bioRxiv preprint doi: https://doi.org/10.1101/246033; this version posted January 10, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 Data-mining of Antibiotic Resistance Genes Provides Insight into the Community 2 Structure of Ocean Microbiome 3 Shiguang Hao1,$, Pengshuo Yang1,$, Maozhen Han1,$, Junjie Xu1, Shaojun Yu1, Chaoyun 4 Chen1, Wei-Hua Chen1, Houjin Zhang1,*, Kang Ning1,* 5 1Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life 6 Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, 7 430074, China 8 9 $ These authors contributed equally to this work. 10 * Corresponding author. E-mail:
[email protected],
[email protected]. 11 1 bioRxiv preprint doi: https://doi.org/10.1101/246033; this version posted January 10, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 12 Abstract 13 Background:Antibiotics have been spread widely in environments, asserting profound 14 effects on environmental microbes as well as antibiotic resistance genes (ARGs) within these 15 microbes. Therefore, investigating the associations between ARGs and bacterial communities 16 become an important issue for environment protection. Ocean microbiomes are potentially 17 large ARG reservoirs, but the marine ARG distribution and its associations with bacterial 18 communities remain unclear.