Supporting data for "Global ocean resistome revealed: exploring Antibiotic Resistance Genes (ARGs) abundance and distribution in TARA oceans samples "

The rise of antibiotic resistance (AR) in clinical settings is one of the biggest modern global public health concerns. Therefore, the understanding of AR mechanisms, evolution, and global distribution is a priority due to its impact on the treatment course and patient survival. Besides all efforts in the elucidation of AR mechanisms in clinical strains, little is known about its prevalence and evolution in environmental microorganisms. In this study, 293 metagenomic samples from the TARA Oceans project were used to detect and quantify environmental antibiotic resistance genes (ARGs) using machine learning tools. After manual curation of ARGs, their abundance and distribution in the global ocean are presented, including taxonomical and phylogenetic classification. <br>Additionally, the potential of horizontal ARG transfer by plasmids and their correlation with environmental and geographical parameters is shown. A total of 99,205 environmental open reading frames (ORFs) were classified as one of 560 different ARGs conferring resistance to 26 antibiotic classes. We found 24,567 ORFs in contigs classified as plasmid sequences, suggesting the importance of mobile genetic elements (MGEs) in the dynamics of environmental ARG transmission. Moreover, 4,804 contigs with more than two putative ARGs were found, including two plasmid-like contigs with five different ARGs, highlighting the potential presence of multi-resistant microorganisms in the natural ocean environment. Finally, we identified ARGs conferring resistance to some of the most relevant clinical antibiotics, revealing the presence of 15 ARGs similar to Mobilized Colistin Resistance genes (mcr) with high abundance on Polar Biomes. Of these, five are assigned to the genus Psychrobacter, a genus including opportunistic pathogens that can cause fatal infections in humans. Our results are available on Zenodo in MySQL database dump format, and all the code used for the analyses, including a Jupyter notebook, can be accessed on GitHub. We also developed a dashboard web application (available at ResistomeDB) for data visualization.