Zhu Fan, Shi Lihong, Li Hongdong, Eksi Ridvan, Engel James Douglas, Guan Yuanfang
Department of Computational Medicine and Bioinformatics, Department of Cell and Developmental Biology, Department of Internal Medicine and Department of Computer Science and Engineering, University of Michigan, MI48109, USA.
Department of Computational Medicine and Bioinformatics, Department of Cell and Developmental Biology, Department of Internal Medicine and Department of Computer Science and Engineering, University of Michigan, MI48109, USA Department of Computational Medicine and Bioinformatics, Department of Cell and Developmental Biology, Department of Internal Medicine and Department of Computer Science and Engineering, University of Michigan, MI48109, USA Department of Computational Medicine and Bioinformatics, Department of Cell and Developmental Biology, Department of Internal Medicine and Department of Computer Science and Engineering, University of Michigan, MI48109, USA.
Bioinformatics. 2014 Dec 1;30(23):3325-33. doi: 10.1093/bioinformatics/btu542. Epub 2014 Aug 12.
Functional relationship networks, which summarize the probability of co-functionality between any two genes in the genome, could complement the reductionist focus of modern biology for understanding diverse biological processes in an organism. One major limitation of the current networks is that they are static, while one might expect functional relationships to consistently reprogram during the differentiation of a cell lineage. To address this potential limitation, we developed a novel algorithm that leverages both differentiation stage-specific expression data and large-scale heterogeneous functional genomic data to model such dynamic changes. We then applied this algorithm to the time-course RNA-Seq data we collected for ex vivo human erythroid cell differentiation.
Through computational cross-validation and literature validation, we show that the resulting networks correctly predict the (de)-activated functional connections between genes during erythropoiesis. We identified known critical genes, such as HBD and GATA1, and functional connections during erythropoiesis using these dynamic networks, while the traditional static network was not able to provide such information. Furthermore, by comparing the static and the dynamic networks, we identified novel genes (such as OSBP2 and PDZK1IP1) that are potential drivers of erythroid cell differentiation. This novel method of modeling dynamic networks is applicable to other differentiation processes where time-course genome-scale expression data are available, and should assist in generating greater understanding of the functional dynamics at play across the genome during development.
The network described in this article is available at http://guanlab.ccmb.med.umich.edu/stageSpecificNetwork.
功能关系网络总结了基因组中任意两个基因之间共同发挥功能的概率，它可以补充现代生物学中还原论的研究重点，以理解生物体中各种生物过程。当前网络的一个主要局限性在于它们是静态的，而人们可能期望功能关系在细胞谱系分化过程中持续重新编程。为了解决这一潜在局限性，我们开发了一种新颖的算法，该算法利用分化阶段特异性表达数据和大规模异质功能基因组数据来模拟这种动态变化。然后，我们将此算法应用于我们为体外人类红细胞分化收集的时间进程RNA测序数据。
通过计算交叉验证和文献验证，我们表明所得网络能够正确预测红细胞生成过程中基因之间（去）激活的功能连接。我们使用这些动态网络识别出了已知的关键基因，如HBD和GATA1，以及红细胞生成过程中的功能连接，而传统的静态网络无法提供此类信息。此外，通过比较静态网络和动态网络，我们识别出了新基因（如OSBP2和PDZK1IP1），它们是红细胞分化的潜在驱动因素。这种模拟动态网络的新方法适用于其他可获得时间进程基因组规模表达数据的分化过程，并且应该有助于更深入地理解发育过程中全基因组的功能动态。
本文所述的网络可在http://guanlab.ccmb.med.umich.edu/stageSpecificNetwork获取。
