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HSA: integrating multi-track Hi-C data for genome-scale reconstruction of 3D chromatin structure

Overview of attention for article published in Genome Biology, March 2016
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  • Good Attention Score compared to outputs of the same age (73rd percentile)

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Title
HSA: integrating multi-track Hi-C data for genome-scale reconstruction of 3D chromatin structure
Published in
Genome Biology, March 2016
DOI 10.1186/s13059-016-0896-1
Pubmed ID
Authors

Chenchen Zou, Yuping Zhang, Zhengqing Ouyang

Abstract

Genome-wide 3C technologies (Hi-C) are being increasingly employed to study three-dimensional (3D) genome conformations. Existing computational approaches are unable to integrate accumulating data to facilitate studying 3D chromatin structure and function. We present HSA ( http://ouyanglab.jax.org/hsa/ ), a flexible tool that jointly analyzes multiple contact maps to infer 3D chromatin structure at the genome scale. HSA globally searches the latent structure underlying different cleavage footprints. Its robustness and accuracy outperform or rival existing tools on extensive simulations and orthogonal experiment validations. Applying HSA to recent in situ Hi-C data, we found the 3D chromatin structures are highly conserved across various human cell types.

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X Demographics

The data shown below were collected from the profiles of 11 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 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 1%
Lithuania 1 1%
Germany 1 1%
Norway 1 1%
Unknown 87 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 31 34%
Researcher 24 26%
Student > Master 10 11%
Student > Doctoral Student 5 5%
Professor 4 4%
Other 8 9%
Unknown 9 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 31 34%
Agricultural and Biological Sciences 26 29%
Computer Science 11 12%
Mathematics 3 3%
Social Sciences 2 2%
Other 6 7%
Unknown 12 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 04 July 2016.
All research outputs
#6,373,631
of 25,373,627 outputs
Outputs from Genome Biology
#3,049
of 4,467 outputs
Outputs of similar age
#82,761
of 312,882 outputs
Outputs of similar age from Genome Biology
#51
of 65 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 4,467 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 31st percentile – i.e., 31% 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 312,882 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 73% of its contemporaries.
We're also able to compare this research output to 65 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.