## 3 Marine-Derived Carotenoid Screening
### In Silico Carotenoid Screening  
The secondary metabolite Biosynthetic Gene Clusters (smBGCs) are a group of genes that regulate a certain metabolic network to produce secondary metabolites. These genes perform a variety of tasks, including controlling expression, aiding biosynthesis of precursor molecules, assembling and modifying translation and post-translational products, and regulating product resistance. Abiotic stress and biotic variables found in the environment where the bacteria originate affect the control of the smBGCs network [64]. The disparity in environmental conditions between the laboratory and the natural ecosystem creates a barrier to the laboratory production of secondary metabolites. Under laboratory circumstances, some of these microbes' genes are not expressed or are \"hidden\" or \"cryptic\". The physical, chemical, and biological conditions for expression of the gene were not met. It is difficult to determine the existence of this gene using only laboratory detection methods. As a result, a strategy is required that considers the target microorganism's metabolic capacity as a whole. Genome mining is one of the most frequently used techniques for resolving this issue. It is a method for identifying previously unidentified biosynthetic gene clusters. In general, genome mining can be divided into in silico and in vitro stages. Biosynthetic Gene Cluster (BGC) identification and characterization are among the in silico processes. This stage entails performing a variety of bioinformatics analyses on the genome sequences of the target microorganisms. Widely available databases and tools offer various utilities that can aid in silico screening for certain compounds. In general, the workflow of in silico natural product screening using various publicly available tools and databases are visualized in Figure 5, whereas the in vitro stage involves the identification and characterization of the BGCs product using wet laboratory protocols. When gene sequences are expected to encode specific genes, they will be expressed in certain vectors and characterized [64]. Rule-based approaches and machine learning-based methods are the two types of in silico secondary metabolite screening methods. The rule-based method utilizes a sequence alignment-based profile in the Hidden Markov Model (HMM) of smBSCs that belongs to a certain biosynthetic pathway and is available in the database [64]. Apparently, available tools and databases commonly encompass various natural products or secondary metabolites (Table 1). There are no specific tools and databases for carotenoid available currently. Thus, screening for a specific set of natural products such as carotenoids can potentially be conducted by changing search parameters in respective databases or tools. Furthermore, various tools are needed for constructing HMM profiles and for applying other bioinformatics methods. Rapid and easy secondary metabolite screening can be performed using publicly available tools (Table 2) with genome files as the query. PRISM [65] and antiSMASH [66] are two tools that are often used to look for smBSCs using a rule-based strategy. By utilizing these rule-based methods, it is possible to efficiently search databases for hidden smBSCs that are similar. However, the disadvantage of this strategy is that it only finds smBSCs that are identical to those in databases, making it difficult to predict novel smBSCs that are not identical to those in databases.