## 2 Review Methodology
### Short Overview of Omic Technologies  
#### 2.3.1 Principles of Omics Data  
Metabolomics and Transcriptomics  
Usually, metabolomics includes six stage keys: (1) experimental plan, (2) sample preparation, (3) investigation and data achievement, (4) raw data treatment, (5) statistical examination, and (6) interpretation [16]. Metabolomics utilized a diversity of analytical tools comprising MS (LC-MS and GC-MS), NMR, and vibrational-based approaches via Raman and IR spectroscopy. To comprehend the nutrient status in fish quality, NMR can be employed by computing the molecular relaxation time, accompanied by differentiating the food quality. GC-MS was applied to assess volatile molecules. Furthermore, amino acids, fatty acids, and other non-volatile compounds can be perceived via LC-MS. Compared to NMR, MS-based platforms are very perceptive, inexpensive, and deliver wide-ranging metabolites. Nonetheless, MS-based analyses are fewer reproducible, frequently need extra trial preparations, and are weaker in the acquisition of data when employing preparation plans. In contrast to MS-based boards, an inclusive GC \(\times\) GC-MS uses two consecutive pre-separation columns to enhance metabolite separation. This can decrease co-eluting metabolites in complicated biological trials, augment the sensibility, and considerably enlarge the range of perceptible metabolites [17]. DART-HR-MS (direct analysis in real-time high-resolution MS) is perfect for product authentication; its results are reproducible and sensitive, and it necessitates a minimal sample preparation [18]. The FT-ICR-MS (Fourier-transform ion cyclotron resonance MS) is a quantitative technique and provides an extensive range of metabolites with high resolution and sensitivity [19]. In addition, mutually coupled MS platforms, comprised of LC-MS, GC-MS, and CE-MS, could be employed as stand-singly or in tandem to perceive a broader range of metabolites [20]. LC-MS has been employed to isolate and inspect heat-unstable and nonvolatile metabolites and is more perceptive than GC-MS when utilized in tandem MS [21]. However, the main drawback of LC-MS is its limited spectral replicability, which complicates the identification of metabolites using present spectral libraries [22]. Some salmonid investigations were conducted using LC-MS, the next most frequently used analytical method. Especially, this tool was prevalently practical in nutritional and ecotoxicological studies [23]. GC-MS, the 3 rd most utilized analytical method in fish metabolomics, was able to separate volatile and heat-stable metabolites. GC-MS is replicable, perceptive, and attains outstanding metabolite parting. Nevertheless, GC-MS is inappropriate for heat-unstable or metabolites at elevated MW. In addition, some sample extracts intended for GC-MS analysis may necessitate derivatization [24]. [PERSON] et al. [25] employed GC-MS to validate  
Figure 2: Publication subject linked to omic technology or aquatic food products (data obtained from the Lens website [www.lens.org]; accessed on 8 November 2024). the metabolic impacts of severe treatment and anesthesia in salmon. Similar to LC-MS and GC-MS, the CE-MS sensitivity does not involve sample pre-treatment, can utilize minor trials, and can distinguish some metabolites that are not proper for other MS plans. Conversely, for nonpolar compounds, CE hurts from reduced reproducibility and absence of parting [26]. Raman and IR, which are both a type of vibrational spectroscopy, provide data on wider metabolite functional groups and do not tolerate metabolite determination. These tools confuse the understanding of spectra resulting from composite metabolite combinations and afford inferior determination data. Transcriptomics tools are adapted methods to investigate an organism's transcriptome, collecting the sum of its RNA transcripts. The mRNA acts as a passing intermediate molecule in the information system, while noncoding RNAs perform a wide range of functions [27; 28].