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Next-generation sequencing-based detection of germline L1-mediated transductions

Overview of attention for article published in BMC Genomics, May 2016
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Title
Next-generation sequencing-based detection of germline L1-mediated transductions
Published in
BMC Genomics, May 2016
DOI 10.1186/s12864-016-2670-x
Pubmed ID
Authors

Jelena Tica, Eunjung Lee, Andreas Untergasser, Sascha Meiers, David A. Garfield, Omer Gokcumen, Eileen E.M. Furlong, Peter J. Park, Adrian M. Stütz, Jan O. Korbel

Abstract

While active LINE-1 (L1) elements possess the ability to mobilize flanking sequences to different genomic loci through a process termed transduction influencing genomic content and structure, an approach for detecting polymorphic germline non-reference transductions in massively-parallel sequencing data has been lacking. Here we present the computational approach TIGER (Transduction Inference in GERmline genomes), enabling the discovery of non-reference L1-mediated transductions by combining L1 discovery with detection of unique insertion sequences and detailed characterization of insertion sites. We employed TIGER to characterize polymorphic transductions in fifteen genomes from non-human primate species (chimpanzee, orangutan and rhesus macaque), as well as in a human genome. We achieved high accuracy as confirmed by PCR and two single molecule DNA sequencing techniques, and uncovered differences in relative rates of transduction between primate species. By enabling detection of polymorphic transductions, TIGER makes this form of relevant structural variation amenable for population and personal genome analysis.

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

Geographical breakdown

Country Count As %
Germany 1 4%
Unknown 26 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 26%
Student > Master 5 19%
Student > Bachelor 3 11%
Professor 3 11%
Researcher 3 11%
Other 2 7%
Unknown 4 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 37%
Agricultural and Biological Sciences 8 30%
Computer Science 2 7%
Nursing and Health Professions 1 4%
Medicine and Dentistry 1 4%
Other 0 0%
Unknown 5 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 23 May 2017.
All research outputs
#14,429,961
of 23,577,761 outputs
Outputs from BMC Genomics
#5,486
of 10,787 outputs
Outputs of similar age
#161,329
of 306,775 outputs
Outputs of similar age from BMC Genomics
#103
of 198 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,787 research outputs from this source. They receive a mean Attention Score of 4.7. 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 306,775 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 198 others from the same source and published within six weeks on either side of this one. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.