Predicting stimulation-dependent enhancer-promoter interactions from ChIP-Seq time course data.
Predicting stimulation-dependent enhancer-promoter interactions from ChIP-Seq time course data.
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作者：
Dzida T;Iqbal M;Charapitsa I;Reid G;Stunnenberg H;Matarese F;Grote K;Honkela A;Rattray M
我们开发了一种机器学习方法，利用基因组蛋白占用随时间变化的证据来预测刺激依赖性增强子-启动子相互作用。通过ChIP-Seq实验，在雌激素刺激的MCF7细胞中测量雌激素受体α (ERα)、RNA聚合酶（Pol II）和组蛋白标记H2AZ和H3K4me3的占用率。开发了贝叶斯分类器，该分类器使用增强子和启动子的时间结合模式的相关性以及基因组接近性作为预测相互作用的特征。该方法使用来自同一系统的实验确定的相互作用进行训练，并被证明比基于最近的ERα结合的基因组接近度的预测获得更高的精度。我们使用该方法在全基因组范围内鉴定了一组可靠的ERα靶基因及其调控增强子。公开可用的GRO-Seq数据验证表明，我们预测的靶标比仅基于基因组ERα结合接近度的预测更有可能显示早期新生转录。
We have developed a machine learning approach to predict stimulation-dependent enhancer-promoter interactions using evidence from changes in genomic protein occupancy over time. The occupancy of estrogen receptor alpha (ERα), RNA polymerase (Pol II) and histone marks H2AZ and H3K4me3 were measured over time using ChIP-Seq experiments in MCF7 cells stimulated with estrogen. A Bayesian classifier was developed which uses the correlation of temporal binding patterns at enhancers and promoters and genomic proximity as features to predict interactions. This method was trained using experimentally determined interactions from the same system and was shown to achieve much higher precision than predictions based on the genomic proximity of nearest ERα binding. We use the method to identify a genome-wide confident set of ERα target genes and their regulatory enhancers genome-wide. Validation with publicly available GRO-Seq data demonstrates that our predicted targets are much more likely to show early nascent transcription than predictions based on genomic ERα binding proximity alone.
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DOI：
10.1093/bioinformatics/btq033
发表时间：
2010-03-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Quinlan AR;Hall IM
通讯作者：
Hall IM
影响因子：
64.8
作者：
Ernst, Jason;Kheradpour, Pouya;Mikkelsen, Tarjei S.;Shoresh, Noam;Ward, Lucas D.;Epstein, Charles B.;Zhang, Xiaolan;Wang, Li;Issner, Robbyn;Coyne, Michael;Ku, Manching;Durham, Timothy;Kellis, Manolis;Bernstein, Bradley E.
通讯作者：
Bernstein, Bradley E.
影响因子：
12.3
作者：
Li G;Fullwood MJ;Xu H;Mulawadi FH;Velkov S;Vega V;Ariyaratne PN;Mohamed YB;Ooi HS;Tennakoon C;Wei CL;Ruan Y;Sung WK
通讯作者：
Sung WK
影响因子：
64.8
作者：
Jin, Fulai;Li, Yan;Dixon, Jesse R.;Selvaraj, Siddarth;Ye, Zhen;Lee, Ah Young;Yen, Chia-An;Schmitt, Anthony D.;Espinoza, Celso A.;Ren, Bing
通讯作者：
Ren, Bing
鉴定雌激素受体结合与乳腺癌的临床结局有关。
影响因子：
64.8
作者：
Ross-Innes, Caryn S.;Stark, Rory;Teschendorff, Andrew E.;Holmes, Kelly A.;Ali, H. Raza;Dunning, Mark J.;Brown, Gordon D.;Gojis, Ondrej;Ellis, Ian O.;Green, Andrew R.;Ali, Simak;Chin, Suet-Feung;Palmieri, Carlo;Caldas, Carlos;Carroll, Jason S.
通讯作者：
Carroll, Jason S.