Computational Molecular Biology 2024, Vol.14, No.2, 64-75 http://bioscipublisher.com/index.php/cmb 71 advantages in terms of data storage, processing power, and accessibility, making them ideal for multi-omics data integration and analysis (Koppad et al., 2021). By leveraging cloud computing, researchers can perform complex computational tasks without the need for extensive local infrastructure, reducing costs and accelerating the pace of discovery (Koppad et al., 2021). Cloud-based bioinformatics applications have been developed to handle various aspects of multi-omics data analysis, including RNA sequencing, metabolomics, and proteomics. These applications facilitate the integration and interpretation of phenotypic data, providing a holistic view of biological systems (Koppad et al., 2021). Additionally, cloud computing enables the sharing and collaboration of large datasets across the global research community, fostering a more collaborative and open scientific environment (Koppad et al., 2021). Big data analytics, combined with cloud computing, allows for the efficient processing and analysis of high-dimensional multi-omics data. Advanced analytical techniques, such as machine learning and deep learning, can be deployed on cloud platforms to uncover hidden patterns and relationships within the data (Kang et al., 2021). This integration of cloud computing and big data analytics is essential for overcoming the challenges associated with multi-omics data, paving the way for new discoveries in systems biology and precision medicine (Koppad et al., 2021; Kang et al., 2021). 7 Case Studies of Successful Multi-Omics Integration 7.1 Cancer research and treatment The integration of multi-omics data has significantly advanced cancer research and treatment, providing a comprehensive understanding of the molecular underpinnings of various cancers. One notable example is the use of multi-omics approaches to achieve precision medicine in oncology. By integrating genomics, transcriptomics, proteomics, and metabolomics data, researchers have been able to classify tumors not just by their site of origin but by their molecular characteristics, leading to the concept of pan-cancer molecular classification (Table 1). This has opened new therapeutic opportunities and allowed for the identification of prognostic and treatment-specific biomarkers, which are crucial for personalized therapy (Nicora et al., 2020; Raufaste-Cazavieille et al., 2022). For instance, the integration of multi-omics data has been pivotal in understanding the spatial and temporal heterogeneity of tumors. This approach has revealed the incredible complexity and molecular diversity within the same tumor type, which traditional single-omics studies could not capture. By combining different layers of biological data, researchers can now dissect the tumor immune environment and host-tumor interactions, providing insights that guide therapeutic decisions in immuno-oncology (Ning and He, 2021; Raufaste-Cazavieille et al., 2022). 7.2 Metabolic disease studies Multi-omics integration has also been instrumental in studying metabolic diseases. The holistic approach of combining genomics, transcriptomics, proteomics, and metabolomics data allows for a comprehensive understanding of the metabolic pathways and their regulation. This is particularly important in diseases like diabetes and obesity, where multiple biological processes are dysregulated. For example, a metabolomics-centric review highlighted the potential of integrating metabolomics data with other omics layers to uncover the complex molecular relationships within metabolic diseases. This approach has enabled researchers to identify novel biomarkers and therapeutic targets, which are essential for developing effective treatments (Wörheide et al., 2021). The integration of multi-omics data has also facilitated the study of metabolic fluxes and the interactions between different metabolic pathways, providing deeper insights into disease mechanisms. Furthermore, the development of computational tools and methods for multi-omics data integration has advanced the field of metabolic disease research. These tools help in addressing the challenges of data dimensionality and heterogeneity, enabling researchers to derive meaningful insights from complex datasets (Subramanian et al., 2020).
RkJQdWJsaXNoZXIy MjQ4ODYzNA==