School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
Brief Bioinform. 2024 Sep 23;25(6). doi: 10.1093/bib/bbae542.
The transcriptional regulatory network (TRN) is a graph framework that helps understand the complex transcriptional regulation mechanisms in the transcription process. Identifying the phenotype-specific transcription regulators is vital to reveal the functional roles of transcription elements in associating the specific phenotypes. Although many methods have been developed towards detecting the phenotype-specific transcription elements based on the static TRN in the past decade, most of them are not satisfactory for elucidating the phenotype-related functional roles of transcription regulators in multiple levels, as the dynamic characteristics of transcription regulators are usually ignored in static models. In this study, we introduce a novel framework called DTGN to identify the phenotype-specific transcription factors (TFs) and pathways by constructing dynamic TRNs. We first design a graph autoencoder model to integrate the phenotype-oriented time-series gene expression data and static TRN to learn the temporal representations of genes. Then, based on the learned temporal representations of genes, we develop a statistical method to construct a series of dynamic TRNs associated with the development of specific phenotypes. Finally, we identify the phenotype-specific TFs and pathways from the constructed dynamic TRNs. Results from multiple phenotypic datasets show that the proposed DTGN framework outperforms most existing methods in identifying phenotype-specific TFs and pathways. Our framework offers a new approach to exploring the functional roles of transcription regulators that associate with specific phenotypes in a dynamic model.
转录调控网络（TRN）是一种图形框架，有助于理解转录过程中复杂的转录调控机制。鉴定表型特异性转录调控因子对于揭示转录元件在与特定表型相关联中的功能作用至关重要。尽管在过去十年中，已经开发了许多基于静态 TRN 的方法来检测表型特异性转录元件，但大多数方法都不能令人满意地阐明转录调控因子在多个层面上与表型相关的功能作用，因为静态模型通常忽略了转录调控因子的动态特征。在本研究中，我们引入了一种称为 DTGN 的新框架，通过构建动态 TRN 来鉴定表型特异性转录因子（TF）和途径。我们首先设计了一个图自动编码器模型，将面向表型的时间序列基因表达数据和静态 TRN 整合在一起，以学习基因的时间表示。然后，基于学习到的基因时间表示，我们开发了一种统计方法来构建一系列与特定表型发展相关的动态 TRN。最后，我们从构建的动态 TRN 中鉴定表型特异性 TF 和途径。来自多个表型数据集的结果表明，所提出的 DTGN 框架在识别表型特异性 TF 和途径方面优于大多数现有方法。我们的框架为探索与特定表型相关联的转录调控因子的功能作用提供了一种新方法，该方法在动态模型中进行。
