Deshpande Dhrithi, Chhugani Karishma, Chang Yutong, Karlsberg Aaron, Loeffler Caitlin, Zhang Jinyang, Muszyńska Agata, Munteanu Viorel, Yang Harry, Rotman Jeremy, Tao Laura, Balliu Brunilda, Tseng Elizabeth, Eskin Eleazar, Zhao Fangqing, Mohammadi Pejman, P Łabaj Paweł, Mangul Serghei
Department of Pharmacology and Pharmaceutical Sciences, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, Los Angeles, CA, United States.
Department of Clinical Pharmacy, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, Los Angeles, CA, United States.
Front Genet. 2023 Mar 13;14:997383. doi: 10.3389/fgene.2023.997383. eCollection 2023.
RNA sequencing (RNA-seq) has become an exemplary technology in modern biology and clinical science. Its immense popularity is due in large part to the continuous efforts of the bioinformatics community to develop accurate and scalable computational tools to analyze the enormous amounts of transcriptomic data that it produces. RNA-seq analysis enables genes and their corresponding to be probed for a variety of purposes, such as detecting novel exons or whole transcripts, assessing expression of genes and alternative transcripts, and studying alternative splicing structure. It can be a challenge, however, to obtain meaningful biological signals from raw RNA-seq data because of the enormous scale of the data as well as the inherent limitations of different sequencing technologies, such as or . The need to overcome these technical challenges has pushed the rapid development of novel computational tools, which have evolved and diversified in accordance with technological advancements, leading to the current myriad of RNA-seq tools. These tools, combined with the diverse computational skill sets of biomedical researchers, help to unlock the full potential of RNA-seq. The purpose of this review is to explain basic concepts in the computational analysis of RNA-seq data and define discipline-specific jargon.
RNA测序（RNA-seq）已成为现代生物学和临床科学中的一项典范技术。它广受欢迎在很大程度上归功于生物信息学领域持续不断的努力，即开发准确且可扩展的计算工具，以分析它所产生的海量转录组数据。RNA-seq分析能够出于多种目的对基因及其对应物进行探究，例如检测新的外显子或完整转录本、评估基因和可变转录本的表达，以及研究可变剪接结构。然而，由于数据规模巨大以及不同测序技术（如 或 ）的固有局限性，从原始RNA-seq数据中获取有意义的生物学信号可能具有挑战性。克服这些技术挑战的需求推动了新型计算工具的快速发展，这些工具随着技术进步不断演变和多样化，导致了当前众多的RNA-seq工具。这些工具与生物医学研究人员多样的计算技能相结合，有助于释放RNA-seq的全部潜力。本综述的目的是解释RNA-seq数据计算分析中的基本概念并定义特定学科的术语。
