Computational Molecular Biology 2024, Vol.14, No.5, 211-219 http://bioscipublisher.com/index.php/cmb 216 Figure 2 Multi-omics integration approaches (Adopted from Son et al., 2020) Image caption: (A) Based on the central dogma, information flows could affect different omics from the upstream level (the genome) to the downstream level (the metabolome). (B) Integrating different biological networks into genome scale metabolic models, and explaining changes in metabolomics through studying integrated networks and multi-omics datasets. (C) Based on a co-regulation analysis of integrated networks, and together with a reporter metabolite analysis, less consumption of mannose in liver tissue was identified among obese subjects (Adopted from Son et al., 2020) 7 Technical Challenges of Multi-Omics Integration 7.1 Data standardization and integration difficulty The integration of multi-omics data is fraught with challenges, primarily due to the vast differences in data types and nomenclature across various omics platforms such as genomics, transcriptomics, proteomics, and metabolomics. Each of these platforms generates large datasets that require extensive data cleaning, normalization, and biomolecule identification before they can be integrated into a cohesive biological context. The lack of standardized analytical pipelines further complicates this process, making it difficult to achieve a holistic systems biology understanding (Pinu et al., 2019). The differences in data dimensionality and the need for biological contextualization and statistical validation add layers of complexity to the integration process (Misra et al., 2019). 7.2 Computational demand and storage bottlenecks The sheer volume of data generated by high-throughput omics technologies poses significant computational and storage challenges. Each omics analysis can produce tera- to peta-byte sized data files, necessitating robust computational frameworks and substantial storage capacities to handle and process these datasets efficiently (Miao et al., 2021). The computational demand is further exacerbated by the need for sophisticated algorithms capable of integrating and analyzing high-dimensional, heterogeneous data. Techniques such as deep learning and network-based methods have shown promise in addressing these challenges, but they also require considerable computational resources (Lee et al., 2020). 7.3 Challenges in integrating heterogeneous data Integrating heterogeneous data from different omics platforms is another major challenge. The complexity, heterogeneity, and high-dimensionality of omics data make it difficult to develop universal integration strategies. Different omics data types often have varying levels of completeness, with missing data being a common issue due to experimental limitations or cost constraints (Flores et al., 2023; Henao et al., 2023). Moreover, the integration of matched (measured on the same cell) and unmatched (measured on different cells) data requires different strategies, each with its own set of computational and biological challenges. The development of methods that can handle these diverse data types and provide biologically interpretable results is crucial for advancing multi-omics integration (Nicora et al., 2020). 8 Future Directions in Multi-Omics Integration 8.1 Real-time multi-omics integration Real-time multi-omics integration represents a significant advancement in systems biology, enabling the dynamic
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