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Chromatin segmentation based on a probabilistic model for read counts explains a large portion of the epigenome

Overview of attention for article published in Genome Biology (Online Edition), July 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)

Mentioned by

twitter
17 tweeters

Citations

dimensions_citation
79 Dimensions

Readers on

mendeley
119 Mendeley
citeulike
2 CiteULike
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Title
Chromatin segmentation based on a probabilistic model for read counts explains a large portion of the epigenome
Published in
Genome Biology (Online Edition), July 2015
DOI 10.1186/s13059-015-0708-z
Pubmed ID
Authors

Alessandro Mammana, Ho-Ryun Chung

Abstract

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is an increasingly common experimental approach to generate genome-wide maps of histone modifications and to dissect the complexity of the epigenome. Here, we propose EpiCSeg: a novel algorithm that combines several histone modification maps for the segmentation and characterization of cell-type specific epigenomic landscapes. By using an accurate probabilistic model for the read counts, EpiCSeg provides a useful annotation for a considerably larger portion of the genome, shows a stronger association with validation data, and yields more consistent predictions across replicate experiments when compared to existing methods.The software is available at http://github.com/lamortenera/epicseg.

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 119 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 6%
United Kingdom 2 2%
Germany 2 2%
Italy 1 <1%
Unknown 107 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 41 34%
Researcher 27 23%
Student > Master 11 9%
Student > Bachelor 10 8%
Student > Doctoral Student 6 5%
Other 14 12%
Unknown 10 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 43 36%
Biochemistry, Genetics and Molecular Biology 39 33%
Computer Science 13 11%
Mathematics 4 3%
Medicine and Dentistry 2 2%
Other 5 4%
Unknown 13 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 24 May 2016.
All research outputs
#2,938,799
of 16,639,069 outputs
Outputs from Genome Biology (Online Edition)
#2,079
of 3,507 outputs
Outputs of similar age
#46,048
of 237,231 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 2 outputs
Altmetric has tracked 16,639,069 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,507 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.2. This one is in the 40th percentile – i.e., 40% 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 237,231 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 79% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them