International Journal of Aquaculture, 2024, Vol.14, No.2, 62-72 http://www.aquapublisher.com/index.php/ija 63 2 Overview of Algal Genomics Algal genomics is a rapidly advancing field that provides critical insights into the genetic mechanisms underlying the adaptation, evolution, and functional capabilities of algae. With the increasing biotechnological, environmental, and nutraceutical importance of algae, understanding their genomic structure, the techniques used in their study, and key genomic findings is essential. 2.1 Algal genome structure The structure of algal genomes varies significantly across different species, reflecting their diverse evolutionary histories and ecological niches. For instance, the genome of the polar eukaryotic microalga Coccomyxa subellipsoidea exhibits significant synteny conservation with its relatives, yet shows extensive intra-chromosomal rearrangements, which may contribute to its adaptation to cold environments (Blanc et al., 2012). Similarly, the genomic analysis of Picochlorum species reveals substantial heterozygosity and allelic diversity, which are crucial for their adaptation to variable environments such as salt plains and brackish waters (Foflonker et al., 2018). These structural variations highlight the complex genomic architecture that supports the ecological versatility of algae. 2.2 Techniques in algal genomics Advancements in sequencing technologies and bioinformatics have revolutionized the study of algal genomics. Whole-genome sequencing, transcriptomics, and comparative genomics are among the key techniques employed. For example, RNA-Seq analysis has been used to assess gene expression patterns in Alexandrium minutumunder nutrient-deficient conditions, providing insights into its physiological adaptations and stress responses (Meng et al., 2019). Additionally, the use of population genomics approaches, such as genome-wide association studies (GWAS) and selection scans, has enabled the identification of loci associated with adaptation and speciation in various algal species (Bamba et al., 2018). These techniques facilitate a comprehensive understanding of the genetic basis of algal adaptation and evolution. 2.3 Key genomic findings in algal research Several key findings have emerged from genomic studies of algae, shedding light on their adaptive strategies and evolutionary processes. The genomic analysis of Picochlorum species has revealed that gene gain, loss, and horizontal gene transfer (HGT) play significant roles in their adaptation to salinity stress. In Coccomyxa subellipsoidea, the presence of unique gene clusters and the loss of certain proteins suggest specific adaptations to low temperatures. Furthermore, the study of Galdieria sulphuraria under continuous cold stress has identified numerous genetic variants and candidate genes involved in thermal adaptation, highlighting the complexity of the adaptive response at the genetic level (Rossoni and Weber, 2019). These findings underscore the dynamic nature of algal genomes and their capacity for rapid adaptation to changing environmental conditions. 3 Transcriptomics in Algal Research 3.1 Principles of transcriptomics Transcriptomics is the study of the complete set of RNA transcripts produced by the genome under specific circumstances or in a specific cell. This field provides insights into gene expression patterns and regulatory mechanisms, which are crucial for understanding the functional elements of the genome and the molecular constituents of cells and tissues. In algal research, transcriptomics helps elucidate how algae respond to environmental changes, stress conditions, and nutrient availability, thereby revealing the underlying genetic mechanisms of adaptation and survival. 3.2 Techniques in transcriptome analysis Several advanced techniques are employed in transcriptome analysis, including RNA sequencing (RNA-Seq), quantitative real-time PCR (qRT-PCR), and microarray analysis. RNA-Seq is a powerful and widely used method that allows for the comprehensive analysis of the transcriptome, providing both qualitative and quantitative data on RNA expression levels (Morse et al., 2018). This technique involves the conversion of RNA into complementary DNA (cDNA), which is then sequenced using high-throughput sequencing technologies. qRT-PCR is often used to validate RNA-Seq results by quantifying the expression levels of specific genes.
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