CRNET: an efficient sampling approach to infer functional regulatory networks by integrating large-scale ChIP-seq and time-course RNA-seq data
CRNET: an efficient sampling approach to infer functional regulatory networks by integrating large-scale ChIP-seq and time-course RNA-seq data
复制标题
DOI：
10.1093/bioinformatics/btx827
复制
发表时间：
2018-05-15
期刊：
影响因子：
5.8
通讯作者：
Xuan, Jianhua
中科院分区：
文献类型：
--
作者：
Chen, Xi;Gu, Jinghua;Xuan, Jianhua
动机：NGS技术已广泛应用于遗传和表观遗传学研究。现在可以联合使用多个ChIP-seq和RNA-seq图谱来推断功能性调控网络（FRN）。然而，现有的方法遭受要么过于简化的假设转录因子（TF）调节或缓慢收敛的采样FRN推断从大规模ChIPseq和时间过程RNA-seq data.Results：我们开发了一个有效的贝叶斯集成方法（CRNET）FRN推断使用两阶段吉布斯采样器迭代估计隐藏的TF活动和后验概率的结合事件。一种新的统计测量，共同考虑监管强度和回归误差，使CRNET的采样过程能够快速收敛，从而使CRNET非常有效的大规模FRN推理。对合成数据和基准数据的实验表明，与现有方法相比，CRNET的性能显着提高。将CRNET应用于乳腺癌数据以鉴定乳腺癌MCF-7细胞中在启动子或增强子区域起作用的FRN。转录因子MYC被预测为启动子和增强子FRN中的关键功能因子。我们在MCF-7细胞中使用适当的RNAi方法实验验证了MYC对CRNET预测的靶基因的调节作用。
Motivation: NGS techniques have been widely applied in genetic and epigenetic studies. Multiple ChIP-seq and RNA-seq profiles can now be jointly used to infer functional regulatory networks (FRNs). However, existing methods suffer from either oversimplified assumption on transcription factor (TF) regulation or slow convergence of sampling for FRN inference from large-scale ChIPseq and time-course RNA-seq data.Results: We developed an efficient Bayesian integration method (CRNET) for FRN inference using a two-stage Gibbs sampler to estimate iteratively hidden TF activities and the posterior probabilities of binding events. A novel statistic measure that jointly considers regulation strength and regression error enables the sampling process of CRNET to converge quickly, thus making CRNET very efficient for large-scale FRN inference. Experiments on synthetic and benchmark data showed a significantly improved performance of CRNET when compared with existing methods. CRNET was applied to breast cancer data to identify FRNs functional at promoter or enhancer regions in breast cancer MCF-7 cells. Transcription factor MYC is predicted as a key functional factor in both promoter and enhancer FRNs. We experimentally validated the regulation effects of MYC on CRNET-predicted target genes using appropriate RNAi approaches in MCF-7 cells.