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Sequencing methods and datasets to improve functional interpretation of sleeping beauty mutagenesis screens

Overview of attention for article published in BMC Genomics, December 2014
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (74th percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

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3 X users
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1 patent

Citations

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22 Dimensions

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23 Mendeley
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Title
Sequencing methods and datasets to improve functional interpretation of sleeping beauty mutagenesis screens
Published in
BMC Genomics, December 2014
DOI 10.1186/1471-2164-15-1150
Pubmed ID
Authors

Jesse D Riordan, Luke J Drury, Ryan P Smith, Benjamin T Brett, Laura M Rogers, Todd E Scheetz, Adam J Dupuy

Abstract

Animal models of cancer are useful to generate complementary datasets for comparison to human tumor data. Insertional mutagenesis screens, such as those utilizing the Sleeping Beauty (SB) transposon system, provide a model that recapitulates the spontaneous development and progression of human disease. This approach has been widely used to model a variety of cancers in mice. Comprehensive mutation profiles are generated for individual tumors through amplification of transposon insertion sites followed by high-throughput sequencing. Subsequent statistical analyses identify common insertion sites (CISs), which are predicted to be functionally involved in tumorigenesis. Current methods utilized for SB insertion site analysis have some significant limitations. For one, they do not account for transposon footprints - a class of mutation generated following transposon remobilization. Existing methods also discard quantitative sequence data due to uncertainty regarding the extent to which it accurately reflects mutation abundance within a heterogeneous tumor. Additionally, computational analyses generally assume that all potential insertion sites have an equal probability of being detected under non-selective conditions, an assumption without sufficient relevant data. The goal of our study was to address these potential confounding factors in order to enhance functional interpretation of insertion site data from tumors.

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X Demographics

The data shown below were collected from the profiles of 3 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 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 9%
Unknown 21 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 35%
Student > Ph. D. Student 5 22%
Student > Bachelor 4 17%
Professor 1 4%
Professor > Associate Professor 1 4%
Other 0 0%
Unknown 4 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 43%
Biochemistry, Genetics and Molecular Biology 4 17%
Computer Science 3 13%
Immunology and Microbiology 1 4%
Medicine and Dentistry 1 4%
Other 1 4%
Unknown 3 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 21 December 2023.
All research outputs
#7,257,168
of 25,523,622 outputs
Outputs from BMC Genomics
#2,965
of 11,272 outputs
Outputs of similar age
#89,988
of 360,854 outputs
Outputs of similar age from BMC Genomics
#78
of 302 outputs
Altmetric has tracked 25,523,622 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 11,272 research outputs from this source. They receive a mean Attention Score of 4.8. This one has gotten more attention than average, scoring higher than 72% 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 360,854 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 74% of its contemporaries.
We're also able to compare this research output to 302 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 73% of its contemporaries.