多种“ omics”方法的集成，称为多组学或multi-omics，，，，has become a major focus in biomedical research, as researchers seek ways to understand biological mechanisms in greater depth. While omics fields such as genomics, transcriptomics,蛋白质组学and metabolomics provide information about DNA, RNA transcripts, proteins and metabolites, respectively, multiomic studies allow multiple biomolecule types to be examined together and offer the potential to obtain a more holistic, pathway-oriented view of biology.

在任何研究环境中，都有产生，共享和挖掘重要的生物学信息的潜力是有价值的。然而，在当前大流行的背景下，紧迫性的感觉更高，研究人员正在寻求揭示Covid-19的分子基础的方法。多组学方法正在用于病理生理学，生物标志物，免疫反应，疾病严重程度预测及以后的与COVID-19相关研究中使用。

In practice, multiomic datasets are large, contain information derived using multiple technologies and feature many potentially confounding variables. Therefore, the management and exploration of multiomic datasets is challenging. In this article, we explore multiomic approaches to COVID-19 research and clinical omics and explore some of the computational and statistical tools being developed to support multiomic analyses.

Aims and strategies of COVID-19 multiomics studies

多组学方法如何帮助我们铺平道路 - 19 Covid-19的大流行？正如所解释的Al J Abadi，，，，genomics data scientist at Melbourne Integrative Genomics, University of Melbourne, it’s all about tracing molecular footprints: “Put simply, the SARS-CoV-2 virus, like any other virus, triggers a host immune response and leaves a footprint in patients' proteome as well as the metabolome. This footprint varies among different patients – some of whom also experience a more severe form of the disease. Characterizing the variability in these omics alterations and their interactome is only possible through multiomics approaches and could help gain a better understanding of the disease mechanism and how we can combat it more efficiently.”

威斯康星大学麦迪逊分校库恩实验室的助理参谋科学家Evgenia Shishkova，，，，explains that the aims of multiomics studies are very project-dependent: “Whether your goal is to create a resource, or you have a more targeted approach, the technology lets you do either. But more often than not, multiomic datasets end up being really big. So even if you do have a targeted question in mind, you end up having this massive resource that others could benefit from.”

Not only do multiomics studies vary in their size, underlying goals and types of data used, but also their approaches to data integration. Algorithms used to integrate omics data can be categorized as supervised, semi-supervised, or unsupervised, and the integration can occur at different stages of analysis.¹Within these categories, there are many different analytical subtypes, such as matrix factorization, Bayesian, network-based and multiple-step approaches which use different modeling and statistical techniques to identify data patterns.

Interrogate the SARS-CoV-2 Genome To Get More Answers

研究SARS-COV-2基因组可以为其进化和传播提供宝贵的见解，以及帮助监视和疫苗开发工作，以帮助控制Covid-19-19的大流行。下载此应用说明以发现一种RNA测序方法，该方法可以准确识别低拷贝数病毒病原体，并且足够敏感，可以在低浓度，低质量和降解样品中使用。

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Building a COVID-19 knowledge base using multiomics approaches

Multiomic data serve as a resource to be explored by the wider scientific community and provide critical supporting evidence of the key biological processes modulated in people with COVID-19. Physiological changes associated with COVID-19 could be identified on a molecular level, such as those related to lipid transport, complement system activation, vessel damage, platelet activation and degranulation, blood coagulation and the acute phase response.

首先触发了最早的大规模Covid-19多构分析之一。Ariel Jaitovich，纽约奥尔巴尼医学院的肺部和重症监护医师。Jaitovich亲眼目睹了Covid-19症状如何在严重程度上范围范围，并想象一项协作研究将提供相关的分子见解。专业知识随后从Coon Laboratoryat the University of Wisconsin-Madison and the Morgridge Institute for Research to produce a large-scale multiomic analysis of COVID-19 severity.²

该小组共同对有或没有COVID-19的患者进行了128个血浆样品进行RNA测序和高分辨率质谱（MS），以及与一系列临床数据和患者的相关性白细胞mRNA表达和血浆蛋白，代谢物和脂质水平结果。“我们的最终目标是利用我们的资源和共同的专业知识，尽可能快地将数据传递给人们，而不会痛苦地分析我们的分析。”库恩实验室的研究数据科学家伊恩·米勒（Ian Miller）。“关于Covid的数据像消防员一样出来，仍然如此。”

