AlgaeOrtho, a bioinformatics tool for processing ortholog inference results in algae.

Area of Science:

• Biotechnology
• Bioinformatics
• Algal Research

Background:

• Microalgae are key feedstocks for biofuels and biochemicals due to rapid growth and high yields.
• Industrial applications require bioengineered algal strains for optimized production.
• Identifying specific gene targets (orthologs) is crucial but challenging for researchers without bioinformatics expertise.

Purpose of the Study:

• To introduce AlgaeOrtho, a user-friendly tool for identifying protein orthologs in diverse algal species.
• To simplify the process of selecting bioengineering targets for microalgae.
• To assist researchers in overcoming bioinformatics challenges in algal strain improvement.

Main Methods:

• AlgaeOrtho integrates the SonicParanoid orthology inference tool with the PhycoCosm database.
• The tool analyzes amino acid sequences to identify potential protein orthologs.
• It processes data from multiple diverse algal species.

Main Results:

• AlgaeOrtho provides a table of putative orthologs, a sequence similarity heatmap, and a phylogenetic tree.
• The tool successfully identified orthologs for bioengineering targets in case studies.
• It demonstrated ease of use and utility for identifying targets, even in unannotated species.

Conclusions:

• AlgaeOrtho simplifies the identification of bioengineering targets in microalgae.
• The tool's visualization capabilities across species are unique.
• It empowers researchers to develop superior algal strains for the bioenergy and bioproduct sectors.