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Representing and decomposing genomic structural variants as balanced integer flows on sequence graphs

Overview of attention for article published in BMC Bioinformatics, September 2016
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (62nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

twitter
5 tweeters

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
28 Mendeley
citeulike
2 CiteULike
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Title
Representing and decomposing genomic structural variants as balanced integer flows on sequence graphs
Published in
BMC Bioinformatics, September 2016
DOI 10.1186/s12859-016-1258-4
Pubmed ID
Authors

Daniel R. Zerbino, Tracy Ballinger, Benedict Paten, Glenn Hickey, David Haussler

Abstract

The study of genomic variation has provided key insights into the functional role of mutations. Predominantly, studies have focused on single nucleotide variants (SNV), which are relatively easy to detect and can be described with rich mathematical models. However, it has been observed that genomes are highly plastic, and that whole regions can be moved, removed or duplicated in bulk. These structural variants (SV) have been shown to have significant impact on phenotype, but their study has been held back by the combinatorial complexity of the underlying models. We describe here a general model of structural variation that encompasses both balanced rearrangements and arbitrary copy-number variants (CNV). In this model, we show that the space of possible evolutionary histories that explain the structural differences between any two genomes can be sampled ergodically.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 4%
United States 1 4%
Germany 1 4%
Unknown 25 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 36%
Researcher 7 25%
Student > Master 3 11%
Student > Doctoral Student 2 7%
Student > Bachelor 1 4%
Other 0 0%
Unknown 5 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 32%
Biochemistry, Genetics and Molecular Biology 5 18%
Computer Science 5 18%
Engineering 2 7%
Medicine and Dentistry 1 4%
Other 1 4%
Unknown 5 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 14 November 2016.
All research outputs
#5,718,348
of 11,293,566 outputs
Outputs from BMC Bioinformatics
#2,011
of 4,195 outputs
Outputs of similar age
#96,337
of 258,992 outputs
Outputs of similar age from BMC Bioinformatics
#54
of 137 outputs
Altmetric has tracked 11,293,566 research outputs across all sources so far. This one is in the 49th percentile – i.e., 49% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,195 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 50% 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 258,992 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 62% of its contemporaries.
We're also able to compare this research output to 137 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.