A prior-based integrative framework for functional transcriptional regulatory network inference.
A prior-based integrative framework for functional transcriptional regulatory network inference.
复制标题
用于功能转录调控网络推理的基于先验的综合框架。
DOI：
10.1093/nar/gkw1160
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发表时间：
2017
影响因子：
14.9
通讯作者：
Roy,Sushmita
中科院分区：
文献类型：
--
作者：
Siahpirani,AlirezaF;Roy,Sushmita
转录调控网络指定控制基因的上下文特异性表达水平的调控蛋白。全基因组调控网络的推断是理解基因调控的核心，但仍然是一个开放的挑战。基于表达的网络推断是推断调控网络的最流行的方法之一，然而，从这样的方法推断的网络与实验导出的网络（例如ChIP芯片和转录因子（TF）敲除）具有低重叠。目前，我们对这种差异的理解有限。为了解决这一差距，我们首先开发了一个监管网络推理算法，基于概率图形模型，集成表达与辅助数据集支持监管边缘。其次，我们全面分析了我们和其他国家的最先进的方法在不同的表达扰动数据集。通过整合序列特异性基序与表达推断的网络与实验衍生的网络具有更大的一致性，同时比基于基序的网络更能预测表达。我们的分析表明，自然遗传变异是网络推理中信息量最大的扰动，并确定了其目标可从表达中预测的核心TF。多种原因使得其他TF的靶点难以识别，包括网络结构和TF mRNA水平的变化不足。最后，我们证明了我们的推理算法的实用性，以推断压力特定的监管网络和监管机构的优先级。
Transcriptional regulatory networks specify regulatory proteins controlling the context-specific expression levels of genes. Inference of genome-wide regulatory networks is central to understanding gene regulation, but remains an open challenge. Expression-based network inference is among the most popular methods to infer regulatory networks, however, networks inferred from such methods have low overlap with experimentally derived (e.g. ChIP-chip and transcription factor (TF) knockouts) networks. Currently we have a limited understanding of this discrepancy. To address this gap, we first develop a regulatory network inference algorithm, based on probabilistic graphical models, to integrate expression with auxiliary datasets supporting a regulatory edge. Second, we comprehensively analyze our and other state-of-the-art methods on different expression perturbation datasets. Networks inferred by integrating sequence-specific motifs with expression have substantially greater agreement with experimentally derived networks, while remaining more predictive of expression than motif-based networks. Our analysis suggests natural genetic variation as the most informative perturbation for network inference, and, identifies core TFs whose targets are predictable from expression. Multiple reasons make the identification of targets of other TFs difficult, including network architecture and insufficient variation of TF mRNA level. Finally, we demonstrate the utility of our inference algorithm to infer stress-specific regulatory networks and for regulator prioritization.