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Reconstructing cancer genomes from paired-end sequencing data

Overview of attention for article published in BMC Bioinformatics, April 2012
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (56th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
38 Dimensions

Readers on

mendeley
85 Mendeley
citeulike
4 CiteULike
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Title
Reconstructing cancer genomes from paired-end sequencing data
Published in
BMC Bioinformatics, April 2012
DOI 10.1186/1471-2105-13-s6-s10
Pubmed ID
Authors

Layla Oesper, Anna Ritz, Sarah J Aerni, Ryan Drebin, Benjamin J Raphael

Abstract

A cancer genome is derived from the germline genome through a series of somatic mutations. Somatic structural variants - including duplications, deletions, inversions, translocations, and other rearrangements - result in a cancer genome that is a scrambling of intervals, or "blocks" of the germline genome sequence. We present an efficient algorithm for reconstructing the block organization of a cancer genome from paired-end DNA sequencing data.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 85 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 8%
United Kingdom 2 2%
Sweden 1 1%
France 1 1%
Netherlands 1 1%
Unknown 73 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 27 32%
Student > Ph. D. Student 23 27%
Student > Master 6 7%
Professor 5 6%
Student > Postgraduate 4 5%
Other 14 16%
Unknown 6 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 38 45%
Computer Science 24 28%
Biochemistry, Genetics and Molecular Biology 8 9%
Medicine and Dentistry 3 4%
Mathematics 2 2%
Other 4 5%
Unknown 6 7%

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 11 October 2014.
All research outputs
#1,988,010
of 4,507,509 outputs
Outputs from BMC Bioinformatics
#1,533
of 2,646 outputs
Outputs of similar age
#41,663
of 102,626 outputs
Outputs of similar age from BMC Bioinformatics
#68
of 108 outputs
Altmetric has tracked 4,507,509 research outputs across all sources so far. This one has received more attention than most of these and is in the 53rd percentile.
So far Altmetric has tracked 2,646 research outputs from this source. They receive a mean Attention Score of 4.4. This one is in the 37th percentile – i.e., 37% 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 102,626 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 56% of its contemporaries.
We're also able to compare this research output to 108 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.