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Improved variant discovery through local re-alignment of short-read next-generation sequencing data using SRMA

Overview of attention for article published in Genome Biology (Online Edition), October 2010
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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 (86th percentile)

Mentioned by

twitter
1 tweeter
patent
8 patents

Citations

dimensions_citation
58 Dimensions

Readers on

mendeley
192 Mendeley
citeulike
24 CiteULike
connotea
1 Connotea
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Title
Improved variant discovery through local re-alignment of short-read next-generation sequencing data using SRMA
Published in
Genome Biology (Online Edition), October 2010
DOI 10.1186/gb-2010-11-10-r99
Pubmed ID
Authors

Nils Homer, Stanley F Nelson

Abstract

A primary component of next-generation sequencing analysis is to align short reads to a reference genome, with each read aligned independently. However, reads that observe the same non-reference DNA sequence are highly correlated and can be used to better model the true variation in the target genome. A novel short-read micro realigner, SRMA, that leverages this correlation to better resolve a consensus of the underlying DNA sequence of the targeted genome is described here.

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 192 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 8 4%
France 3 2%
United Kingdom 3 2%
Norway 2 1%
Spain 2 1%
South Africa 1 <1%
Sweden 1 <1%
China 1 <1%
Germany 1 <1%
Other 2 1%
Unknown 168 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 60 31%
Student > Ph. D. Student 54 28%
Other 14 7%
Professor > Associate Professor 14 7%
Student > Master 14 7%
Other 25 13%
Unknown 11 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 112 58%
Biochemistry, Genetics and Molecular Biology 32 17%
Computer Science 14 7%
Medicine and Dentistry 8 4%
Mathematics 4 2%
Other 10 5%
Unknown 12 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 29 June 2021.
All research outputs
#2,651,104
of 19,833,325 outputs
Outputs from Genome Biology (Online Edition)
#2,060
of 3,875 outputs
Outputs of similar age
#15,137
of 117,371 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 19,833,325 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,875 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.9. This one is in the 46th percentile – i.e., 46% 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 117,371 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them