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Integrating Molecular Perspectives: Strategies for Comprehensive Multi-Omics Integrative Data Analysis and Machine Learning Applications in Transcriptomics, Proteomics, and Metabolomics

Pedro H. Godoy Sanches, Nicolly Clemente de Melo, Andreia M. Porcari and 1 more

Biology · 2024 · Review · CC BY

doi:10.3390/biology13110848

Abstract

Simple Summary. Recent high-throughput technologies such as transcriptomics, proteomics, and metabolomics have allowed progress in understanding biological systems at different levels of detail. Even so, it is necessary to integrate multiple omics data sets to achieve a comprehensive understanding of the subject under study. In this article, we review the methods used for integrating transcriptomics, proteomics, and metabolomics data and summarize them in three approaches: combined omics integration, correlation-based integration strategies, and machine learning integrative approaches. Our goal is to showcase the uses and limitations of each approach, allowing researchers to choose the more appropriate tool for each scenario to extract a comprehensive view of a biological system.

Abstract. With the advent of high-throughput technologies, the field of omics has made significant strides in characterizing biological systems at various levels of complexity. Transcriptomics, proteomics, and metabolomics are the three most widely used omics technologies, each providing unique insights into different layers of a biological system. However, analyzing each omics data set separately may not provide a comprehensive understanding of the subject under study. Therefore, integrating multi-omics data has become increasingly important in bioinformatics research. In this article, we review strategies for integrating transcriptomics, proteomics, and metabolomics data, including co-expression analysis, metabolite–gene networks, constraint-based models, pathway enrichment analysis, and interactome analysis. We discuss combined omics integration approaches, correlation-based strategies, and machine learning techniques that utilize one or more types of omics data. By presenting these methods, we aim to provide researchers with a better understanding of how to integrate omics data to gain a more comprehensive view of a biological system, facilitating the identification of complex patterns and interactions that might be missed by single-omics analyses.

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