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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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3 X users

Citations

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43 Dimensions

Readers on

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87 Mendeley
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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.

X Demographics

X Demographics

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 87 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%
France 1 1%
Netherlands 1 1%
Sweden 1 1%
Unknown 75 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 28 32%
Student > Ph. D. Student 22 25%
Student > Master 6 7%
Professor 5 6%
Student > Bachelor 4 5%
Other 15 17%
Unknown 7 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 38 44%
Computer Science 24 28%
Biochemistry, Genetics and Molecular Biology 9 10%
Medicine and Dentistry 3 3%
Mathematics 2 2%
Other 4 5%
Unknown 7 8%
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 11 October 2014.
All research outputs
#14,181,583
of 22,729,647 outputs
Outputs from BMC Bioinformatics
#4,720
of 7,266 outputs
Outputs of similar age
#95,320
of 161,966 outputs
Outputs of similar age from BMC Bioinformatics
#60
of 97 outputs
Altmetric has tracked 22,729,647 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,266 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. 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 161,966 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 97 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.