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  • Good Attention Score compared to outputs of the same age (70th percentile)

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60 Mendeley
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3 CiteULike
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Article details
Title
A probabilistic generative model for quantification of DNA modifications enables analysis of demethylation pathways
Published in
Genome Biology, March 2016
DOI 10.1186/s13059-016-0911-6
Pubmed ID
Authors
Abstract

We present a generative model, Lux, to quantify DNA methylation modifications from any combination of bisulfite sequencing approaches, including reduced, oxidative, TET-assisted, chemical-modification assisted, and methylase-assisted bisulfite sequencing data. Lux models all cytosine modifications (C, 5mC, 5hmC, 5fC, and 5caC) simultaneously together with experimental parameters, including bisulfite conversion and oxidation efficiencies, as well as various chemical labeling and protection steps. We show that Lux improves the quantification and comparison of cytosine modification levels and that Lux can process any oxidized methylcytosine sequencing data sets to quantify all cytosine modifications. Analysis of targeted data from Tet2-knockdown embryonic stem cells and T cells during development demonstrates DNA modification quantification at unprecedented detail, quantifies active demethylation pathways and reveals 5hmC localization in putative regulatory regions.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United Kingdom 2 3%
Japan 1 2%
Italy 1 2%
Brazil 1 2%
Unknown 55 92%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 16 27%
Student > Ph. D. Student 12 20%
Student > Master 5 8%
Student > Doctoral Student 4 7%
Student > Bachelor 4 7%
Other 9 15%
Unknown 10 17%
Readers by discipline
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 17 28%
Agricultural and Biological Sciences 17 28%
Medicine and Dentistry 6 10%
Computer Science 5 8%
Neuroscience 2 3%
Other 3 5%
Unknown 10 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 18 May 2016.
All research outputs
#7,722,074
of 27,781,301 outputs
Outputs from Genome Biology
#3,370
of 4,749 outputs
Outputs of similar age
#93,294
of 317,212 outputs
Outputs of similar age from Genome Biology
#52
of 63 outputs
Altmetric has tracked 27,781,301 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 4,749 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.3. This one is in the 28th percentile – i.e., 28% 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 317,212 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 70% of its contemporaries.
We're also able to compare this research output to 63 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.