高通量生物数据的定量分析

IF 16.8 2区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2023-02-01 DOI:10.1002/wcms.1658
Hsueh-Fen Juan, Hsuan-Cheng Huang
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引用次数: 1

摘要

对基因组、转录组、蛋白质组和代谢组等多个“组”的研究已在生物医学研究中得到广泛应用。高通量技术能够快速生成高维多组学数据。与传统方法相比,这种多组学方法为研究生物系统提供了更全面的视角。然而,不同类型的高维组学数据的定量分析和整合仍然是一个挑战。在这里,我们提供了用于组学数据量化和整合的最新和全面的回顾方法。我们首先回顾了大量的定量分析,以及单细胞转录组学数据和蛋白质组学数据。然后介绍了当前减少批效应和集成异构高维数据的方法。大规模生物医学数据的网络分析可以捕获药物、靶点和疾病关系的全局特性,从而能够更好地理解生物系统。本文还讨论了将定量组学数据分析扩展到生物网络的应用和方法的当前趋势。本文分类如下:
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Quantitative analysis of high-throughput biological data

The study of multiple “omes,” such as the genome, transcriptome, proteome, and metabolome has become widespread in biomedical research. High-throughput techniques enable the rapid generation of high-dimensional multiomics data. This multiomics approach provides a more complete perspective to study biological systems compared with traditional methods. However, the quantitative analysis and integration of distinct types of high-dimensional omics data remain a challenge. Here, we provide an up-to-date and comprehensive review of the methods used for omics data quantification and integration. We first review the quantitative analysis of not only bulk but also single-cell transcriptomics data, as well as proteomics data. Current methods for reducing batch effects and integrating heterogeneous high-dimensional data are then introduced. Network analysis on large-scale biomedical data can capture the global properties of drugs, targets, and disease relationships, thus enabling a better understanding of biological systems. Current trends in the applications and methods used to extend quantitative omics data analysis to biological networks are also discussed.

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来源期刊
Wiley Interdisciplinary Reviews: Computational Molecular Science
Wiley Interdisciplinary Reviews: Computational Molecular Science CHEMISTRY, MULTIDISCIPLINARY-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
28.90
自引率
1.80%
发文量
52
审稿时长
6-12 weeks
期刊介绍: Computational molecular sciences harness the power of rigorous chemical and physical theories, employing computer-based modeling, specialized hardware, software development, algorithm design, and database management to explore and illuminate every facet of molecular sciences. These interdisciplinary approaches form a bridge between chemistry, biology, and materials sciences, establishing connections with adjacent application-driven fields in both chemistry and biology. WIREs Computational Molecular Science stands as a platform to comprehensively review and spotlight research from these dynamic and interconnected fields.
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