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MeDReaders:一个与甲基化 DNA 结合的转录因子数据库。

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

机构信息

School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.

The Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.

出版信息

Nucleic Acids Res. 2018 Jan 4;46(D1):D146-D151. doi: 10.1093/nar/gkx1096.

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/.

摘要

理解转录因子(TFs)与 DNA 靶标相互作用的分子原理是转录调控的主要课题之一。最近,新出现的证据表明,一些 TF 可以在体外和体内结合含有高度甲基化 CpG 的 DNA 基序。鉴定这些 TF 并阐明其生理作用,现在成为理解甲基化介导的生物学过程机制的重要基石,这对人类疾病和疾病发展具有至关重要的意义。因此,我们构建了一个名为 MeDReaders 的数据库,用于收集有关甲基化 DNA 结合活性的信息。在文献中报道的人类和小鼠研究中,共手动整理了 731 种可与甲基化 DNA 序列结合的 TF。通过整合来自 ENCODE 和 GEO 数据库的六个人类细胞系和一个小鼠细胞系的全基因组亚硫酸氢盐测序(WGBS)和 ChIP-Seq 数据集,应用计算方法预测了 292 种 TF 的甲基化和非甲基化基序。MeDReaders 数据库将为进一步的研究提供一个全面的资源,并有助于相关的实验设计。该数据库为用户提供了对大多数涉及这种与甲基化相关结合活性的 TF 的统一访问。该网站可在 http://medreader.org/ 上获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc40/5753207/3c61f658bbd9/gkx1096fig1.jpg

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