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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, 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 (78th percentile)

Mentioned by

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15 X users

Citations

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85 Dimensions

Readers on

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122 Mendeley
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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, 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 15 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 122 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 110 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 41 34%
Researcher 27 22%
Student > Master 13 11%
Student > Bachelor 10 8%
Student > Doctoral Student 7 6%
Other 12 10%
Unknown 12 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 43 35%
Biochemistry, Genetics and Molecular Biology 39 32%
Computer Science 14 11%
Mathematics 4 3%
Medicine and Dentistry 2 2%
Other 5 4%
Unknown 15 12%
Attention Score in Context

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
#4,835,465
of 25,371,288 outputs
Outputs from Genome Biology
#2,799
of 4,467 outputs
Outputs of similar age
#55,829
of 274,966 outputs
Outputs of similar age from Genome Biology
#49
of 62 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
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 36th percentile – i.e., 36% 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 274,966 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 78% of its contemporaries.
We're also able to compare this research output to 62 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.