genome Biology, May 2012
DOI 10.1186/gb-2012-13-5-r34
Pubmed ID
Authors

Mingkun Li, Mark Stoneking

Abstract

We propose a new method that incorporates population re-sequencing data, distribution of reads, and strand bias in detecting low-level mutations. The method can accurately identify low-level mutations down to a level of 2.3%, with an average coverage of 500×, and with a false discovery rate of less than 1%. In addition, we also discuss other problems in detecting low-level mutations, including chimeric reads and sample cross-contamination, and provide possible solutions to them.

X Demographics

X Demographics

The data shown below were collected from the profiles of 16 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 241 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 10 4%
United Kingdom 5 2%
Netherlands 3 1%
Brazil 2 <1%
Germany 2 <1%
Belgium 2 <1%
Norway 1 <1%
Sweden 1 <1%
France 1 <1%
Other 8 3%
Unknown 206 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 81 34%
Student > Ph. D. Student 54 22%
Other 16 7%
Student > Master 16 7%
Student > Bachelor 15 6%
Other 48 20%
Unknown 11 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 138 57%
Biochemistry, Genetics and Molecular Biology 42 17%
Computer Science 16 7%
Medicine and Dentistry 13 5%
Immunology and Microbiology 4 2%
Other 12 5%
Unknown 16 7%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 24. 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 11 February 2020.
All research outputs
#1,584,229
of 25,373,627 outputs
Outputs from Genome Biology
#1,293
of 4,467 outputs
Outputs of similar age
#8,912
of 177,810 outputs
Outputs of similar age from Genome Biology
#10
of 42 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,467 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 gotten more attention than average, scoring higher than 71% 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 177,810 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 42 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.