Literature DB >> 24109772

EpiDiff: entropy-based quantitative identification of differential epigenetic modification regions from epigenomes.

Yan Zhang, Jangzhong Su, Di Yu, Qiong Wu, Haidan Yan.   

Abstract

Genome-wide epigenetic modification dynamics, including DNA methylation and chromatin modification, are involved in biological processes such as development, aging, and disease. Quantitative identification of differential epigenetic modification regions (DEMRs) from various temporal and spatial epigenomes is a crucial step towards investigating the relationship between epigenotype and phenotype. Here, we describe EpiDiff (http://bioinfo.hrbmu.edu.cn/epidiff/), an integrated software platform that supports quantification of epigenetic difference and identification of DEMRs by Shannon entropy. Two main modules, quantitative differential chromatin modification region (QDCMR) and quantitative differentially methylated region (QDMR) are provided for bioinformatic analysis of chromatin modifications and DNA methylation data, respectively. The third module, quantitative differential expressed gene (QDEG), can be used to identify differentially expressed genes. The platform-free and species-free nature of EpiDiff makes it potentially applicable to a wide variety of epigenomes at an unprecedented scale and resolution. The graphical user interface provides biologists with a practicable and reliable way to analyze and visualize epigenetic difference.

Mesh:

Year:  2013        PMID: 24109772     DOI: 10.1109/EMBC.2013.6609585

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  6 in total

1.  Mining the Prognostic Role of DNA Methylation Heterogeneity in Lung Adenocarcinoma.

Authors:  Hongying Liao; Xiaolong Luo; Yisheng Huang; Xingping Yang; Yuzhen Zheng; Xianyu Qin; Jian Tan; Piao Shen; Renjiang Tian; Weijie Cai; Xiaoshun Shi; Xiaofang Deng
Journal:  Dis Markers       Date:  2022-05-28       Impact factor: 3.464

2.  Comprehensive analysis of DNA methylation data with RnBeads.

Authors:  Yassen Assenov; Fabian Müller; Pavlo Lutsik; Jörn Walter; Thomas Lengauer; Christoph Bock
Journal:  Nat Methods       Date:  2014-09-28       Impact factor: 28.547

3.  ADMIRE: analysis and visualization of differential methylation in genomic regions using the Infinium HumanMethylation450 Assay.

Authors:  Jens Preussner; Julia Bayer; Carsten Kuenne; Mario Looso
Journal:  Epigenetics Chromatin       Date:  2015-12-01       Impact factor: 4.954

4.  DNA methylation-based classification and identification of renal cell carcinoma prognosis-subgroups.

Authors:  Wenbiao Chen; Jia Zhuang; Peizhong Peter Wang; Jingjing Jiang; Chenhong Lin; Ping Zeng; Yan Liang; Xujun Zhang; Yong Dai; Hongyan Diao
Journal:  Cancer Cell Int       Date:  2019-07-16       Impact factor: 5.722

5.  HDNA methylation data-based molecular subtype classification related to the prognosis of patients with hepatocellular carcinoma.

Authors:  Hui He; Di Chen; Shimeng Cui; Gang Wu; Hailong Piao; Xun Wang; Peng Ye; Shi Jin
Journal:  BMC Med Genomics       Date:  2020-08-24       Impact factor: 3.063

Review 6.  Ten Years of EWAS.

Authors:  Siyu Wei; Junxian Tao; Jing Xu; Xingyu Chen; Zhaoyang Wang; Nan Zhang; Lijiao Zuo; Zhe Jia; Haiyan Chen; Hongmei Sun; Yubo Yan; Mingming Zhang; Hongchao Lv; Fanwu Kong; Lian Duan; Ye Ma; Mingzhi Liao; Liangde Xu; Rennan Feng; Guiyou Liu; The Ewas Project; Yongshuai Jiang
Journal:  Adv Sci (Weinh)       Date:  2021-08-11       Impact factor: 16.806

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.