Cytobacillus solani FJAT-18043 is a mesophilic prokaryote that was isolated from rhizosphere soil from a potato field.
mesophilic genome sequence 16S sequence| @ref 20215 |
|
|
| Domain Bacillati |
| Phylum Bacillota |
| Class Bacilli |
| Order Caryophanales |
| Family Cytobacillaceae |
| Genus Cytobacillus |
| Species Cytobacillus solani |
| Full scientific name Cytobacillus solani (Liu et al. 2015) Patel and Gupta 2020 |
| Synonyms (3) |
| @ref | Name | Growth | Medium link | Composition | |
|---|---|---|---|---|---|
| 67645 | NUTRIENT AGAR (DSMZ Medium 1) | Medium recipe at MediaDive | Name: NUTRIENT AGAR (DSMZ Medium 1) Composition: Agar 15.0 g/l Peptone 5.0 g/l Meat extract 3.0 g/l Distilled water |
| @ref | Growth | Type | Temperature (°C) | Range | |
|---|---|---|---|---|---|
| 67645 | positive | growth | 30 | mesophilic |
| @ref | Sample type | Geographic location | Country | Country ISO 3 Code | Continent | Latitude | Longitude | |
|---|---|---|---|---|---|---|---|---|
| 67645 | rhizosphere soil from a potato field | Xinjiang province, Yili district, Nilka county (43° 46' 28'' N 82° 35' 1'' E) | China | CHN | Asia | 43.7744 | 82.5836 43.7744/82.5836 |
Global distribution of 16S sequence KP268082 (>99% sequence identity) for Bacillaceae from Microbeatlas ![]()
| @ref | Biosafety level | Biosafety level comment | |
|---|---|---|---|
| 67645 | 1 | Risk group (German classification) |
| @ref | Description | Assembly level | INSDC accession | IMG accession | NCBI tax ID | Score | |
|---|---|---|---|---|---|---|---|
| 66792 | ASM142059v1 assembly for Cytobacillus solani FJAT-18043 | scaffold | 1637975 | 74.57 |
| @ref | Description | Accession | Length | Database | NCBI tax ID | |
|---|---|---|---|---|---|---|
| 67645 | Cytobacillus solani strain FJAT-18043 16S ribosomal RNA gene, partial sequence | KP268082 | 1410 | 1637975 |
| @ref | GC-content (mol%) | Method | |
|---|---|---|---|
| 67645 | 48.8 | high performance liquid chromatography (HPLC) |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125439 | spore_formation | BacteriaNetⓘ | yes | 93.00 | no |
| 125439 | motility | BacteriaNetⓘ | yes | 87.30 | no |
| 125439 | gram_stain | BacteriaNetⓘ | variable | 80.70 | no |
| 125439 | oxygen_tolerance | BacteriaNetⓘ | facultative anaerobe | 91.10 | no |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125438 | gram-positive | gram-positiveⓘ | yes | 68.69 | no |
| 125438 | anaerobic | anaerobicⓘ | no | 95.32 | no |
| 125438 | aerobic | aerobicⓘ | yes | 68.54 | no |
| 125438 | spore-forming | spore-formingⓘ | yes | 92.54 | no |
| 125438 | thermophilic | thermophileⓘ | no | 93.68 | yes |
| 125438 | flagellated | motile2+ⓘ | yes | 79.55 | no |
| Topic | Title | Authors | Journal | DOI | Year | |
|---|---|---|---|---|---|---|
| Phylogeny | Bacillus dafuensis sp. Nov., Isolated from a Forest Soil in China. | Zheng X, Liu G, Wang Z, Wang J, Zhang H, Liu B | Curr Microbiol | 10.1007/s00284-020-02014-2 | 2020 | |
| Phylogeny | Bacillus solani sp. nov., isolated from rhizosphere soil of a potato field. | Liu B, Liu GH, Sengonca C, Schumann P, Ge CB, Wang JP, Cui WD, Lin NQ | Int J Syst Evol Microbiol | 10.1099/ijsem.0.000539 | 2015 | |
| Phylogeny | Bacillus telluris sp. nov. Isolated from Greenhouse Soil in Beijing, China. | Guo HB, He SW, Wang X, Thin KK, Wei HL, Zhang XX | Microorganisms | 10.3390/microorganisms8050702 | 2020 | |
| Phylogeny | Bacillus ciccensis sp. nov., isolated from maize (Zea mays L.) seeds. | Liu Y, Li N, Eom MK, Schumann P, Zhang X, Cao Y, Ge Y, Xiao M, Zhao J, Cheng C, Kim SG | Int J Syst Evol Microbiol | 10.1099/ijsem.0.002341 | 2017 |
| #20215 | Parte, A.C., Sardà Carbasse, J., Meier-Kolthoff, J.P., Reimer, L.C. and Göker, M.: List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ. IJSEM ( DOI 10.1099/ijsem.0.004332 ) |
| #66792 | Julia Koblitz, Joaquim Sardà, Lorenz Christian Reimer, Boyke Bunk, Jörg Overmann: Automatically annotated for the DiASPora project (Digital Approaches for the Synthesis of Poorly Accessible Biodiversity Information) . |
| #67645 | Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 29501 |
| #69479 | João F Matias Rodrigues, Janko Tackmann,Gregor Rot, Thomas SB Schmidt, Lukas Malfertheiner, Mihai Danaila,Marija Dmitrijeva, Daniela Gaio, Nicolas Näpflin and Christian von Mering. University of Zurich.: MicrobeAtlas 1.0 beta . |
| #125438 | Julia Koblitz, Lorenz Christian Reimer, Rüdiger Pukall, Jörg Overmann: Predicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets. 2024 ( DOI 10.1101/2024.08.12.607695 ) |
| #125439 | Philipp Münch, René Mreches, Martin Binder, Hüseyin Anil Gündüz, Xiao-Yin To, Alice McHardy: deepG: Deep Learning for Genome Sequence Data. R package version 0.3.1 . |
| #126262 | A. Lissin, I. Schober, J. F. Witte, H. Lüken, A. Podstawka, J. Koblitz, B. Bunk, P. Dawyndt, P. Vandamme, P. de Vos, J. Overmann, L. C. Reimer: StrainInfo—the central database for linked microbial strain identifiers. ( DOI 10.1093/database/baaf059 ) |
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https://doi.org/10.13145/bacdive160732.20251217.10
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BacDive in 2025: the core database for prokaryotic strain data