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 b...

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Bibliographic Details
Published in:Wiley interdisciplinary reviews. Computational molecular science Vol. 13; no. 4; pp. e1658 - n/a
Main Authors: Juan, Hsueh‐Fen, Huang, Hsuan‐Cheng
Format: Journal Article
Language:English
Published: Hoboken, USA Wiley Periodicals, Inc 01.07.2023
Wiley Subscription Services, Inc
Subjects:
Analysis
Artificial intelligence
Biological data
biological network
Biomedical data
Data analysis
data integration
Data science
Genomes
Machine learning
Medical research
multiomics data
Network analysis
Proteomes
Proteomics
Quantitative analysis
single‐cell transcriptomics
Transcriptomes
Transcriptomics
ISSN:1759-0876, 1759-0884
Online Access:Get full text
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Summary: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. This article is categorized under: Data Science > Artificial Intelligence/Machine Learning This review provides an up‐to‐date and comprehensive overview of the methods for omics data quantification and integration, as well as their applications.
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ISSN:1759-0876
1759-0884
DOI:10.1002/wcms.1658