米勒回想起如何在代谢组学测量中观察到不同的治疗分子，从而提供了他描述的“烘焙理智检查或控制”。219个生物分子与共同19的状态和严重程度高度相关，密集的生物分子簇也是如此。值得注意的是，一个鉴定出的簇含有脂质 - 纤溶酶 - 磷脂酰胆碱和高密度脂蛋白 - 与CoVID-19的状态和严重程度显着相关的类别。

The characterization of COVID-19 pathophysiology in multiomic studies allows for the identification of potential therapeutic opportunities – several of which were postulated in the publication.²例如，作者强调了他汀类药物或其他旨在恢复高密度脂蛋白（HDL）的疗法的潜在使用。在其他研究中，COVID-19患者的循环HDL水平降低，有可能导致加剧CoVID-19病理生理学的压力和炎症。³最终，希望还可以使用多构想数据来支持对患者预后的更准确的预测。

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Investigating cellular hallmarks of severe COVID-19

考虑到重症患者血液凝结问题的高流行率，研究Covid-19中血栓形成并发症的机制是一个优先事项。⁴几项研究指出，血小板免疫细胞相互作用改变和受影响的肺中的巨核细胞的存在。^{5 ，，，， 6}为了进一步研究，Bernardes及其同事采用了纵向多组学分析来表征严重的Covid-19病例的细胞特征。⁷Analyzing the bulk transcriptome, bulk DNA methylome, and single-cell transcriptome of over 358,000 cells from peripheral blood enabled cellular changes to be characterized over time. Elevated levels of circulating megakaryocytes were identified in severe COVID-19 cases and linked to heightened inflammatory parameters, alongside changes in expression revealed by bulk RNA sequencing data. The multiomic approach provides greater context when studying dynamic changes of circulating cells in the blood. For example, genome-wide hypomethylation was observed at each time point compared with healthy controls, supporting earlier suggestions of epigenetic changes contributing to systemic inflammatory states.⁸

Making multiomic COVID-19 data more accessible

As summarized by Abadi, sophisticated tools are needed if multiomics data are to be harnessed: “The sequencing data generated by these assays require new data structures to streamline the downstream analyses. These data are also highly dimensional and gaining direct insight from them is beyond our limited intuition.

量身定制的计算方法和统计方法在揭示数据中隐藏且经常有趣的模式，测试研究人员产生的假设和/或创建可以帮助产生新假设的新见解方面至关重要。”结果，许多小组正在开发旨在帮助弥合数据和理解数据之间的差距的工具。混合学is one such example – a free and open-source multiomics tool kit, and the result of an international, multi-university collaboration. Abadi has had a central role in the implementation and improvement of novel multivariate statistical methods for mixOmics, while also developing powerful visualization tools to present outcomes in a more compelling and intuitive way.

在威斯康星州麦迪逊分校合作者中，对数据可视化工具的需求不会丢失。为了将数据分析从“消防”体验转变为更易于管理的东西，该小组将数据传输到交互式网络工具上 -covid-omics.app–allowing access to the wider scientific community. “We want to share what we have produced – that’s our model. And that’s inherent to the funding mechanism for most of these projects too, a lot of this work is federally funded” saysKatie Overmyer，，，，associate director of the Laboratory for Biomolecular Mass Spectrometry at the Morgridge Institute for Research.

为数据驱动的生物学研究做准备

通过利用机器学习，RNA测序和MS等技术，可以将OMICS数据与临床信息结合使用，以为研究人员提供大量数据以探索。对于Miller来说，数据素养是他建议的生物学家将其添加到其工具包中的重要工具：“您可以掌握的数据素养越多，您可以学习的基本编程越多，它就会允许您理解和分享您的工作。”随着研究人员致力于传播和打击Covid-19感染的高度可变临床过程，与更广泛的受众共享有价值的数据的能力（同时为可视化和分析提供了有用的工具）从未如此关键。

参考

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   文章updated September 6, 2021, to remove the hyphen from "Multi-omics".