Microalgae

The advent of the -omics era, and the development of the related technologies for the acquisition and analysis of the number of big datasets available; has revolutionized biological research. This holistic approach promises to discover and understand new patterns and has diverse and important applications in the field of biotechnology. In the last decade, growing public and private interest and investment in the marine biotechnology (or blue biotechnology); have increased the opportunity to generate information; to collect huge amounts of data to understand different cellular processes and biological phenomena.

Blue biotechnology makes use as well of multi-omics methodologies (such as genomics, transcriptomics, proteomics, metabolomics, metagenomics, and metatranscriptomics); for the production and analysis of massive biological data. One of the most promising outcomes of blue biotechnology research is the discovery of marine natural products (MNPs); defined as bioactive compounds derived from marine organisms.

Derived from marine microalgae

During recent years, -omics resources provided novel opportunities to identify and characterize high-value; bioactive compounds derived from marine microalgae. However, a user that ventures into the discovery of MNPs from marine microalgae face fragmentation of data sources, incomplete reference datasets, and a lack of dedicated software. Despite the decreasing costs of the -omics technologies; the datasets available are still too few for comparative tools to work effectively; more investments into the production of data from microalgal species are desirable.

Moreover, the vastness of large-scale -omics datasets requires the integration of these data under bioinformatics models/tools. These tools are already available in the field of human diseases and plants. To date, the solely integrated approach applied in algae is reported by Maes and colleagues.

Ecological and biotechnological

The authors published an integrated pipeline, called MinOmics (Methods for Integrated analysis of Multiple Omics datasets) to manage various biological data, such as genes, transcripts, and proteins; deriving from the freshwater microalga C. reinhardtii. Improvements in this field can made by extending this tool to other -omics data generated from other microalgae; especially those of ecological and biotechnological interest.
Machine Learning (ML), and in particular Deep Learning (DL); two subfields of artificial intelligence, are able to handle unstructured big data sets and find the correlation among them. They have already been successful in several biology areas and could also be helpful to correlate the available microalgal -omics datasets for MNPs discovery. Combination of these omics resources and physiological data will identify the bioactive species; as well as the culturing and environmental conditions in which higher amounts of the metabolite of interest are produced. Hence, multi-disciplinarity will finally speed up drug discovery from marine species.