Subramanian Indhupriya, Verma Srikant, Kumar Shiva, Jere Abhay, Anamika Krishanpal
LABS, Persistent Systems, Pune, India.
Innovation Cell, Ministry of Human Resource Development, New Delhi, India.
Bioinform Biol Insights. 2020 Jan 31;14:1177932219899051. doi: 10.1177/1177932219899051. eCollection 2020.
To study complex biological processes holistically, it is imperative to take an integrative approach that combines multi-omics data to highlight the interrelationships of the involved biomolecules and their functions. With the advent of high-throughput techniques and availability of multi-omics data generated from a large set of samples, several promising tools and methods have been developed for data integration and interpretation. In this review, we collected the tools and methods that adopt integrative approach to analyze multiple omics data and summarized their ability to address applications such as disease subtyping, biomarker prediction, and deriving insights into the data. We provide the methodology, use-cases, and limitations of these tools; brief account of multi-omics data repositories and visualization portals; and challenges associated with multi-omics data integration.
要全面研究复杂的生物过程，必须采用一种综合方法，将多组学数据结合起来，以突出所涉及生物分子之间的相互关系及其功能。随着高通量技术的出现以及从大量样本中生成的多组学数据的可用性，已经开发了几种有前景的工具和方法用于数据整合和解释。在本综述中，我们收集了采用综合方法分析多个组学数据的工具和方法，并总结了它们在疾病亚型分类、生物标志物预测以及深入了解数据等应用方面的能力。我们提供了这些工具的方法、用例和局限性；对多组学数据存储库和可视化门户的简要介绍；以及与多组学数据整合相关的挑战。
