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ReMixT: clone-specific genomic structure estimation in cancer

Overview of attention for article published in Genome Biology, July 2017
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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 (76th percentile)

Mentioned by

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15 X users

Citations

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31 Dimensions

Readers on

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67 Mendeley
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Title
ReMixT: clone-specific genomic structure estimation in cancer
Published in
Genome Biology, July 2017
DOI 10.1186/s13059-017-1267-2
Pubmed ID
Authors

Andrew W. McPherson, Andrew Roth, Gavin Ha, Cedric Chauve, Adi Steif, Camila P. E. de Souza, Peter Eirew, Alexandre Bouchard-Côté, Sam Aparicio, S. Cenk Sahinalp, Sohrab P. Shah

Abstract

Somatic evolution of malignant cells produces tumors composed of multiple clonal populations, distinguished in part by rearrangements and copy number changes affecting chromosomal segments. Whole genome sequencing mixes the signals of sampled populations, diluting the signals of clone-specific aberrations, and complicating estimation of clone-specific genotypes. We introduce ReMixT, a method to unmix tumor and contaminating normal signals and jointly predict mixture proportions, clone-specific segment copy number, and clone specificity of breakpoints. ReMixT is free, open-source software and is available at http://bitbucket.org/dranew/remixt .

X Demographics

X Demographics

The data shown below were collected from the profiles of 15 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 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 67 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 22%
Student > Ph. D. Student 12 18%
Student > Master 9 13%
Student > Bachelor 8 12%
Student > Doctoral Student 4 6%
Other 1 1%
Unknown 18 27%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 17 25%
Agricultural and Biological Sciences 14 21%
Computer Science 7 10%
Medicine and Dentistry 6 9%
Immunology and Microbiology 1 1%
Other 4 6%
Unknown 18 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 20 April 2018.
All research outputs
#4,660,989
of 25,382,440 outputs
Outputs from Genome Biology
#2,753
of 4,468 outputs
Outputs of similar age
#75,326
of 327,247 outputs
Outputs of similar age from Genome Biology
#54
of 63 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,468 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 38th percentile – i.e., 38% 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 327,247 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 63 others from the same source and published within six weeks on either side of this one. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.