Corynebacterium pseudotuberculosis CCUG 27541 is an aerobe, mesophilic prokaryote that was isolated from Horse.
aerobe mesophilic genome sequence| @ref 20215 |
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| Domain Bacillati |
| Phylum Actinomycetota |
| Class Actinomycetes |
| Order Mycobacteriales |
| Family Corynebacteriaceae |
| Genus Corynebacterium |
| Species Corynebacterium pseudotuberculosis |
| Full scientific name Corynebacterium pseudotuberculosis (Buchanan 1911) Eberson 1918 (Approved Lists 1980) |
| Synonyms (1) |
| @ref | Growth | Type | Temperature (°C) | Range | |
|---|---|---|---|---|---|
| 48825 | positive | growth | 30-37 | mesophilic |
| Metadata FA analysis | |||||||||||||||||||||||||
| type of FA analysis | whole cell analysis | ||||||||||||||||||||||||
| method/protocol | CCUG | ||||||||||||||||||||||||
| @ref | 48825 | ||||||||||||||||||||||||
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| 48825 | Sample typeHorse |
| @ref | Description | Assembly level | INSDC accession | BV-BRC accession | IMG accession | NCBI tax ID | Score | |
|---|---|---|---|---|---|---|---|---|
| 66792 | ASM72972v1 assembly for Corynebacterium pseudotuberculosis CCUG 27541 | contig | 1719 | 78.8 |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125439 | spore_formation | BacteriaNetⓘ | no | 86.00 | no |
| 125439 | motility | BacteriaNetⓘ | no | 73.80 | no |
| 125439 | gram_stain | BacteriaNetⓘ | negative | 65.50 | no |
| 125439 | oxygen_tolerance | BacteriaNetⓘ | obligate aerobe | 92.50 | no |
| @ref | Trait | Model | Prediction | Confidence in % | In training data |
|---|---|---|---|---|---|
| 125438 | gram-positive | gram-positiveⓘ | yes | 93.75 | no |
| 125438 | anaerobic | anaerobicⓘ | no | 91.00 | yes |
| 125438 | spore-forming | spore-formingⓘ | no | 82.10 | no |
| 125438 | aerobic | aerobicⓘ | no | 62.08 | no |
| 125438 | thermophilic | thermophileⓘ | no | 94.00 | no |
| 125438 | flagellated | motile2+ⓘ | no | 92.50 | no |
| Title | Authors | Journal | DOI | Year | |
|---|---|---|---|---|---|
| Corynebacterium pseudotuberculosis Pneumonia in a Veterinary Student Infected During Laboratory Work. | Heggelund L, Gaustad P, Havelsrud OE, Blom J, Borgen L, Sundset A, Sorum H, Froland SS. | Open Forum Infect Dis | 10.1093/ofid/ofv053 | 2015 | |
| Phospholipase D activity of Corynebacterium pseudotuberculosis (Corynebacterium ovis) and Corynebacterium ulcerans, a distinctive marker within the genus Corynebacterium. | Barksdale L, Linder R, Sulea IT, Pollice M. | J Clin Microbiol | 10.1128/jcm.13.2.335-343.1981 | 1981 | |
| Genome Sequences of Corynebacterium pseudotuberculosis Strains 48252 (Human, Pneumonia), CS_10 (Lab Strain), Ft_2193/67 (Goat, Pus), and CCUG 27541. | Havelsrud OE, Sorum H, Gaustad P | Genome Announc | 10.1128/genomeA.00869-14 | 2014 |
| #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 ) |
| #48825 | Culture Collection University of Gothenburg (CCUG) ; Curators of the CCUG; CCUG 27541 |
| #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 ) |
| #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/bacdive145060.20251217.10
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BacDive in 2025: the core database for prokaryotic strain data