Base-resolution methylation patterns accurately predict transcription factor bindings in vivo.
Base-resolution methylation patterns accurately predict transcription factor bindings in vivo.
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DOI：
10.1093/nar/gkv151
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发表时间：
2015-03-11
影响因子：
14.9
通讯作者：
Qin ZS
中科院分区：
文献类型：
--
作者：
Xu T;Li B;Zhao M;Szulwach KE;Street RC;Lin L;Yao B;Zhang F;Jin P;Wu H;Qin ZS
检测体内转录因子（TF）结合对于理解基因调控回路是重要的。ChIP-seq是一种强有力的技术，可凭经验确定体内TF结合。然而，众多不同的TF使得它们的全基因组分析都是劳动密集型和昂贵的。已经开发了用于TF结合的计算机预测的算法，主要基于组蛋白修饰或DNA酶I超敏性数据结合DNA基序和其他基因组特征。然而，这些方法的技术局限性使其无法广泛应用，特别是在临床环境中。我们进行了一项涉及多种细胞系、TF和甲基化类型的全面调查，发现TF结合与结合位点周围甲基化水平变化之间存在密切关系。利用DNA甲基化和TF结合之间的联系，我们提出了一种新的监督学习方法来预测TF-DNA相互作用，使用来自碱基分辨率全基因组甲基化测序实验的数据。我们设计了β-二项式模型来表征TF结合位点和背景周围的甲基化数据。沿着其他静态基因组特征，我们采用随机森林框架来预测TF-DNA相互作用。在进行全面测试后，我们发现所提出的方法准确地预测了TF结合，并且与竞争方法相比表现良好。
Detecting in vivo transcription factor (TF) binding is important for understanding gene regulatory circuitries. ChIP-seq is a powerful technique to empirically define TF binding in vivo. However, the multitude of distinct TFs makes genome-wide profiling for them all labor-intensive and costly. Algorithms for in silico prediction of TF binding have been developed, based mostly on histone modification or DNase I hypersensitivity data in conjunction with DNA motif and other genomic features. However, technical limitations of these methods prevent them from being applied broadly, especially in clinical settings. We conducted a comprehensive survey involving multiple cell lines, TFs, and methylation types and found that there are intimate relationships between TF binding and methylation level changes around the binding sites. Exploiting the connection between DNA methylation and TF binding, we proposed a novel supervised learning approach to predict TF–DNA interaction using data from base-resolution whole-genome methylation sequencing experiments. We devised beta-binomial models to characterize methylation data around TF binding sites and the background. Along with other static genomic features, we adopted a random forest framework to predict TF–DNA interaction. After conducting comprehensive tests, we saw that the proposed method accurately predicts TF binding and performs favorably versus competing methods.
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DOI：
10.1073/pnas.1204398110
发表时间：
2013-04-23
影响因子：
11.1
作者：
Ji, Hongkai;Li, Xia;Ning, Yang
通讯作者：
Ning, Yang
影响因子：
14.9
作者：
Feng H;Conneely KN;Wu H
通讯作者：
Wu H
影响因子：
30.8
作者：
He, Housheng Hansen;Meyer, Clifford A.;Shin, Hyunjin;Bailey, Shannon T.;Wei, Gang;Wang, Qianben;Zhang, Yong;Xu, Kexin;Ni, Min;Lupien, Mathieu;Mieczkowski, Piotr;Lieb, Jason D.;Zhao, Keji;Brown, Myles;Liu, X. Shirley
通讯作者：
Liu, X. Shirley
影响因子：
25
作者：
Guo, Junjie U.;Su, Yijing;Shin, Joo Heon;Shin, Jaehoon;Li, Hongda;Xie, Bin;Zhong, Chun;Hu, Shaohui;Le, Thuc;Fan, Guoping;Zhu, Heng;Chang, Qiang;Gao, Yuan;Ming, Guo-li;Song, Hongjun
通讯作者：
Song, Hongjun
影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J