IEEE/ACM Trans Comput Biol Bioinform. 2020 Mar-Apr;17(2):516-525. doi: 10.1109/TCBB.2018.2869590. Epub 2018 Sep 10.
Accurately reconstructing gene regulatory networks (GRNs) from high-throughput gene expression data has been a major challenge in systems biology for decades. Many approaches have been proposed to solve this problem. However, there is still much room for the improvement of GRN inference. Integrating data from different sources is a promising strategy. Epigenetic modifications have a close relationship with gene regulation. Hence, epigenetic data such as histone modification profiles can provide useful information for uncovering regulatory interactions between genes. In this paper, we propose a method to integrate epigenetic data into the inference of GRNs. In particular, a dynamic Bayesian network (DBN) is employed to infer gene regulations from time-series gene expression data. Epigenetic data (histone modification profiles here) are integrated into the prior probability distribution of the Bayesian model. Our method has been validated on both synthetic and real datasets. Experimental results show that the integration of epigenetic data can significantly improve the performance of GRN inference. As more epigenetic datasets become available, our method would be useful for elucidating the gene regulatory mechanisms driving various cellular activities. The source code and testing datasets are available at https://github.com/Zheng-Lab/MetaGRN/tree/master/histonePrior.
准确地从高通量基因表达数据中重建基因调控网络（GRNs）是系统生物学几十年来的主要挑战。已经提出了许多方法来解决这个问题。然而，GRN 推断仍然有很大的改进空间。整合来自不同来源的数据是一种很有前途的策略。表观遗传修饰与基因调控密切相关。因此，表观遗传数据（如组蛋白修饰谱）可以为揭示基因之间的调控相互作用提供有用的信息。在本文中，我们提出了一种将表观遗传数据整合到 GRN 推断中的方法。具体来说，我们使用动态贝叶斯网络（DBN）从时间序列基因表达数据中推断基因调控。将表观遗传数据（此处为组蛋白修饰谱）整合到贝叶斯模型的先验概率分布中。我们的方法已经在合成和真实数据集上进行了验证。实验结果表明，整合表观遗传数据可以显著提高 GRN 推断的性能。随着更多的表观遗传数据集的出现，我们的方法将有助于阐明驱动各种细胞活动的基因调控机制。源代码和测试数据集可在 https://github.com/Zheng-Lab/MetaGRN/tree/master/histonePrior 上获得。
