Weisella soli 3-88: Core-phylogenetic tree, eps cluster, and dDDH values

Fig. 1A. Maximum-likelihood core-gene phylogenetic tree of the W. soli and Weissella type strains. Core genes were identified using Roary v3.13.0 (1). The phylogenetic tree was built with FastTree v2.1.11 (2) and visualized using tvBOT v2.6 (3). All reference genomes were obtained from GenBank.Fig. 1B. Comparison of the eps cluster encoded by W. soli 3-88 and L. lactis KLDS 4.0325. The Wzx and Wzy flippase and polymerase, respectively, were inferred by comparing their predicted transmembrane domain structures to those of the corresponding proteins in Lactococcus lactis KLDS 4.0325, with which they showed structural similarity. Prediction of transmembrane domains was carried out using Deep TMHMM v1.0 (4). Visualization was performed using the Clinker tool (5).Fig. 2. Digital DNA-DNA hybridization (dDDH) values between strain 3-88 and other Weissella soli strains as well as other related type species of the genus Weissella. Digital DNA–DNA hybridization (dDDH) values were estimated using the Genome-to-Genome Distance Calculator (GGDC) version 3.0, accessed through the Type (Strain) Genome Server (TYGS) platform (6,7).1. Page, A. J., Cummins, C. A., Hunt, M., Wong, V. K., Reuter, S., Holden, M. T., Fookes, M., Falush, D., Keane, J. A., & Parkhill, J. (2015). Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics (Oxford, England), 31(22), 3691–3693. https://doi.org/10.1093/bioinformatics/btv4212. Price, M. N., Dehal, P. S., & Arkin, A. P. (2009). FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Molecular biology and evolution, 26(7), 1641–1650. https://doi.org/10.1093/molbev/msp0773. Xie, J., Chen, Y., Cai, G., Cai, R., Hu, Z., & Wang, H. (2023). Tree Visualization By One Table (tvBOT): a web application for visualizing, modifying and annotating phylogenetic trees. Nucleic acids research, 51(W1), W587–W592. https://doi.org/10.1093/nar/gkad3594. Jeppe Hallgren, Konstantinos D. Tsirigos, Mads D. Pedersen, José Juan Almagro Armenteros, Paolo Marcatili, Henrik Nielsen, Anders Krogh and Ole Winther (2022). DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks.https://doi.org/10.1101/2022.04.08.4876095. Gilchrist, C. L. M., & Chooi, Y. (2021). clinker & clustermap.js: automatic generation of gene cluster comparison figures. Bioinformatics, 37(16), 2473–2475. https://doi.org/10.1093/bioinformatics/btab0076. Meier-Kolthoff, J. P., Auch, A. F., Klenk, H. P., & Göker, M. (2013). Genome sequence-based species delimitation with confidence intervals and improved distance functions. BMC bioinformatics, 14, 60. https://doi.org/10.1186/1471-2105-14-607. Meier-Kolthoff, J. P., Carbasse, J. S., Peinado-Olarte, R. L., & Göker, M. (2022). TYGS and LPSN: a database tandem for fast and reliable genome-based classification and nomenclature of prokaryotes. Nucleic acids research, 50(D1), D801–D807. https://doi.org/10.1093/nar/gkab902