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Probing long-range interactions by extracting free energies from genome-wide chromosome conformation capture data

Overview of attention for article published in BMC Bioinformatics, May 2015
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Title
Probing long-range interactions by extracting free energies from genome-wide chromosome conformation capture data
Published in
BMC Bioinformatics, May 2015
DOI 10.1186/s12859-015-0584-2
Pubmed ID
Authors

Saeed Saberi, Pau Farré, Olivier Cuvier, Eldon Emberly

Abstract

A variety of DNA binding proteins are involved in regulating and shaping the packing of chromatin. They aid the formation of loops in the DNA that function to isolate different structural domains. A recent experimental technique, Hi-C, provides a method for determining the frequency of such looping between all distant parts of the genome. Given that the binding locations of many chromatin associated proteins have also been measured, it has been possible to make estimates for their influence on the long-range interactions as measured by Hi-C. However, a challenge in this analysis is the predominance of non-specific contacts that mask out the specific interactions of interest. We show that transforming the Hi-C contact frequencies into free energies gives a natural method for separating out the distance dependent non-specific interactions. In particular we apply Principal Component Analysis (PCA) to the transformed free energy matrix to identify the dominant modes of interaction. PCA identifies systematic effects as well as high frequency spatial noise in the Hi-C data which can be filtered out. Thus it can be used as a data driven approach for normalizing Hi-C data. We assess this PCA based normalization approach, along with several other normalization schemes, by fitting the transformed Hi-C data using a pairwise interaction model that takes as input the known locations of bound chromatin factors. The result of fitting is a set of predictions for the coupling energies between the various chromatin factors and their effect on the energetics of looping. We show that the quality of the fit can be used as a means to determine how much PCA filtering should be applied to the Hi-C data. We find that the different normalizations of the Hi-C data vary in the quality of fit to the pairwise interaction model. PCA filtering can improve the fit, and the predicted coupling energies lead to biologically meaningful insights for how various chromatin bound factors influence the stability of DNA loops in chromatin.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 5%
Germany 1 2%
United Kingdom 1 2%
Lithuania 1 2%
Spain 1 2%
Russia 1 2%
Unknown 36 84%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 44%
Researcher 7 16%
Student > Master 6 14%
Student > Bachelor 4 9%
Professor 2 5%
Other 2 5%
Unknown 3 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 44%
Biochemistry, Genetics and Molecular Biology 9 21%
Physics and Astronomy 7 16%
Computer Science 2 5%
Engineering 2 5%
Other 2 5%
Unknown 2 5%
Attention Score in Context

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 31 May 2015.
All research outputs
#7,500,672
of 23,577,654 outputs
Outputs from BMC Bioinformatics
#2,922
of 7,400 outputs
Outputs of similar age
#88,195
of 269,270 outputs
Outputs of similar age from BMC Bioinformatics
#58
of 122 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 7,400 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 58% 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 269,270 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 66% of its contemporaries.
We're also able to compare this research output to 122 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 50% of its contemporaries.