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Jitterbug: somatic and germline transposon insertion detection at single-nucleotide resolution

Overview of attention for article published in BMC Genomics, October 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)

Mentioned by

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12 tweeters
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1 Facebook page

Citations

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28 Dimensions

Readers on

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100 Mendeley
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Title
Jitterbug: somatic and germline transposon insertion detection at single-nucleotide resolution
Published in
BMC Genomics, October 2015
DOI 10.1186/s12864-015-1975-5
Pubmed ID
Authors

Elizabeth Hénaff, Luís Zapata, Josep M. Casacuberta, Stephan Ossowski

Abstract

Transposable elements are major players in genome evolution. Transposon insertion polymorphisms can translate into phenotypic differences in plants and animals and are linked to different diseases including human cancer, making their characterization highly relevant to the study of genome evolution and genetic diseases. Here we present Jitterbug, a novel tool that identifies transposable element insertion sites at single-nucleotide resolution based on the pairedend mapping and clipped-read signatures produced by NGS alignments. Jitterbug can be easily integrated into existing NGS analysis pipelines, using the standard BAM format produced by frequently applied alignment tools (e.g. bwa, bowtie2), with no need to realign reads to a set of consensus transposon sequences. Jitterbug is highly sensitive and able to recall transposon insertions with a very high specificity, as demonstrated by benchmarks in the human and Arabidopsis genomes, and validation using long PacBio reads. In addition, Jitterbug estimates the zygosity of transposon insertions with high accuracy and can also identify somatic insertions. We demonstrate that Jitterbug can identify mosaic somatic transposon movement using sequenced tumor-normal sample pairs and allows for estimating the cancer cell fraction of clones containing a somatic TE insertion. We suggest that the independent methods we use to evaluate performance are a step towards creating a gold standard dataset for benchmarking structural variant prediction tools.

Twitter Demographics

The data shown below were collected from the profiles of 12 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 100 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
France 2 2%
Austria 1 1%
Brazil 1 1%
Italy 1 1%
New Zealand 1 1%
Argentina 1 1%
Belgium 1 1%
Spain 1 1%
Other 1 1%
Unknown 87 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 26 26%
Student > Ph. D. Student 21 21%
Student > Master 11 11%
Other 6 6%
Student > Postgraduate 6 6%
Other 19 19%
Unknown 11 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 46 46%
Biochemistry, Genetics and Molecular Biology 26 26%
Computer Science 8 8%
Medicine and Dentistry 3 3%
Engineering 2 2%
Other 1 1%
Unknown 14 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 25 January 2016.
All research outputs
#3,579,216
of 15,519,772 outputs
Outputs from BMC Genomics
#1,644
of 8,748 outputs
Outputs of similar age
#56,604
of 254,823 outputs
Outputs of similar age from BMC Genomics
#1
of 1 outputs
Altmetric has tracked 15,519,772 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,748 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 81% 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 254,823 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 77% 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