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Retrieving Chromatin Patterns from Deep Sequencing Data Using Correlation Functions

Overview of attention for article published in Biophysical Journal, January 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)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

Mentioned by

twitter
17 tweeters

Readers on

mendeley
19 Mendeley
Title
Retrieving Chromatin Patterns from Deep Sequencing Data Using Correlation Functions
Published in
Biophysical Journal, January 2017
DOI 10.1016/j.bpj.2017.01.001
Pubmed ID
Authors

Jana Molitor, Jan-Philipp Mallm, Karsten Rippe, Fabian Erdel, Molitor, Jana, Mallm, Jan-Philipp, Rippe, Karsten, Erdel, Fabian

Abstract

Epigenetic modifications and other chromatin features partition the genome on multiple length scales. They define chromatin domains with distinct biological functions that come in sizes ranging from single modified DNA bases to several megabases in the case of heterochromatic histone modifications. Due to chromatin folding, domains that are well separated along the linear nucleosome chain can form long-range interactions in three-dimensional space. It has now become a routine task to map epigenetic marks and chromatin structure by deep sequencing methods. However, assessing and comparing the properties of chromatin domains and their positional relationships across data sets without a priori assumptions remains challenging. Here, we introduce multiscale correlation evaluation (MCORE), which uses the fluctuation spectrum of mapped sequencing reads to quantify and compare chromatin patterns over a broad range of length scales in a model-independent manner. We applied MCORE to map the chromatin landscape in mouse embryonic stem cells and differentiated neural cells. We integrated sequencing data from chromatin immunoprecipitation, RNA expression, DNA methylation, and chromosome conformation capture experiments into network models that reflect the positional relationships among these features on different genomic scales. Furthermore, we used MCORE to compare our experimental data to models for heterochromatin reorganization during differentiation. The application of correlation functions to deep sequencing data complements current evaluation schemes and will support the development of quantitative descriptions of chromatin networks.

Twitter Demographics

The data shown below were collected from the profiles of 17 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 42%
Student > Ph. D. Student 4 21%
Student > Bachelor 2 11%
Student > Master 2 11%
Professor > Associate Professor 1 5%
Other 2 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 58%
Biochemistry, Genetics and Molecular Biology 8 42%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 11 February 2017.
All research outputs
#808,738
of 7,690,302 outputs
Outputs from Biophysical Journal
#177
of 2,921 outputs
Outputs of similar age
#40,089
of 238,072 outputs
Outputs of similar age from Biophysical Journal
#25
of 122 outputs
Altmetric has tracked 7,690,302 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,921 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done particularly well, scoring higher than 93% 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 238,072 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 122 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.