MeDReaders: a database for transcription factors that bind to methylated DNA.

Guohua Wang, Ximei Luo, Jianan Wang, Jun Wan, Shuli Xia, Heng Zhu, Jiang Qian, Yadong Wang
Author Information
  1. Guohua Wang: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.
  2. Ximei Luo: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.
  3. Jianan Wang: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.
  4. Jun Wan: Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
  5. Shuli Xia: Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
  6. Heng Zhu: Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
  7. Jiang Qian: The Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
  8. Yadong Wang: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.

Abstract

Understanding the molecular principles governing interactions between transcription factors (TFs) and DNA targets is one of the main subjects for transcriptional regulation. Recently, emerging evidence demonstrated that some TFs could bind to DNA motifs containing highly methylated CpGs both in vitro and in vivo. Identification of such TFs and elucidation of their physiological roles now become an important stepping-stone toward understanding the mechanisms underlying the methylation-mediated biological processes, which have crucial implications for human disease and disease development. Hence, we constructed a database, named as MeDReaders, to collect information about methylated DNA binding activities. A total of 731 TFs, which could bind to methylated DNA sequences, were manually curated in human and mouse studies reported in the literature. In silico approaches were applied to predict methylated and unmethylated motifs of 292 TFs by integrating whole genome bisulfite sequencing (WGBS) and ChIP-Seq datasets in six human cell lines and one mouse cell line extracted from ENCODE and GEO database. MeDReaders database will provide a comprehensive resource for further studies and aid related experiment designs. The database implemented unified access for users to most TFs involved in such methylation-associated binding actives. The website is available at http://medreader.org/.

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Grants

  1. K08 NS063956/NINDS NIH HHS
  2. R01 NS091165/NINDS NIH HHS
  3. U54 HD079123/NICHD NIH HHS

MeSH Term

Animals
Binding Sites
Cell Line
DNA
DNA Methylation
Databases, Genetic
Humans
Knowledge Bases
Mice
Nucleotide Motifs
Sequence Analysis, DNA
Transcription Factors
Whole Genome Sequencing

Chemicals

Transcription Factors
DNA

Word Cloud

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