Zhu, Y. G. et al. Continental-scale pollution of estuaries with antibiotic resistance genes. Nat. Microbiol. 2, 16270, https://doi.org/10.1038/nmicrobiol.2016.270 (2017).
Google Scholar
Hutchins, D. A. & Fu, F. Microorganisms and ocean global change. Nat. Microbiol. 2, 17058, https://doi.org/10.1038/nmicrobiol.2017.58 (2017).
Google Scholar
Wang, J. & Jia, H. Metagenome-wide association studies: fine-mining the microbiome. Nat. Rev. Microbiol. 14, 508–522, https://doi.org/10.1038/nrmicro.2016.83 (2016).
Google Scholar
Kan, J., Suzuki, M. T., Wang, K., Evans, S. E. & Chen, F. High temporal but low spatial heterogeneity of bacterioplankton in the Chesapeake bay. Appl. Environ. Microbiol. 73, 6776–6789, https://doi.org/10.1128/Aem.00541-07 (2007).
Google Scholar
Bouvier, T. C. & del Giorgio, P. A. Compositional changes in free-living bacterial communities along a salinity gradient in two temperate estuaries. Limnol. Oceanogr. 47, 453–470, https://doi.org/10.4319/lo.2002.47.2.0453 (2002).
Google Scholar
Campbell, B. J. & Kirchman, D. L. Bacterial diversity, community structure and potential growth rates along an estuarine salinity gradient. ISME J. 7, 210–220, https://doi.org/10.1038/ismej.2012.93 (2013).
Google Scholar
Fortunato, C. S., Herfort, L., Zuber, P., Baptista, A. M. & Crump, B. C. Spatial variability overwhelms seasonal patterns in bacterioplankton communities across a river to ocean gradient. ISME J. 6, 554–563, https://doi.org/10.1038/ismej.2011.135 (2012).
Google Scholar
Ghosh, A. & Bhadury, P. Exploring biogeographic patterns of bacterioplankton communities across global estuaries. MicrobiologyOpen 8, https://doi.org/10.1002/mbo3.741 (2019).
Zhang, C. J., Chen, Y. L., Pan, J., Wang, Y. M. & Li, M. Spatial and seasonal variation of methanogenic community in a river-bay system in South China. Appl. Microbiol. Biotechnol. 104, 4593–4603, https://doi.org/10.1007/s00253-020-10613-z (2020).
Google Scholar
Yu, T. et al. Characteristics of Microbial Communities and Their Correlation With Environmental Substrates and Sediment Type in the Gas-Bearing Formation of Hangzhou Bay, China. Front. Microbiol. 10, https://doi.org/10.3389/fmicb.2019.02421 (2019).
Zhou, L. et al. Stochastic determination of the spatial variation of potentially pathogenic bacteria communities in a large subtropical river. Environ. Pollut. 264, 114683, https://doi.org/10.1016/j.envpol.2020.114683 (2020).
Google Scholar
Zhou, L. et al. Environmental filtering dominates bacterioplankton community assembly in a highly urbanized estuarine ecosystem. Environ. Res. 196, 110934, https://doi.org/10.1016/j.envres.2021.110934 (2021).
Google Scholar
Li, D., Liu, C. M., Luo, R., Sadakane, K. & Lam, T. W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676, https://doi.org/10.1093/bioinformatics/btv033 (2015).
Google Scholar
Mikheenko, A., Saveliev, V. & Gurevich, A. MetaQUAST: evaluation of metagenome assemblies. Bioinformatics 32, 1088–1090, https://doi.org/10.1093/bioinformatics/btv697 (2016).
Google Scholar
Uritskiy, G. V., DiRuggiero, J. & Taylor, J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6, https://doi.org/10.1186/s40168-018-0541-1 (2018).
Prjibelski, A., Antipov, D., Meleshko, D., Lapidus, A. & Korobeynikov, A. Using SPAdes de novo assembler. Curr. Protoc. Bioinf. 70, e102, https://doi.org/10.1002/cpbi.102 (2020).
Google Scholar
Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication[J]. ISME J. 11, 2864–2868, https://doi.org/10.1038/ismej.2017.126 (2017).
Google Scholar
Uritskiy, G. et al. Halophilic microbial community compositional shift after a rare rainfall in the Atacama Desert. ISME J. 13, 2737–2749, https://doi.org/10.1038/s41396-019-0468-y (2019).
Google Scholar
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419, https://doi.org/10.1038/nmeth.4197 (2017).
Google Scholar
Chaumeil, P. A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927, https://doi.org/10.1093/bioinformatics/btz848 (2020).
Google Scholar
Parks, D. H. et al. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat. Biotechnol. 38, 1079–1086, https://doi.org/10.1038/s41587-020-0501-8 (2020).
Google Scholar
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2–approximately maximum-likelihood trees for large alignments. PloS one 5, e9490, https://doi.org/10.1371/journal.pone.0009490 (2010).
Google Scholar
Nguyen, L. T., Schmidt, H. A., Von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274, https://doi.org/10.1093/molbev/msu300 (2015).
Google Scholar
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293–W296, https://doi.org/10.1093/nar/gkab301 (2021).
Google Scholar
NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRP320016 (2021).
Zhou, L., Huang, S., Gong, J., Xu, P. & Huang, X. 500 metagenome-assembled microbial genomes from 30 subtropical estuaries in South China. Figshare https://doi.org/10.6084/m9.figshare.14717061.v4 (2021).
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055, https://doi.org/10.1101/gr.186072.114 (2015).
Google Scholar
Source: Ecology - nature.com
