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BIMMER: a novel algorithm for detecting differential DNA methylation regions from MBDCap-seq data

Overview of attention for article published in BMC Bioinformatics, November 2014
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Title
BIMMER: a novel algorithm for detecting differential DNA methylation regions from MBDCap-seq data
Published in
BMC Bioinformatics, November 2014
DOI 10.1186/1471-2105-15-s12-s6
Pubmed ID
Authors

Zijing Mao, Chifeng Ma, Tim H-M Huang, Yidong Chen, Yufei Huang

Abstract

DNA methylation is a common epigenetic marker that regulates gene expression. A robust and cost-effective way for measuring whole genome methylation is Methyl-CpG binding domain-based capture followed by sequencing (MBDCap-seq). In this study, we proposed BIMMER, a Hidden Markov Model (HMM) for differential Methylation Regions (DMRs) identification, where HMMs were proposed to model the methylation status in normal and cancer samples in the first layer and another HMM was introduced to model the relationship between differential methylation and methylation statuses in normal and cancer samples. To carry out the prediction for BIMMER, an Expectation-Maximization algorithm was derived. BIMMER was validated on the simulated data and applied to real MBDCap-seq data of normal and cancer samples. BIMMER revealed that 8.83% of the breast cancer genome are differentially methylated and the majority are hypo-methylated in breast cancer.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Turkey 1 4%
Unknown 25 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 27%
Student > Postgraduate 4 15%
Professor > Associate Professor 4 15%
Student > Master 3 12%
Researcher 3 12%
Other 4 15%
Unknown 1 4%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 27%
Agricultural and Biological Sciences 6 23%
Computer Science 5 19%
Nursing and Health Professions 1 4%
Business, Management and Accounting 1 4%
Other 5 19%
Unknown 1 4%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 05 December 2014.
All research outputs
#18,385,510
of 22,772,779 outputs
Outputs from BMC Bioinformatics
#6,306
of 7,276 outputs
Outputs of similar age
#188,188
of 262,799 outputs
Outputs of similar age from BMC Bioinformatics
#125
of 144 outputs
Altmetric has tracked 22,772,779 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,276 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% of its peers scored the same or lower than it.
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We're also able to compare this research output to 144 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.