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Differential analysis between somatic mutation and germline variation profiles reveals cancer-related genes

Overview of attention for article published in Genome Medicine, August 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • Average Attention Score compared to outputs of the same age and source

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Title
Differential analysis between somatic mutation and germline variation profiles reveals cancer-related genes
Published in
Genome Medicine, August 2017
DOI 10.1186/s13073-017-0465-6
Pubmed ID
Authors

Pawel F. Przytycki, Mona Singh

Abstract

A major aim of cancer genomics is to pinpoint which somatically mutated genes are involved in tumor initiation and progression. We introduce a new framework for uncovering cancer genes, differential mutation analysis, which compares the mutational profiles of genes across cancer genomes with their natural germline variation across healthy individuals. We present DiffMut, a fast and simple approach for differential mutational analysis, and demonstrate that it is more effective in discovering cancer genes than considerably more sophisticated approaches. We conclude that germline variation across healthy human genomes provides a powerful means for characterizing somatic mutation frequency and identifying cancer driver genes. DiffMut is available at https://github.com/Singh-Lab/Differential-Mutation-Analysis .

X Demographics

X Demographics

The data shown below were collected from the profiles of 20 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 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 73 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 26%
Student > Ph. D. Student 10 14%
Student > Bachelor 8 11%
Student > Master 8 11%
Student > Postgraduate 6 8%
Other 9 12%
Unknown 13 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 26 36%
Agricultural and Biological Sciences 11 15%
Computer Science 7 10%
Medicine and Dentistry 5 7%
Neuroscience 2 3%
Other 5 7%
Unknown 17 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 30 October 2017.
All research outputs
#2,749,377
of 23,794,258 outputs
Outputs from Genome Medicine
#632
of 1,476 outputs
Outputs of similar age
#51,436
of 317,825 outputs
Outputs of similar age from Genome Medicine
#15
of 24 outputs
Altmetric has tracked 23,794,258 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,476 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.3. This one has gotten more attention than average, scoring higher than 57% 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 317,825 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 83% of its contemporaries.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.