Constructing the dynamic transcriptional regulatory networks to identify phenotype-specific transcription regulators.

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Briefings in bioinformatics Pub Date : 2024-09-23 DOI:10.1093/bib/bbae542
Yang Guo, Zhiqiang Xiao
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Abstract

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.

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构建动态转录调控网络,识别表型特异性转录调控因子。
转录调控网络(TRN)是一个图形框架,有助于理解转录过程中复杂的转录调控机制。识别表型特异性转录调控因子对于揭示转录元件在特定表型中的功能作用至关重要。过去十年中,虽然有很多基于静态 TRN 检测表型特异性转录元件的方法,但由于静态模型通常忽略了转录调节因子的动态特征,因此大多数方法在多层次阐明转录调节因子与表型相关的功能作用方面并不理想。在本研究中,我们引入了一种名为 DTGN 的新框架,通过构建动态 TRN 来识别表型特异的转录因子(TFs)和通路。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征。然后,基于学习到的基因时间表征,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 中识别出表型特异的 TF 和通路。多个表型数据集的研究结果表明,在识别表型特异性 TF 和通路方面,所提出的 DTGN 框架优于大多数现有方法。我们的框架为探索动态模型中与特定表型相关的转录调节因子的功能作用提供了一种新方法。
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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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