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A full Bayesian partition model for identifying hypo- and hyper-methylated loci from single nucleotide resolution sequencing data

Overview of attention for article published in BMC Bioinformatics, January 2016
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4 tweeters

Citations

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4 Dimensions

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Title
A full Bayesian partition model for identifying hypo- and hyper-methylated loci from single nucleotide resolution sequencing data
Published in
BMC Bioinformatics, January 2016
DOI 10.1186/s12859-015-0850-3
Pubmed ID
Authors

Henan Wang, Chong He, Garima Kushwaha, Dong Xu, Jing Qiu

Abstract

DNA methylation is an epigenetic modification that plays important roles on gene regulation. Study of whole-genome bisulfite sequencing and reduced representation bisulfite sequencing brings the availability of DNA methylation at single CpG resolution. The main interest of study on DNA methylation data is to test the methylation difference under two conditions of biological samples. However, the high cost and complexity of this sequencing experiment limits the number of biological replicates, which brings challenges to the development of statistical methods. Bayesian modeling is well known to be able to borrow strength across the genome, and hence is a powerful tool for high-dimensional- low-sample- size data. In order to provide accurate identification of methylation loci, especially for low coverage data, we propose a full Bayesian partition model to detect differentially methylated loci under two conditions of scientific study. Since hypo-methylation and hyper-methylation have distinct biological implication, it is desirable to differentiate these two types of differential methylation. The advantage of our Bayesian model is that it can produce one-step output of each locus being either equal-, hypo- or hyper-methylated locus without further post-hoc analysis. An R package named as MethyBayes implementing the proposed full Bayesian partition model will be submitted to the bioconductor website upon publication of the manuscript. The proposed full Bayesian partition model outperforms existing methods in terms of power while maintaining a low false discovery rate based on simulation studies and real data analysis including bioinformatics analysis.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 58 100%

Demographic breakdown

Readers by professional status Count As %
Student > Postgraduate 25 43%
Student > Bachelor 8 14%
Researcher 8 14%
Student > Ph. D. Student 3 5%
Student > Master 3 5%
Other 4 7%
Unknown 7 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 27 47%
Agricultural and Biological Sciences 14 24%
Computer Science 4 7%
Neuroscience 2 3%
Mathematics 2 3%
Other 1 2%
Unknown 8 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 29 January 2016.
All research outputs
#5,471,347
of 10,444,782 outputs
Outputs from BMC Bioinformatics
#2,462
of 4,169 outputs
Outputs of similar age
#137,813
of 329,881 outputs
Outputs of similar age from BMC Bioinformatics
#80
of 133 outputs
Altmetric has tracked 10,444,782 research outputs across all sources so far. This one is in the 46th percentile – i.e., 46% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,169 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 38th percentile – i.e., 38% 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 329,881 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 56% of its contemporaries.
We're also able to compare this research output to 133 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.