Reverse engineering of transcriptional regulatory networks (TRN) from genomics data has always represented a computational challenge in System Biology. The major issue is modeling the complex crosstalk among transcription factors (TFs) and their target genes, with a method able to handle both the high number of interacting variables and the noise in the available heterogeneous experimental sources of information. In this work, we propose a data fusion approach that exploits the integration of complementary omics-data as prior knowledge within a Bayesian framework, in order to learn and model large-scale transcriptional networks. We develop a hybrid structure-learning algorithm able to jointly combine TFs ChIP-Sequencing data and gene expression compendia to reconstruct TRNs in a genome-wide perspective. Applying our method to high-throughput data, we verified its ability to deal with the complexity of a genomic TRN, providing a snapshot of the synergistic TFs regulatory activity. Given the noisy nature of data-driven prior knowledge, which potentially contains incorrect information, we also tested the method’s robustness to false priors on a benchmark dataset, comparing the proposed approach to other regulatory network reconstruction algorithms. We demonstrated the effectiveness of our framework by evaluating structural commonalities of our learned genomic network with other existing networks inferred by different DNA binding information-based methods. This Bayesian omics-data fusion based methodology allows to gain a genome-wide picture of the transcriptional interplay, helping to unravel key hierarchical transcriptional interactions, which could be subsequently investigated, and it represents a promising learning approach suitable for multi-layered genomic data integration, given its robustness to noisy sources and its tailored framework for handling high dimensional data.

中文翻译：

---

基于贝叶斯数据融合的方法，用于学习全基因组转录调控网络。


利用基因组数据对转录调控网络（TRN）进行逆向工程一直是系统生物学中的计算挑战。主要问题是对转录因子 (TF) 及其靶基因之间的复杂串扰进行建模，采用一种能够处理大量相互作用变量和可用异质实验信息源中的噪声的方法。在这项工作中，我们提出了一种数据融合方法，利用互补组学数据的集成作为贝叶斯框架内的先验知识，以学习和建模大规模转录网络。我们开发了一种混合结构学习算法，能够联合结合 TF ChIP 测序数据和基因表达概要，从全基因组角度重建 TRN。将我们的方法应用于高通量数据，我们验证了其处理基因组 TRN 复杂性的能力，提供了协同 TF 调控活动的快照。考虑到数据驱动的先验知识的噪声性质（可能包含不正确的信息），我们还在基准数据集上测试了该方法对错误先验的鲁棒性，并将所提出的方法与其他监管网络重建算法进行了比较。我们通过评估我们学习的基因组网络与通过不同的基于 DNA 结合信息的方法推断的其他现有网络的结构共性，证明了我们框架的有效性。 这种基于贝叶斯组学数据融合的方法允许获得转录相互作用的全基因组图景，有助于揭示关键的分层转录相互作用，随后可以对其进行研究，并且它代表了一种适合多层基因组数据集成的有前途的学习方法，考虑到其对噪声源的鲁棒性以及用于处理高维数据的定制框架。