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Predicting double-strand DNA breaks using epigenome marks or DNA at kilobase resolution

Overview of attention for article published in Genome Biology, March 2018
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

Mentioned by

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1 blog
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48 X users

Citations

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27 Dimensions

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104 Mendeley
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Title
Predicting double-strand DNA breaks using epigenome marks or DNA at kilobase resolution
Published in
Genome Biology, March 2018
DOI 10.1186/s13059-018-1411-7
Pubmed ID
Authors

Raphaël Mourad, Krzysztof Ginalski, Gaëlle Legube, Olivier Cuvier

Abstract

Double-strand breaks (DSBs) result from the attack of both DNA strands by multiple sources, including radiation and chemicals. DSBs can cause the abnormal chromosomal rearrangements associated with cancer. Recent techniques allow the genome-wide mapping of DSBs at high resolution, enabling the comprehensive study of their origins. However, these techniques are costly and challenging. Hence, we devise a computational approach to predict DSBs using the epigenomic and chromatin context, for which public data are readily available from the ENCODE project. We achieve excellent prediction accuracy at high resolution. We identify chromatin accessibility, activity, and long-range contacts as the best predictors.

X Demographics

X Demographics

The data shown below were collected from the profiles of 48 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 104 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 104 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 25%
Researcher 17 16%
Student > Master 9 9%
Student > Bachelor 6 6%
Other 6 6%
Other 13 13%
Unknown 27 26%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 38 37%
Agricultural and Biological Sciences 19 18%
Medicine and Dentistry 8 8%
Computer Science 5 5%
Nursing and Health Professions 2 2%
Other 3 3%
Unknown 29 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 33. 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 10 July 2019.
All research outputs
#1,212,408
of 25,382,440 outputs
Outputs from Genome Biology
#912
of 4,468 outputs
Outputs of similar age
#27,181
of 351,767 outputs
Outputs of similar age from Genome Biology
#14
of 44 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% 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 has done well, scoring higher than 79% 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 351,767 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 44 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 68% of its contemporaries.