Yin Huachun, Duo Hongrui, Li Song, Qin Dan, Xie Lingling, Xiao Yingxue, Sun Jing, Tao Jingxin, Zhang Xiaoxi, Li Yinghong, Zou Yue, Yang Qingxia, Yang Xian, Hao Youjin, Li Bo
College of Life Sciences, Chongqing Normal University, Chongqing 401331, PR China; Department of Neurosurgery, Xinqiao Hospital, The Army Medical University, Chongqing 400037, PR China; Department of Neurobiology, Chongqing Key Laboratory of Neurobiology, The Army Medical University, Chongqing 400038, PR China.
College of Life Sciences, Chongqing Normal University, Chongqing 401331, PR China.
J Adv Res. 2024 Dec 6. doi: 10.1016/j.jare.2024.12.004.
Identifying differentially expressed genes (DEGs) is a core task of transcriptome analysis, as DEGs can reveal the molecular mechanisms underlying biological processes. However, interpreting the biological significance of large DEG lists is challenging. Currently, gene ontology, pathway enrichment and protein-protein interaction analysis are common strategies employed by biologists. Additionally, emerging analytical strategies/approaches (such as network module analysis, knowledge graph, drug repurposing, cell marker discovery, trajectory analysis, and cell communication analysis) have been proposed. Despite these advances, comprehensive guidelines for systematically and thoroughly mining the biological information within DEGs remain lacking.
This review aims to provide an overview of essential concepts and methodologies for the biological interpretation of DEGs, enhancing the contextual understanding. It also addresses the current limitations and future perspectives of these approaches, highlighting their broad applications in deciphering the molecular mechanism of complex diseases and phenotypes. To assist users in extracting insights from extensive datasets, especially various DEG lists, we developed DEGMiner (https://www.ciblab.net/DEGMiner/), which integrates over 300 easily accessible databases and tools.
This review offers strong support and guidance for exploring DEGs, and also will accelerate the discovery of hidden biological insights within genomes.
识别差异表达基因（DEGs）是转录组分析的核心任务，因为差异表达基因能够揭示生物过程背后的分子机制。然而，解读大量差异表达基因列表的生物学意义具有挑战性。目前，基因本体论、通路富集分析和蛋白质-蛋白质相互作用分析是生物学家常用的策略。此外，还提出了一些新兴的分析策略/方法（如网络模块分析、知识图谱、药物再利用、细胞标志物发现、轨迹分析和细胞通讯分析）。尽管取得了这些进展，但仍然缺乏用于系统、全面挖掘差异表达基因中生物学信息的综合指南。
本综述旨在概述差异表达基因生物学解读的基本概念和方法，以增强背景理解。它还讨论了这些方法当前的局限性和未来前景，强调了它们在破译复杂疾病和表型分子机制方面的广泛应用。为了帮助用户从大量数据集中提取见解，特别是各种差异表达基因列表，我们开发了DEGMiner（https://www.ciblab.net/DEGMiner/），它整合了300多个易于访问的数据库和工具。
本综述为探索差异表达基因提供了有力支持和指导，也将加速在基因组中发现隐藏的生物学见解。
