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DRME specifications


Unique identifier OMICS_12001
Alternative name Differential RNA MEthylation
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
Computer skills Advanced
Stability Stable
Maintained Yes


No version available



  • person_outline Jia Meng <>

Publication for Differential RNA MEthylation

DRME citations


Systems Biology Approaches to Mining High Throughput Biological Data

PMCID: 4539426
PMID: 26347339
DOI: 10.1155/2015/504362

[…] two conditions. however, existing peak-based methods could not effectively differentiate multiple methylation residuals located within a single methylation site. in the paper “spatially enhanced differential rna methylation analysis from affinity-based sequencing data with hidden markov model,” y.-c. zhang et al. proposed a hidden markov model (hmm) based approach to address this issue. […]


Spatially Enhanced Differential RNA Methylation Analysis from Affinity Based Sequencing Data with Hidden Markov Model

PMCID: 4537718
PMID: 26301253
DOI: 10.1155/2015/852070

[…] [] and systems biology approaches for decomposing the rna methylome to unveil the latent enzymatic regulators of epitranscriptome []. software tools for rna methylation site detection [, ] and for differential rna methylation analysis [] from merip-seq data are now available in a rather user friendly manner. nevertheless, as a newly arising technique, merip-seq still poses computational […]


Widespread occurrence of 5 methylcytosine in human coding and non coding RNA

PMCID: 3367185
PMID: 22344696
DOI: 10.1093/nar/gks144

[…] rna methylation patterns we have observed are dynamically responding to, and/or supportive of, distinct states of cellular growth, differentiation and transformation. comparative studies of differential rna methylation, particularly in the cancer and stem cell contexts, and in cells depleted of the candidate rna mtases are now warranted., a key outcome of this study was the clear […]

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DRME institution(s)
Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, XI'an, China; Picower Institute for Learning and Memory, Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Suzhou Urban and Environmental Research Institute, Huai'an Research Institute of New-type Urbanization, Department of Environmental Sciences, XI'an Jiaotong–Liverpool University, Suzhou, China; Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, USA; Institute of Biophysics, Chinese Academy of Sciences, Beijing, China; Department of Biological Sciences, XI'an Jiaotong–Liverpool University, Suzhou, China
DRME funding source(s)
National Natural Science Foundation of China (61401370, 61473232, 91430111, 61170134 and 61301220); Jiangsu Science and Technology Program (BK20140403)

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