"Streptomyces sudanensis" SD 504 is a mesophilic, Gram-positive bacterium that was isolated from from patients with mycetoma infections.
Gram-positive mesophilic genome sequence 16S sequence Bacteria|
|
| Domain Bacteria |
| Phylum Actinomycetota |
| Class Actinomycetes |
| Order Kitasatosporales |
| Family Streptomycetaceae |
| Genus Streptomyces |
| Species "Streptomyces sudanensis" |
| Full scientific name Streptomyces sudanensis Quintana et al. 2008 |
| @ref | Gram stain | Confidence | |
|---|---|---|---|
| 125438 | positive | 91.747 |
| @ref: | 10661 |
| manual_annotation: | 1 |
| multimedia content: | DSM_41923.jpg |
| multimedia.multimedia content: | https://www.dsmz.de/microorganisms/photos/DSM_41923.jpg |
| caption: | Medium 65 28°C |
| intellectual property rights: | © Leibniz-Institut DSMZ |
| @ref | Name | Growth | Medium link | Composition | |
|---|---|---|---|---|---|
| 10661 | GYM STREPTOMYCES MEDIUM (DSMZ Medium 65) | Medium recipe at MediaDive | Name: GYM STREPTOMYCES MEDIUM (DSMZ Medium 65) Composition: Agar 18.0 g/l Malt extract 10.0 g/l Yeast extract 4.0 g/l Glucose 4.0 g/l CaCO3 2.0 g/l Distilled water | ||
| 10661 | ROLLED OATS MINERAL MEDIUM (DSMZ Medium 84) | Medium recipe at MediaDive | Name: ROLLED OATS MINERAL MEDIUM (DSMZ Medium 84) Composition: Agar 20.0 g/l Rolled oats 20.0 g/l ZnSO4 x 7 H2O 0.001 g/l MnCl2 x 4 H2O 0.001 g/l FeSO4 x 7 H2O 0.001 g/l Distilled water | ||
| 10661 | STARCH - MINERAL SALT - AGAR (STMS) (DSMZ Medium 252) | Medium recipe at MediaDive | Name: STARCH - MINERAL SALT - AGAR (STMS) (DSMZ Medium 252) Composition: Agar 14.985 g/l Starch 9.99001 g/l (NH4)2SO4 1.998 g/l CaCO3 1.998 g/l K2HPO4 0.999001 g/l MgSO4 x 7 H2O 0.999001 g/l NaCl 0.999001 g/l FeSO4 x 7 H2O 0.000999001 g/l MnCl2 x 4 H2O 0.000999001 g/l ZnSO4 x 7 H2O 0.000999001 g/l Distilled water |
| @ref | Growth | Type | Temperature (°C) | Range | |
|---|---|---|---|---|---|
| 10661 | positive | growth | 28 | mesophilic |
| @ref | Sample type | Country | Country ISO 3 Code | Continent | |
|---|---|---|---|---|---|
| 10661 | from patients with mycetoma infections | Sudan | SDN | Africa |
| 10661 | GC-content (mol%)70.1 |
| @ref | Description | Accession | Length | Database | NCBI tax ID | |
|---|---|---|---|---|---|---|
| 10661 | Streptomyces sudanensis strain SD504 16S ribosomal RNA gene, partial sequence | EF515876 | 1521 | 1169864 |
| @ref | Description | Accession | Assembly level | Database | NCBI tax ID | |
|---|---|---|---|---|---|---|
| 66792 | Streptomyces sudanensis SD 504 | GCA_023614315 | chromosome | 436397 |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125438 | gram-positive | gram-positiveⓘ | yes | 91.75 | no |
| 125438 | anaerobic | anaerobicⓘ | no | 95.65 | no |
| 125438 | aerobic | aerobicⓘ | yes | 89.80 | no |
| 125438 | spore-forming | spore-formingⓘ | yes | 84.54 | no |
| 125438 | thermophilic | thermophileⓘ | no | 93.63 | yes |
| 125438 | flagellated | motile2+ⓘ | no | 89.50 | no |
| Topic | Title | Authors | Journal | DOI | Year | |
|---|---|---|---|---|---|---|
| Phylogeny | Streptomyces sudanensis sp. nov., a new pathogen isolated from patients with actinomycetoma. | Quintana ET, Wierzbicka K, Mackiewicz P, Osman A, Fahal AH, Hamid ME, Zakrzewska-Czerwinska J, Maldonado LA, Goodfellow M | Antonie Van Leeuwenhoek | 10.1007/s10482-007-9205-z | 2007 |
| Culture collection no. | |
|---|---|
| DSM 41923 | |
| SD 504 | |
| NRRL B-24575 |
| #10661 | Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 41923 |
| #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) . |
| #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 ) |
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