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Targeted identification of TE insertions in a Drosophila genome through hemi-specific PCR

Overview of attention for article published in Mobile DNA, July 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#37 of 268)
  • High Attention Score compared to outputs of the same age (86th percentile)

Mentioned by

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26 tweeters

Citations

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5 Dimensions

Readers on

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17 Mendeley
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Title
Targeted identification of TE insertions in a Drosophila genome through hemi-specific PCR
Published in
Mobile DNA, July 2017
DOI 10.1186/s13100-017-0092-1
Pubmed ID
Authors

Shuo Zhang, Erin S. Kelleher

Abstract

Transposable elements (TEs) are major components of eukaryotic genomes and drivers of genome evolution, producing intraspecific polymorphism and interspecific differences through mobilization and non-homologous recombination. TE insertion sites are often highly variable within species, creating a need for targeted genome re-sequencing (TGS) methods to identify TE insertion sites. We present a hemi-specific PCR approach for TGS of P-elements in Drosophila genomes on the Illumina platform. We also present a computational framework for identifying new insertions from TGS reads. Finally, we describe a new method for estimating the frequency of TE insertions from WGS data, which is based precise insertion sites provided by TGS annotations. By comparing our results to TE annotations based on whole genome re-sequencing (WGS) data for the same Drosophilamelanogaster strain, we demonstrate that TGS is powerful for identifying true insertions, even in repeat-rich heterochromatic regions. We also demonstrate that TGS offers enhanced annotation of precise insertion sites, which facilitates estimation of TE insertion frequency. TGS by hemi-specific PCR is a powerful approach for identifying TE insertions of particular TE families in species with a high-quality reference genome, at greatly reduced cost as compared to WGS. It may therefore be ideal for population genomic studies of particular TE families. Additionally, TGS and WGS can be used as complementary approaches, with TGS annotations identifying more annotated insertions with greater precision for a target TE family, and WGS data allowing for estimates of TE insertion frequencies, and a broader picture of the location of non-target TEs across the genome.

Twitter Demographics

The data shown below were collected from the profiles of 26 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 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 29%
Student > Ph. D. Student 5 29%
Student > Doctoral Student 3 18%
Student > Bachelor 2 12%
Professor 1 6%
Other 1 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 65%
Biochemistry, Genetics and Molecular Biology 5 29%
Immunology and Microbiology 1 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 August 2017.
All research outputs
#1,441,130
of 15,921,538 outputs
Outputs from Mobile DNA
#37
of 268 outputs
Outputs of similar age
#38,050
of 272,349 outputs
Outputs of similar age from Mobile DNA
#1
of 2 outputs
Altmetric has tracked 15,921,538 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 268 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one has done well, scoring higher than 86% 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 272,349 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 2 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