Computational Molecular Biology 2024, Vol.14, No.5, 211-219 http://bioscipublisher.com/index.php/cmb 212 The integration of genomics and transcriptomics is fundamental in understanding the flow of genetic information from DNA to RNA. Genomic data provides insights into the genetic blueprint, while transcriptomic data reveals gene expression patterns under different conditions. Combining these datasets helps in identifying regulatory elements and understanding gene function and regulation. For instance, genome-scale models (GEMs) have been employed to interpret and integrate genomics and transcriptomics data, enabling the modeling of metabolic, transcriptional, and translational reactions within an organism (Dahal et al., 2020). High-throughput strategies such as SPOT (Sample Preparation for Multi-Omics Technologies) facilitate the simultaneous analysis of genomic and transcriptomic data, enhancing throughput and resource efficiency (Gutierrez et al., 2018). 2.2 Integration of proteomics and metabolomics Proteomics and metabolomics integration is crucial for linking protein function with metabolic pathways. Proteins act as catalysts and regulators of metabolic reactions, and their expression levels can significantly impact metabolite concentrations. Integrating these datasets provides a holistic view of cellular responses and metabolic states. For example, proteomic profiles can reflect cellular responses to genomic and environmental changes, and integrating these with metabolomic data can reveal novel biological insights and potential clinical applications (Zhang and Kuster, 2019). Moreover, metabolomics-centric approaches have been highlighted for their potential to combine metabolomics data with other omics layers, providing a global depiction of complex biological relationships (Wörheide et al., 2021). Tools like XCMS Online facilitate the integration of metabolomic data with transcriptomic and proteomic data, superimposing raw data onto metabolic pathways for comprehensive analysis (Huan et al., 2017). 2.3 Epigenomics integration Epigenomics integration involves combining data on epigenetic modifications, such as DNA methylation and histone modifications, with other omics layers to understand their impact on gene expression and cellular function. Epigenetic changes can regulate gene activity without altering the DNA sequence, and their integration with genomics, transcriptomics, and proteomics data can elucidate mechanisms of gene regulation and disease progression. For instance, integrating epigenomic data with proteomic and transcriptomic data has been shown to provide insights into the regulatory effects of epigenetic modifications on protein expression and cellular functions. Systems genomics approaches have been employed to integrate epigenomic data with other omics layers, enhancing our understanding of complex traits and diseases in animal production and health (Suravajhala et al., 2016). 3 Computational Tools and Data Analysis for Multi-Omics Integration 3.1 Network construction and regulatory analysis Network construction and regulatory analysis are pivotal in understanding the complex interactions within biological systems. Multi-omics data integration often involves constructing networks that represent relationships between various biological entities, such as genes, proteins, and metabolites. One approach to this is the use of heterogeneous multi-layered networks (HMLNs), which can integrate diverse biological data to represent the hierarchy and interactions within a biological system. HMLNs have been successful in inferring novel biological relations and understanding the environmental impact on organisms (Lee et al., 2020). Tools like KiMONo have been developed to handle missing data in multi-omics studies, allowing for robust network inference even when data is incomplete (Henao et al., 2023). These network-based approaches are essential for identifying key nodes and subnetworks that drive physiological and pathological mechanisms. 3.2 Application of machine learning in multi-omics analysis Machine learning has become an indispensable tool in the analysis of multi-omics data. It offers novel techniques to integrate and analyze various omics data, enabling the discovery of new biomarkers and aiding in disease prediction, patient stratification, and precision medicine (Reel et al., 2021). Different machine learning methods, such as Bayesian models, tree-based methods, kernel methods, and deep neural networks, have been employed to handle the complexity and heterogeneity of multi-omics data. These methods can transform and map omics data into new representations, facilitating downstream analysis and improving the understanding of biological systems.
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