Computational Molecular Biology 2024, Vol.14, No.5, 220-228 http://bioscipublisher.com/index.php/cmb 225 multi-omics data integration have been applied to study coral adaptation, providing insights into the molecular drivers of physiological and pathological mechanisms in these organisms (Subramanian et al., 2020). The use of cloud computing and containerization has further facilitated the scaling and linking of functional services designed for various purposes, making it easier to perform integrative analyses of coral adaptation evolution (Augustyn et al, 2021). Figure 2 An overview of the multi-omics integration approach and the methods for network-based integration. (A) Processed omics data and prior knowledge for integrative analysis. (B) An integrative multi-omics approach that could be implemented. (C) Integrative network-based methods (D) Multi-layered network showing intra-layer interaction (solid lines) and crosstalk (dashed lines) across different layers (L1, L2, L3) (Adopted from Agamah et al., 2022) 7.3 Gene evolution study on stress-resistance traits in soybean The study of stress-resistance traits in soybean has benefited from the integrative analysis of multi-omics data. By employing a powerful framework that manages and analyzes heterogeneous omics data, researchers have been able to carry out comprehensive integrative analyses. For instance, the mixOmics package for R software has been used to integrate phenomics, metabolomics, cell wall proteomics, and transcriptomics data to study the cell wall plasticity of plants exposed to sub-optimal temperature growth conditions (Duruflé et al., 2020). This approach has enabled the identification of novel conclusions on the biological system, which would not have been possible using standard statistical methods. The methodologies presented in these studies can be applied to various organisms and biological questions, making them highly versatile for studying gene evolution in stress-resistant traits.
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