Embedding DNA-based natural language in microbes for the benefit of future researchers

Abstract

Microorganisms are valuable resources as antibiotic producers, biocontrol agents, and symbiotic agents in various ecosystems and organisms. Over the past decades, there has been a notable increase in the identification and generation of both wild-type and genetically modified microbial strains from research laboratories worldwide. However, a substantial portion of the information represented in these strains remains scattered across the scientific literature. To facilitate the work of future researchers, in this perspective article, we advocate the adoption of the DNA-based natural language (DBNL) algorithm standard and then demonstrate it using a Streptomyces species as a proof of concept. This standard enables the sophisticated genome sequencing and subsequent extraction of valuable information encoded within a particular microbial species. In addition, it allows the access of such information for the continued research and applications even if a currently cultivated microbe cannot be cultured in the future. Embracing the DBNL algorithm standard promises to enhance the efficiency and effectiveness of microbial research, paving the way for innovative solutions and discoveries in diverse fields.