The term “Omics” broadly refers to a set of genomics technologies that are utilized in cellular and molecular biology to investigate molecular (DNA, RNA), protein and epigenetics signatures. Omics are utilized to study both host and pathogens, and across many disciplines such as immunology, oncology and cell biology. With the high‐throughput technologies and the development of new bioinformatics tools, these approaches have significantly contributed to our understanding of how molecular mechanisms determine the structure, function and dynamics of organisms. With the large and growing volume of data generated with these approaches, bioinformatics has become an integral part of research in biology and notably immunology and microbiology. Large omics data sets are now also important components in clinical and translational research. For instance, databases of gene expression, somatic mutations and protein networks can be used to predict disease outcome or to develop new molecular diagnostic tools.

In this Special Feature, the four reviews describe some of the most prominent applications of omics and bioinformatics in immunology research and clinical translational applications. These articles exemplify the vast research work that has occurred in the last two decades on the application of omics in medical and basic science research, with the objective to provide the reader with an introductory view of the current state of the art.

T‐cell receptors are formed from a complex set of genes undergoing homologous recombination, and it has been the result of very long evolutionary processes. T‐cell receptor drives one of the most sophisticated weapons that the immune system has against pathogens. Understanding the biology of T‐cell receptors is an ongoing subject of investigation since their discovery and sequence in the early 1980s.¹ The review from Watkins and Miles ² brings the reader up to speed with sequencing technologies and bioinformatics that have enabled diagnostic applications and predictive analyses in basic and clinical immunology.

Autoimmunity: High‐throughput sequencing of genomics has significantly impacted research in autoimmune disorders, with the possibility to screen large sample size and identify genetic variants across the full genome. The review from Field ³ reports the bioinformatics challenges that arose with the high‐throughput detection of pathogenic variants and discusses the future applications toward personalized medicine.

Single‐cell multi‐omics: In the last decade, single‐cell genomics have permitted unprecedented detection of biological complexity and heterogeneity, notably in immunology.⁴ Advances in microfluidics and other technologies have allowed investigation of individual cells, thus removing the unwanted noise arising from the analysis of heterogenous cell populations. More recently, new technologies and bioinformatics analyses have enabled the simultaneous investigation of multiple modalities within the same cells utilizing more than one omic technology. The review from Louie and Luciani ⁵ discusses the recent advances in multi‐omics to study immune cells, and also how the near future may be shaped by these technologies.

Omics technology in the clinic: In the clinical setting, the application of omics technology has shown considerable promise. From patient stratification and therapeutic selection to the realization of personalized medicine, these technologies are beginning to fundamentally alter the way we approach molecular studies in translational research. The review from Giles et al.⁶ review current applications of omics technologies in immunology and oncology, along with ethical and practical challenges currently constraining adoption.

Omics technologies and bioinformatics are increasingly becoming essential components in biology and specifically immunology. The development of high‐throughput technologies has permitted the generation of numerous data sets across human and pathogens, introducing the need for large, centralized repositories, such as the Human Cell Atlas for single‐cell genomics.⁷ Bioinformatics analysis and integration of very large omics data are providing the foundation to address unanswered questions including heterogeneity of biological processes and cell types, and how these can be translated into new treatment and interventions. These reviews represent key examples of the questions that are currently being addressed and a view of the future directions that omics and bioinformatics will shape the next decade of research.

术语“组学”泛指一组基因组学技术，用于细胞和分子生物学研究分子（DNA、RNA）、蛋白质和表观遗传学特征。组学被用于研究宿主和病原体，并跨越许多学科，如免疫学、肿瘤学和细胞生物学。随着高通量技术和新生物信息学工具的发展，这些方法极大地促进了我们对分子机制如何决定生物体的结构、功能和动力学的理解。随着这些方法产生的数据量越来越大，生物信息学已经成为生物学研究的一个组成部分，尤其是免疫学和微生物学。大型组学数据集现在也是临床和转化研究的重要组成部分。例如，

在本专题中，四篇评论描述了组学和生物信息学在免疫学研究和临床转化应用中的一些最突出的应用。这些文章举例说明了过去二十年中关于组学在医学和基础科学研究中应用的大量研究工作，目的是为读者提供对当前技术状态的介绍性观点。

T 细胞受体是由一组复杂的基因经过同源重组形成的，它是非常长的进化过程的结果。T 细胞受体驱动着免疫系统对抗病原体的最复杂的武器之一。自从 1980 年代早期发现和测序 T 细胞受体以来，了解 T 细胞受体的生物学一直是一个持续的研究课题。¹ Watkins 和 Miles 的评论²使读者快速了解测序技术和生物信息学，这些技术和生物信息学使基础和临床免疫学的诊断应用和预测分析成为可能。

自身免疫：基因组学的高通量测序显着影响了自身免疫性疾病的研究，有可能筛选大样本量并识别整个基因组中的遗传变异。Field ³的综述报告了高通量检测致病变异带来的生物信息学挑战，并讨论了个性化医疗的未来应用。

单细胞多组学：在过去十年中，单细胞基因组学允许前所未有地检测生物复杂性和异质性，尤其是在免疫学方面。⁴微流体和其他技术的进步已经允许研究单个细胞，从而消除分析异质细胞群时产生的不需要的噪音。最近，新技术和生物信息学分析使得能够利用一种以上的组学技术同时研究同一细胞内的多种模式。Louie 和 Luciani 的综述⁵讨论了多组学研究免疫细胞的最新进展，以及这些技术如何塑造不久的将来。

组学技术在临床上：在临床上，组学技术的应用已经显示出相当大的希望。从患者分层和治疗选择到个性化医疗的实现，这些技术开始从根本上改变我们在转化研究中进行分子研究的方式。Giles等人的评论。⁶回顾了组学技术在免疫学和肿瘤学中的当前应用，以及目前限制采用的伦理和实践挑战。

组学技术和生物信息学正日益成为生物学特别是免疫学的重要组成部分。高通量技术的发展允许在人类和病原体之间生成大量数据集，从而需要大型集中存储库，例如用于单细胞基因组学的人类细胞图谱。⁷非常大的组学数据的生物信息学分析和整合为解决悬而未决的问题奠定了基础，包括生物过程和细胞类型的异质性，以及如何将这些转化为新的治疗和干预措施。这些评论代表了当前正在解决的问题的关键示例，以及对组学和生物信息学将塑造下一个十年研究的未来方向的看法。