Computational Molecular Biology 2024, Vol.14, No.5, 220-228 http://bioscipublisher.com/index.php/cmb 220 Review and Progress Open Access Integrative Approaches in Computational Genomics: Combining Omics Data to Study Gene Evolution Hongwei Liu Modern Agricultural Research Center of Cuixi Academy of Biotechology, Zhuji, 311800, Zhejiang, China Corresponding email: hongwei.liu@cuixi.org Computational Molecular Biology, 2024, Vol.14, No.5 doi: 10.5376/cmb.2024.14.0025 Received: 23 Aug., 2024 Accepted: 25 Sep., 2024 Published: 30 Oct., 2024 Copyright © 2024 Liu, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Liu H.W., 2024, Integrative approaches in computational genomics: combining omics data to study gene evolution, Computational Molecular Biology, 14(5): 220-228 (doi: 10.5376/cmb.2024.14.0025) Abstract This study explores integrative approaches in computational genomics that combine multi-omics data to study gene evolution. It provides a detailed analysis of the key components of genomics, transcriptomics, proteomics, and epigenomics, clarifying their roles in gene evolution research. Furthermore, it discusses computational techniques such as network-based methods, machine learning, and gene set analysis, which enhance the integration and interpretation of multi-omics data. With the advancement of high-throughput technologies, multi-omics integration has become a vital approach to understanding the complexity of gene evolution, as it offers a comprehensive perspective on how changes in the genome, transcriptome, proteome, and epigenome drive evolutionary processes. Through case studies in agriculture, medicine, and microbial evolution, this study emphasizes the practical applications of multi-omics integration, reveals the molecular mechanisms behind gene evolution, and provides guidance for future research and applications across various fields. Keywords Multi-omics integration; Gene evolution; Computational genomics; Machine learning; Epigenomics 1 Introduction Gene evolution is a fundamental aspect of biology that provides insights into the mechanisms driving diversity and adaptation in living organisms. Understanding gene evolution helps elucidate the origins of genetic variation, the development of new functions, and the evolutionary pressures shaping genomes over time. This knowledge is crucial for various applications, including evolutionary biology, medicine, and biotechnology. By studying gene evolution, researchers can identify conserved genetic elements, understand the basis of genetic diseases, and develop strategies for genetic engineering and synthetic biology (Qin et al., 2016; Agamah et al., 2022). Computational genomics leverages advanced computational techniques to analyze and interpret complex genomic data. With the advent of high-throughput technologies, vast amounts of data from different omics layers—such as genomics, transcriptomics, proteomics, and metabolomics—are generated. Integrating these multi-omics datasets is essential to gain a comprehensive understanding of biological systems and their regulatory mechanisms (Demirel et al., 2021; Wörheide et al., 2021). Multi-omics integration involves combining data from various sources to highlight the interrelationships between biomolecules and their functions, providing a holistic view of cellular processes (Subramanian et al., 2020). This study will explore integrative approaches in computational genomics that combine multi-omics data to study gene evolution. It aims to identify and evaluate current methods and tools used for multi-omics data integration, including network-based approaches, machine learning techniques, and gene set analysis methods, to promote comprehensive analysis of multi-omics data. The study will discuss the application of these integrative methods in understanding gene evolution, with a focus on how they can be used to investigate gene regulation, identify evolutionarily conserved elements, and uncover the molecular mechanisms driving gene evolution. Through the application of multi-omics data integration techniques, this study aims to provide a more comprehensive and deeper understanding of gene evolution, while offering guidance for future research and practical applications.
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