QDMR statistics

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QDMR specifications

Information


Unique identifier OMICS_00623
Name QDMR
Alternative name Quantitative Differentially Methylated Region
Software type Application/Script, Package/Module
Interface Command line interface, Graphical user interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages Java
Computer skills Medium
Version 1.0
Stability Stable
Requirements
EpiDiff
Maintained Yes

Versioning


Version Operating System DOI Size Download Info
1.0
32/64 bits
10.24347/OMIC_623_1.0/wlm8664 21.17 Mo get_app

Documentation


Maintainer


Additional information


QDMR is embedded in EpiDiff as the second module.

Publication for Quantitative Differentially Methylated Region

QDMR in pipeline

2016
PMCID: 4957136
PMID: 27428748
DOI: 10.1038/ng.3610

[…] the four genotypes. next, the mean methylation levels of cpgs covered with at least 5 reads in dmrs were computed in each genotype. finally, the dmrs were further assessed by a quantitative method qdmr based on shannon entropy among the four genotypes. the dmrs with default entropy value <2 remained. to study the patterns of dynamic change of dna methylation and their coordinate regulation […]


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QDMR in publications

 (5)
PMCID: 5593605
PMID: 28915634
DOI: 10.18632/oncotarget.18072

[…] 75%, while in the other type ofgametes less than 25%, with a significant p < 0.05 given by multiple student's t-test and a benjamini-hochberg false discovery rate (fdr) < 0.05., we applied qdmr (version 1.0) to analyze d-dmrs with default parameters. each region was assigned an entropy value by qdmr based on the methylation levels for all the samples. the regions whose entropy is less […]

PMCID: 5138066
PMID: 27980397
DOI: 10.4137/BBI.S38427

[…] nonparametric and kernel-based method (m3d), beta-binomial model (methylsig), beta-binomial hierarchical model (moabs), hidden markov model (nhmmfdr), three-state hmm (methpipe), shannon entropy (qdmr), and a binary segmentation algorithm combined with a two-dimensional statistical test (metilene). usually, the latest software compares with some previous methods and claims best performance, […]

PMCID: 5032785
PMID: 27688708
DOI: 10.4137/CIN.S40300

[…] ti,b=∑s=1n[w(ui,s)×mi,s]∑s=1nw(ui,s) is a weighted mean. a processed methylation level m′i,,s for sample s is then calculated as m′i,s = |mi,s – ti,b|. using the transformed methylation levels, qdmr first defines an entropy score as: hp=−∑s=1np′i,slog2(p′i,s), where p′i,s=mi,s/∑s=1nm′i,s. to account for the range of variation, qdmr introduces a methylation weight to modify the above hp. […]

PMCID: 4440512
PMID: 25997618
DOI: 10.1186/s12864-015-1552-y

[…] annotation information was based on the hg19 human genome (http://genome.ucsc.edu). differentially methylated regions (dmrs) were identified by quantitative differentially methylated regions (qdmr) []. the qdmr is a quantitative approach to quantify methylation difference and identify dmrs from genome-wide methylation profiles with a concept of ‘dna methylation entropy’ []. the ‘dna […]

PMCID: 4008136
PMID: 24106769
DOI: 10.1186/1471-2164-14-692

[…] each cell type using the pearson correlation (mean = 0.97). to identify cgis that were differentially methylated between two replicas of the same cell line, we have sequentially applied two methods: quantitative differentially methylated region (qdmr) and hypergeometric based approach (hba). the first is a quantitative method that identifies differentially methylated regions using […]


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QDMR institution(s)
College of Bioinformatics Science and Technology, Harbin Institute of Technology, Harbin, China; The Third Affiliated Hospital, Harbin Medical University, Harbin, China; Department of Life Science and Engineering, Harbin Institute of Technology, Harbin, China

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