Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro, Mexico.
Institute for Infectious Diseases and Infection Control, Jena University Hospital, Jena, Germany; Integrated Research and Treatment Center, Center for Sepsis Control and Care, Jena University Hospital, Jena, Germany.
Biochimie. 2022 Feb;193:115-125. doi: 10.1016/j.biochi.2021.10.016. Epub 2021 Nov 2.
The transcriptional regulatory network (TRN) in a cell orchestrates spatio-temporal expression of genes to generate cellular responses for maintenance, reproduction, development and survival of the cell and its hosting organism. Transcription factors (TF) regulate the expression of their target genes (TG) and are the fundamental units of TRN. Several databases have been developed to catalogue human TRN based on low- and high-throughput experimental and computational studies considering their importance in understanding cellular physiology. However, literature lacks their comparative assessment on the strengths and weaknesses. We compared over 2.2 million regulatory pairs between 1379 TF and 22,518 TG from 14 resources. Our study reveals that the TF and TG were common across data resources but not their regulatory pairs. TF and TG of the regulatory pairs showed weak expression correlation, significant gene ontology overlap, co-citations in PubMed and low numbers of TF-TG pairs representing transcriptional repression relationships. We assigned each TF-TG regulatory pair a combined confidence score reflecting its reliability based on its presence in multiple databases. The assembled TRN contains 2,246,598 TF-TG pairs, of which, 44,284 with information on TF's activating or repressing effects on their TG and is available upon request. This study brings the information about transcriptional regulation scattered over the literature and databases at one place in the form of one of the most comprehensive and complete human TRN assembled to date. It will be a valuable resource for benchmarking TRN prediction tools, and to the scientific community working in functional genomics, gene expression and regulation analysis.
细胞中的转录调控网络 (TRN) 协调基因的时空表达,以产生细胞对维持、繁殖、发育和生存的反应,以及其宿主生物。转录因子 (TF) 调节其靶基因 (TG) 的表达,是 TRN 的基本单位。已经开发了几个数据库,根据低通量和高通量实验以及计算研究对人类 TRN 进行编目,考虑到它们在理解细胞生理学中的重要性。然而,文献缺乏对其优缺点的比较评估。我们比较了来自 14 个资源的 1379 个 TF 和 22518 个 TG 之间的超过 220 万个调控对。我们的研究表明,TF 和 TG 在数据资源中是常见的,但它们的调控对却不是。调控对的 TF 和 TG 表达相关性较弱,基因本体论重叠显著,PubMed 中有共同引用,并且代表转录抑制关系的 TF-TG 对数量较少。我们根据它们在多个数据库中的存在为每个 TF-TG 调控对分配了一个综合置信度得分,反映其可靠性。组装的 TRN 包含 2246598 个 TF-TG 对,其中 44284 对具有关于 TF 对其 TG 的激活或抑制作用的信息,并可根据要求提供。这项研究将散布在文献和数据库中的转录调控信息以一种迄今为止最全面和完整的人类 TRN 的形式汇集在一起。它将成为 TRN 预测工具基准测试的宝贵资源,并为从事功能基因组学、基因表达和调控分析的科学界提供帮助。