Molecular Pathogens 2024, Vol.15, No.2, 72-82 http://microbescipublisher.com/index.php/mp 81 Declercq A., Tilleman L., Gansemans Y., Witte C., Haesebrouck F., Nieuwerburgh F., Smet A., and Decostere A., 2021, Comparative genomics of Flavobacterium columnare unveils novel insights in virulence and antimicrobial resistance mechanisms, Veterinary Research, 52: 1-13. https://doi.org/10.1186/s13567-021-00899-w Dittami S., and Corre E., 2017, Detection of bacterial contaminants and hybrid sequences in the genome of the kelp Saccharina japonica using Taxoblast, Peer J., 5: e4073. https://doi.org/10.7717/peerj.4073 Florez J., Camus C., Hengst M., and Buschmann A., 2021, A mesocosm study on bacteria‐kelp interactions: Importance of nitrogen availability and kelp genetics, Journal of Phycology, 57(6): 1777-1791. https://doi.org/10.1111/jpy.13213 Frickmann H., Zautner A., Moter A., Kikhney J., Hagen R., Stender H., and Poppert S., 2017, Fluorescence in situ hybridization (FISH) in the microbiological diagnostic routine laboratory: a review, Critical Reviews in Microbiology, 43(3): 263-293. https://doi.org/10.3109/1040841X.2016.1169990 Griot R., Allal F., Phocas F., Brard-Fudulea S., Morvezen R., Bestin A., Haffray P., François Y., Morin T., Poncet C., Vergnet A., Cariou S., Brunier J., Bruant J., Peyrou B., Gagnaire P., and Vandeputte M., 2021, Genome-wide association studies for resistance to viral nervous necrosis in three populations of European sea bass (Dicentrarchus labrax) using a novel 57k SNP array DlabChip, Aquaculture, 530: 735930. https://doi.org/10.1016/j.aquaculture.2020.735930 Jin L., Chen Y., Yang W., Qiao Z., and Zhang X., 2020, Complete genome sequence of fish-pathogenic Aeromonas hydrophila HX-3 and a comparative analysis: insights into virulence factors and quorum sensing, Scientific Reports, 10(1): 15479. https://doi.org/10.1038/s41598-020-72484-8 Jo J., Oh J., and Park C., 2020, Microbial community analysis using high-throughput sequencing technology: a beginner’s guide for microbiologists, Journal of Microbiology, 58: 176-192. https://doi.org/10.1007/s12275-020-9525-5 Lemay M., Martone P., Keeling P., Burt J., Krumhansl K., Sanders R., and Parfrey L., 2018, Sympatric kelp species share a large portion of their surface bacterial communities, Environmental Microbiology, 20: 658-670. https://doi.org/10.1111/1462-2920.13993 Liu B., Zheng D., Jin Q., Chen L., and Yang J., 2018, VFDB 2019: a comparative pathogenomic platform with an interactive web interface, Nucleic Acids Research, 47: D687 - D692. https://doi.org/10.1093/nar/gky1080 Lu J., and Salzberg S., 2018, Removing contaminants from databases of draft genomes, PLoS Computational Biology, 14(6): e1006277. https://doi.org/10.1371/journal.pcbi.1006277 Lumpe J., Gumbleton L., Gorzalski A., Libuit K., Varghese V., Lloyd T., Tadros F., Arsimendi T., Wagner E., Stephens C., Sevinsky J., Hess D., and Pandori M., 2022, GAMBIT (Genomic Approximation Method for Bacterial Identification and Tracking): A methodology to rapidly leverage whole genome sequencing of bacterial isolates for clinical identification, PLOS ONE, 18(2): e0277575. https://doi.org/10.1371/journal.pone.0277575 Murúa P., Müller D., Etemadi M., West P., and Gachon C., 2020, Host and pathogen autophagy are central to the inducible local defences and systemic response of the giant kelp Macrocystis pyrifera against the oomycete pathogen Anisolpidium ectocarpii, The New Phytologist, 226(5): 1445 - 1460. https://doi.org/10.1111/nph.16438 Nies L., Lopes S., Busi S., Galata V., Heintz‐Buschart A., Laczny C., May P., and Wilmes P., 2021, PathoFact: a pipeline for the prediction of virulence factors and antimicrobial resistance genes in metagenomic data, Microbiome, 9: 1-14. https://doi.org/10.1186/s40168-020-00993-9 Nogueira T., and Botelho A., 2021, Metagenomics and other omics approaches to bacterial communities and antimicrobial resistance assessment in aquacultures, Antibiotics, 10(7): 787. https://doi.org/10.3390/antibiotics10070787 Palaiokostas C., 2021, Predicting for disease resistance in aquaculture species using machine learning models, Aquaculture Reports, 20: 100660. https://doi.org/10.1016/J.AQREP.2021.100660 Popović N., Kepec S., Kazazić S., Strunjak-Perović I., Bojanić K., and Čož-Rakovac R., 2022, Identification of environmental aquatic bacteria by mass spectrometry supported by biochemical differentiation, PLoS ONE 17(6): e0269423. https://doi.org/10.1371/journal.pone.0269423 Ramírez-Puebla S., Weigel B., Jack L., Schlundt C., Pfister C., and Welch J., 2020, Spatial organization of the kelp microbiome at micron scales, Microbiome, 10(1): 52. https://doi.org/10.1101/2020.03.01.972083 Rana S., Valentin K., Bartsch I., and Glöckner G., 2019, Loss of a chloroplast encoded function could influence species range in kelp, Ecology and Evolution, 9: 8759 - 8770. https://doi.org/10.1002/ece3.5428
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