Unlocking biological insights from differentially expressed genes: Concepts, methods, and future perspectives
Huachun Yin, Hongrui Duo, Li Song, Dan Qin, Lingling Xie, Yingxue Xiao, Jing Sun, Jingxin Tao, Xiaoxi Zhang, Yinghong Li, Yue Zou, Qingxia Yang, Xian Yang, Youjin Hao, Bo Li
Abstract
• The first review to systematically introduce and summarize the tools for maximizing biological information of genes. • A comprehensive overview of representative tools and algorithms for analyzing differentially expressed genes. • More than 300 tools, databases, and algorithms are summarized on the website DEGMiner. • A detailed guideline is provided to help researchers better mine the functions and interactions of genes. 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 graphs, 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. of review: 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.