International Journal of Molecular Medical Science, 2024, Vol.14, No.1, 90-99 http://medscipublisher.com/index.php/ijmms 92 while liquid chromatography separates proteins based on their chemical properties. The separated proteins can then be identified and quantified using mass spectrometry techniques, such as tandem mass spectrometry (MS/MS) and time-of-flight mass spectrometry (TOF-MS). Mass spectrometry analysis measures the mass and amino acid sequence of proteins, thereby determining their identity. Finally, bioinformatics tools and databases are used to analyze and annotate the mass spectrometry data. This includes protein identification, modification site identification, protein interaction analysis, and functional annotation. The applications of proteomics data are extensive. By analyzing proteomics data, researchers can understand the composition and abundance of proteins in cells or tissues, which in turn provides insights into the functions and physiological processes of biological systems. By comparing proteomics data from different samples, proteins with significant expression differences under different physiological or disease states can be identified, leading to the discovery of potential biomarkers or drug targets. Additionally, proteomics data can identify protein-protein interactions and functional associations, revealing protein regulatory networks, signaling pathways, and cellular functions. Proteomics data can also detect protein modifications such as phosphorylation, acetylation, and methylation, which are important for understanding protein regulation and function. 1.4 Acquisition and application of metabolomics data Metabolomics is the study of the composition and changes of metabolites within organisms. The acquisition and application of metabolomics data provide researchers with important tools to understand the composition and changes of metabolites and their relationships with biological processes and diseases. Metabolomics has broad application prospects in fields such as medicine, biological sciences, agricultural sciences, and food sciences. To acquire and apply metabolomics data, samples must be collected from biological specimens under study (e.g., blood, urine, tissues) and undergo preprocessing steps, such as protein removal and salt removal, to reduce the impact of interfering substances. Next, an appropriate analytical platform is selected for metabolite analysis. Common analytical techniques include gas chromatography-mass spectrometry (GC-MS) (Figure 1), liquid chromatography-mass spectrometry (LC-MS), nuclear magnetic resonance (NMR), and mass spectrometry imaging (MSI). Metabolite analysis is then performed on the samples to obtain high-throughput metabolomics data. These data provide information on the types, relative abundance, and changing trends of metabolites. Bioinformatics tools and statistical methods are used to process and analyze metabolomics data, including feature detection, mass spectrometry peak alignment, metabolite identification, quantification, and differential analysis. Figure 1 Gas Chromatography-Mass Spectrometry (GC-MS) Instrument (Adopted from zyzhan.com) Metabolomics data have widespread applications. By analyzing metabolomics data from different samples, metabolites associated with specific physiological states, diseases, or drug responses can be identified. These metabolites can serve as biomarkers (Wu et al., 2022), used for disease diagnosis, prognosis assessment, and
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