With the widespread of high-throughput technologies, multi-omics integrative analysis has rapidly developed. Researchers can obtain large-scale omics data from different molecular levels such as genomics, transcriptomics, proteomics, interactomics, epigenomics, metabolomics, lipidomics, and microbiomics, which has revolutionized biology and promoted our profound understanding of biological processes and molecular mechanisms. With the use of high-throughput omics methods in the analysis of biological samples, trillions to petabytes of data files are generated every day. Improvement in comprehensive research is inevitable. Multi-omics analysis is not just the integration of data, but also an in-depth study of biological explanations, providing new insights for basic biology and disease research.
A single omics analysis method can provide information about the biological processes that differ between a certain life process or disease group and a normal group. However, these analyses often have limitations. Multi-omics analysis integrates information from several omics levels, providing more evidence for biological mechanisms and a deeper exploration of candidate key factors. By integrating information between different levels, such as genes, regulatory factors, proteins, and metabolites, the construction of gene regulatory networks allows for a deeper understanding of the regulation and causal relationships among various molecules, thereby gaining a deeper understanding of the molecular mechanisms and genetic basis of complex traits in biological and disease processes.
Agriculture and forestry: growth and development research, stress and non-stress mechanisms, crop breeding, rare species protection research, medicinal plant research, etc.
Animal husbandry: growth and development research, mining of functional genes related to important economic traits in livestock and poultry, pathogenic mechanism research, exploration of transcription factors in forage slope and stress conditions, etc.
Marine aquaculture: growth and development research, evolutionary research, toxicology and water product safety, etc.
Biomedicine: biomarkers, disease mechanism, drug targets, disease classification, personalized treatment, etc.
Microbiology: pathogenic mechanisms, drug resistance mechanisms, pathogen-host interactions, etc.
Environmental science: optimization of fermentation processes, biofuel production, environmental hazard risk assessment, etc.
By conducting integrative analysis of transcriptomics and metabolomics on biological samples, the intrinsic changes of organisms can be analyzed at both causal and consequential levels. This allows for a more systematic and comprehensive understanding of the functional roles and regulatory mechanisms of biological molecules, leading to the identification of key metabolic pathways, genes, and metabolites for further in-depth research and application in molecular biology.
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