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Mobster: accurate detection of mobile element insertions in next generation sequencing data

Overview of attention for article published in Genome Biology (Online Edition), October 2014
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (76th percentile)

Mentioned by

twitter
8 tweeters
googleplus
1 Google+ user

Citations

dimensions_citation
67 Dimensions

Readers on

mendeley
149 Mendeley
citeulike
5 CiteULike
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Title
Mobster: accurate detection of mobile element insertions in next generation sequencing data
Published in
Genome Biology (Online Edition), October 2014
DOI 10.1186/s13059-014-0488-x
Pubmed ID
Authors

Djie Tjwan Thung, Joep de Ligt, Lisenka EM Vissers, Marloes Steehouwer, Mark Kroon, Petra de Vries, Eline P Slagboom, Kai Ye, Joris A Veltman, Jayne Y Hehir-Kwa

Abstract

Mobile elements are major drivers in changing genomic architecture and can cause disease. The detection of mobile elements is hindered due to the low mappability of their highly repetitive sequences. We have developed an algorithm, called Mobster, to detect non-reference mobile element insertions in next generation sequencing data from both whole genome and whole exome studies. Mobster uses discordant read pairs and clipped reads in combination with consensus sequences of known active mobile elements. Mobster has a low false discovery rate and high recall rate for both L1 and Alu elements. Mobster is available at http://sourceforge.net/projects/mobster.

Twitter Demographics

The data shown below were collected from the profiles of 8 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 149 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 3 2%
Brazil 1 <1%
Norway 1 <1%
Germany 1 <1%
United Kingdom 1 <1%
Russia 1 <1%
United States 1 <1%
Luxembourg 1 <1%
Unknown 139 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 45 30%
Researcher 34 23%
Student > Master 18 12%
Student > Bachelor 10 7%
Professor 9 6%
Other 16 11%
Unknown 17 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 61 41%
Biochemistry, Genetics and Molecular Biology 42 28%
Computer Science 9 6%
Medicine and Dentistry 6 4%
Neuroscience 2 1%
Other 7 5%
Unknown 22 15%

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 16 October 2015.
All research outputs
#5,169,895
of 19,569,206 outputs
Outputs from Genome Biology (Online Edition)
#2,681
of 3,849 outputs
Outputs of similar age
#73,136
of 308,751 outputs
Outputs of similar age from Genome Biology (Online Edition)
#6
of 6 outputs
Altmetric has tracked 19,569,206 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 3,849 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.9. This one is in the 30th percentile – i.e., 30% 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 308,751 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 76% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one.