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Exon capture and bulk segregant analysis: rapid discovery of causative mutations using high-throughput sequencing

Overview of attention for article published in BMC Genomics, January 2012
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (59th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

Mentioned by

twitter
5 tweeters

Citations

dimensions_citation
14 Dimensions

Readers on

mendeley
41 Mendeley
citeulike
1 CiteULike
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Title
Exon capture and bulk segregant analysis: rapid discovery of causative mutations using high-throughput sequencing
Published in
BMC Genomics, January 2012
DOI 10.1186/1471-2164-13-649
Pubmed ID
Authors

Florencia del Viso, Dipankan Bhattacharya, Yong Kong, Michael J Gilchrist, Mustafa K Khokha

Abstract

Exome sequencing has transformed human genetic analysis and may do the same for other vertebrate model systems. However, a major challenge is sifting through the large number of sequence variants to identify the causative mutation for a given phenotype. In models like Xenopus tropicalis, an incomplete and occasionally incorrect genome assembly compounds this problem. To facilitate cloning of X. tropicalis mutants identified in forward genetic screens, we sought to combine bulk segregant analysis and exome sequencing into a single step.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters 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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 7%
Australia 1 2%
France 1 2%
Canada 1 2%
Netherlands 1 2%
Unknown 34 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 39%
Student > Ph. D. Student 6 15%
Student > Master 4 10%
Professor > Associate Professor 3 7%
Professor 3 7%
Other 7 17%
Unknown 2 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 28 68%
Biochemistry, Genetics and Molecular Biology 5 12%
Medicine and Dentistry 2 5%
Computer Science 1 2%
Chemistry 1 2%
Other 0 0%
Unknown 4 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 02 January 2013.
All research outputs
#6,432,614
of 12,373,620 outputs
Outputs from BMC Genomics
#3,050
of 7,313 outputs
Outputs of similar age
#102,334
of 258,999 outputs
Outputs of similar age from BMC Genomics
#219
of 631 outputs
Altmetric has tracked 12,373,620 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,313 research outputs from this source. They receive a mean Attention Score of 4.3. This one has gotten more attention than average, scoring higher than 56% 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 258,999 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.
We're also able to compare this research output to 631 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.