Multi-omics integration in biomedical research - A metabolomics-centric review.
Multi-omics integration in biomedical research - A metabolomics-centric review.
复制标题
生物医学研究中的多组学整合——以代谢组学为核心的综述
DOI：
10.1016/j.aca.2020.10.038
复制
发表时间：
2021-01-02
影响因子：
6.2
通讯作者：
Arnold M
中科院分区：
文献类型：
--
作者：
Wörheide MA;Krumsiek J;Kastenmüller G;Arnold M
高通量技术的最新进展使得能够分析生物系统的多个层，包括DNA序列数据（基因组学）、RNA表达水平（转录组学）和代谢物水平（代谢组学）。这导致了大量生物数据的产生，这些数据可以整合到所谓的多组学研究中，以研究健康和疾病的复杂分子基础。这些数据集的综合分析并不简单，而且由于数据的高维性和异质性以及缺乏通用的分析协议而特别复杂。以前的评论讨论了各种策略，以解决数据集成的挑战，详细说明了具体方面，如网络推理或功能选择技术。因此，主要关注的是两个组学层与感兴趣的表型的关系的整合。在本文中，我们概述了典型的多组学工作流程，重点关注有可能将代谢组学数据与两个或更多组学联合收割机组合的集成方法。我们讨论了多种集成概念，包括数据驱动，基于知识，同步和逐步的方法。我们强调这些方法在最近的多组学研究中的应用，包括大规模的整合工作，旨在全球描绘不同生物层内和之间的复杂关系，而不关注特定的表型。
Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.
登录
查看更多内容
影响因子：
4
作者：
Batra R;Alcaraz N;Gitzhofer K;Pauling J;Ditzel HJ;Hellmuth M;Baumbach J;List M
通讯作者：
List M
影响因子：
--
作者：
Alcaraz N;Pauling J;Batra R;Barbosa E;Junge A;Christensen AG;Azevedo V;Ditzel HJ;Baumbach J
通讯作者：
Baumbach J
影响因子：
14.8
作者：
Becker, Scott A.;Feist, Adam M.;Herrgard, Markus J.
通讯作者：
Herrgard, Markus J.
影响因子：
4.5
作者：
Bartel J;Krumsiek J;Schramm K;Adamski J;Gieger C;Herder C;Carstensen M;Peters A;Rathmann W;Roden M;Strauch K;Suhre K;Kastenmüller G;Prokisch H;Theis FJ
通讯作者：
Theis FJ
DOI：
10.1002/wics.1246
发表时间：
2013-03-01
影响因子：
3.2
作者：
Abdi, Herve;Williams, Lynne J.;Valentin, Domininique
通讯作者：
Valentin, Domininique